JURIKOM (Jurnal Riset Komputer) discusses knowledge in the field of Informatics, Information Systems, Informatics Management, DSS, AI, ES, and Networks, as a forum for expressing research results both conceptually and technically related to Information Technology and Computers.
Penerapan BERT dalam Memetakan Opini Pengguna Instagram Terhadap Program Makan Bergizi Gratis
The Free Lunch Program is a new government initiative designed to improve students’ nutritional intake and enhance their focus during school activities. Its implementation has gained significant public attention, generating a variety of reactions across social media platforms, particularly Instagram. This study aims to examine public sentiment toward the program’s rollout in South Sumatra by collecting user comments from posts using the hashtag #mbgsumsel. The collected comments were processed through several stages, including data cleaning, text pre-processing, dataset partitioning, fine-tuning and model evaluation. The BERT model was employed due to its strong capability in capturing contextual meaning within text, making it more effective than conventional classification approaches. Experimental results indicate that the model achieved an akurasi of 88% in classifying sentiments. The test dataset consisted of 751 positive comments, 233 neutral comments, and 257 negative comments. Overall, this study provides a quantitative overview of how the public perceives the Free Lunch Program based on their social media expressions.
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Optimasi Hyperparameter Optuna Pada Model mT5 Untuk Penerjemahan Angkola-Indonesia
This research aims to address the challenges of preserving the Angkola language in the digital era, which are exacerbated by the lack of an adequate digital data corpus, by developing an accurate and efficient automatic Angkola-to-Indonesian machine translation system. The proposed method focuses on a fine-tuning approach for the Multilingual Text-to-Text Transfer Transformer (mT5-base) model using an Angkola-Indonesian text data corpus.The initial dataset, consisting of Angkola-Indonesian sentence pairs, was cleaned, resulting in 28,775 sentence pairs used for training. The data was subsequently split into 70% training data (20,142 lines), 15% validation data (4,316 lines), and 15% test data (4,317 lines). Intelligent model performance optimization was conducted using Optuna Hyperparameter Tuning to find the best hyperparameter combination. Optuna's objective function was designed to maximize a composite score based on the BLEU and chrF metrics from the validation evaluation results. The optimization process yielded the best Trial (Trial 50) with key hyperparameters: learning rate = 0.0004316 and num beams = 4. The best model obtained from the fine-tuning process was then evaluated on a separate Test dataset. The final evaluation on the test data using standard translation metrics demonstrated excellent performance, achieving a BLEU score of 73.84 and a chrF score of 83.34. Overall, this research successfully implemented hyperparameter optimization using Optuna for the mT5 model, resulting in an Angkola-to-Indonesian translation model that exhibits high accuracy and more efficient performance. These results provide a tangible contribution to the preservation of the Angkola language by offering a modern and accurate translation tool.
Edge AI Berbasis Computer Vision Untuk Meningkatkan Efektivitas Sistem Deteksi Pemilahan Sampah Real-Time Integrasi YOLOv8, Raspberry Pi 5 dan SEE
Waste management in Indonesia is still characterized by a high volume of improperly managed waste and low source-level segregation, causing recyclable materials to mix with other waste streams and reducing their recovery value. This situation calls for a sorting system that is effective, fast, and affordable, while also providing real-time operational information to support on site decision-making. This study presents an integrated computer vision approach using YOLOv8 deployed on a Raspberry Pi 5 with a Camera Module 3, connected to a real-time information system via Server-Sent Events (SSE) for monitoring and analytics. The methodology includes constructing a labeled dataset in YOLO TXT format, training a YOLOv8n model, deploying edge inference, and developing a backend API to receive detection outputs and stream them to a dashboard in real time. The system is evaluated using mean Average Precision (mAP), precision–recall, frames per second (FPS), and end-to-end latency from the camera to the dashboard. The prototype achieves an mAP@0.5 of 98.5% with precision–recall above 97%, an average throughput of 8.3 FPS at 640×640 resolution, and a median SSE communication latency of 0.5–0.6 ms, demonstrating the feasibility of a cost-effective solution for automated waste sorting. The system also provides logging, operational statistics, an offline queue, and an idempotency mechanism to support reliable operation in real-world deployments.
Analisis Sentimen Masyarakat Indonesia terhadap Keterlibatan Bill Gates dalam Program Vaksin TBC di Media Sosial X Menggunakan SVM
The involvement of Bill Gates in the development program of the tuberculosis (TB) vaccine has generated diverse responses among the Indonesian public, particularly as expressed through social media platform X, making it an important issue to examine since public sentiment may influence public trust in health programs and the success of TB prevention efforts. This study aims to analyze public sentiment in Indonesia toward Bill Gates’ involvement in the TB vaccine program based on social media data from platform X using the Support Vector Machine (SVM) algorithm. The research data consist of 784 Indonesian-language tweets collected through web scraping techniques using keywords related to the TB vaccine issue and Bill Gates. The collected data then underwent several text preprocessing stages, including data cleaning, case folding, tokenization, word normalization, stopword removal, and stemming to improve text quality and consistency. Feature representation was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, while the dataset was split into 80% training data and 20% testing data. The classification model was built using an SVM algorithm with a linear kernel to optimally separate positive and negative sentiment classes. The experimental results show that the SVM model combined with TF-IDF achieved an accuracy of 96.8%, with a precision of 98.3%, a recall of 79.2%, and an F1-score of 86.0%. Sentiment distribution analysis indicates that the majority of tweets were dominated by negative sentiment, while positive sentiment appeared in a smaller proportion. The main contribution of this study lies in the application of social media–based sentiment analysis specifically to the TB vaccine issue associated with a global public figure in the Indonesian context, as well as in demonstrating that the combination of SVM and TF-IDF is effective in accurately classifying public opinion. These findings are expected to serve as a data-driven reference for government institutions and public health stakeholders in designing more effective public communication strategies.
Penerapan Logika Fuzzy Tsukamoto Sebagai Sistem Pendukung Keputusan Penentuan Mata Kuliah Pilihan Mahasiswa Ilmu Komputer XYZ
The selection of elective courses poses a challenge for Computer Science students at XYZ University because it influences competency development, while objective decision-making guidance remains limited. This study aims to develop a web-based decision support system to recommend specialization elective courses using the Fuzzy Tsukamoto method. Data were collected through questionnaires from students in semesters five to seven and processed into four input variables: Robotics, Mathematics, Programming, and Analysis. Each variable was modeled into three fuzzy sets (Weak, Moderate, Strong) using trapezoidal membership functions and processed through IF–THEN rule-based inference with a total of 162 rules. Output values were obtained through weighted average defuzzification to generate course recommendations. System testing was conducted by comparing system outputs with manual calculations and evaluated using the Mean Absolute Percentage Error (MAPE). The results showed a MAPE value of approximately ±0.1096%, indicating that the implementation of the Tsukamoto method in the system is consistent with manual calculations. This study contributes to providing a structured and objective decision support system to assist students in determining elective courses based on their competencies.
Optimized Fault Prediction in Power Distribution Transformers Using Grey Wolf Optimizer-Based SVM and MLP Models
Distribution transformers are critical components of power distribution systems, and their reliability directly affects the continuity and quality of electrical energy supply. However, early-stage transformer faults are difficult to detect because their operational characteristics often closely resemble normal operating conditions, which can lead to undetected degradation and unexpected failures. This study aims to improve the accuracy and robustness of fault prediction in distribution transformers by proposing a hybrid approach that integrates the Grey Wolf Optimizer (GWO) with Support Vector Machine (SVM) and Multilayer Perceptron (MLP) models. The main contribution of this research is a direct and systematic performance comparison between baseline machine learning models and their GWO-optimized counterparts, highlighting the effectiveness of metaheuristic optimization in enhancing classification performance. GWO is employed to optimize key model parameters, enabling improved convergence behavior, higher classification accuracy, and better generalization capability. The proposed models are evaluated under four transformer operating conditions, namely Light Load Imbalance, Light Overload, Normal, and Normal High Temperature, which represent practical scenarios in power distribution networks. Model performance is assessed using standard classification metrics, including Accuracy, Precision, Recall, and F1-Score. Experimental results show that the baseline SVM achieved an accuracy of 68%, while the baseline MLP reached 87% accuracy. After GWO-based optimization, the SVM–GWO model demonstrated a significant improvement, achieving 92% accuracy, whereas the MLP–GWO model produced the best overall performance, achieving 93% accuracy, precision, recall, and F1-score. These findings confirm that GWO-based optimization substantially enhances transformer fault prediction performance and demonstrates the strong potential of the proposed hybrid models for real-time monitoring and preventive maintenance of power distribution transformers.
Analisis Performa WireGuard dan OpenVPN pada VPN Perbankan Berbasis MikroTik menggunakan ICMP, iPerf3, dan Mann–Whitney
The banking industry necessitates secure and reliable site-to-site connectivity between data centres and branch offices to support transactions, data replication, and monitoring that is sensitive to latency, jitter, and packet loss. In practice, VPN gateways often operate concurrently with routing and security functions, leading to specific issues characterised by a trade-off between network service quality and processing overhead on gateway devices. Given that OpenVPN remains widely used while WireGuard is increasingly adopted as a more streamlined protocol, there is a need for measurable evidence to determine the most suitable protocol in a banking environment based on MikroTik routers. This study aims to compare the performance of WireGuard and OpenVPN across three dimensions: connection quality via ICMP Ping (minimum/average/maximum round-trip time, jitter, packet loss), transfer capacity using iPerf3 in TCP/UDP scenarios (download and upload), and processing efficiency through monitoring of CPU and memory usage. Data will be summarised using descriptive statistics and tested with the Mann–Whitney U test under equivalent test conditions. The results indicate that WireGuard provides better delay stability (lower average/maximum round-trip time and jitter) and more efficient CPU utilisation compared to OpenVPN, whereas throughput is context-dependent, with WireGuard generally excelling in UDP scenarios and OpenVPN performing better in certain TCP situations. The contribution of this research lies in the replicable testing methodology and quantitative evidence that serves as a foundation for recommending VPN protocol selection for banking based on network performance, resource efficiency, and service continuity.
Analisis Kepatuhan Belanja Dana BOS Melalui Klasterisasi K-Means++: Studi Kasus Pada Satuan Pendidikan Tingkat Menengah Pertama
The Ministry of Education, Culture, Research, and Technology Regulation Number 18 of 2022 mandates all educational units to procure goods and services using School Operational Assistance (BOS) funds through the SIPLah platform. This policy is a strategic government step to enhance transparency, accountability, and efficiency in the management of national education funds. However, field implementation still faces significant challenges characterized by low platform adoption rates, where the majority of schools tend to prioritize conventional manual procurement prone to administrative irregularity risks. This study aims to analyze the compliance level of BOS fund spending through spending pattern clustering to identify SIPLah adoption characteristics and formulate targeted policy recommendations. The method used is the K-Means++ algorithm, selected for its superiority in optimal centroid initialization compared to standard K-Means, resulting in more accurate clusters with fast convergence. Research data sourced from the 2024 School Activity Plan and Budget (RKAS) of 40 State Junior High School (SMPN) samples selected using Slovin's formula with a 10% error rate. The clustering process produced three distinct compliance characteristic groups: Cluster 1 (Low Compliance) dominating with 30 schools (75%) in the SIPLah usage range of 0-5%, Cluster 2 (Medium Compliance) comprising 8 schools (20%) in the range of >5-15%, and Cluster 3 (High Compliance) comprising only 2 schools (5%) in the range of >15%. Validation using the Silhouette Coefficient yielded a score of 0.687, indicating a well-formed cluster structure. These findings provide a significant contribution to the education office as a basis for policy evaluation, where schools in Cluster 1 require infrastructure audits and intensive assistance, while Cluster 3 can serve as a best practice model in school financial governance.
Implementasi Metode Forward Chaining untuk Rekomendasi Jurusan Perguruan Tinggi Berdasarkan Minat dan Bakat MA XYZ
Choosing a college major is a crucial decision for final year students as it impacts their academic success and future career paths. However, the process of selecting a major is often carried out without objectively considering students' interests and talents, which can lead to mismatches in the learning process. This study aims to develop an expert system-based college major recommendation system using the Forward Chaining method to analyze students' interests and talents. Interest and talent data are obtained through questionnaires filled out by students independently through the system, then used as the initial basis for the conclusion-making process. The knowledge base is structured in the form of IF–THEN rules that link interest and talent characteristics with specific majors and their respective weights. The inference process is carried out by matching existing facts with available rules to produce a suitability score for each major. The results of the study show that the system is able to provide logical and structured major recommendations according to students' interest and talent profiles. The results of system testing on student data indicate that the system is able to produce logical and consistent major recommendations. Functional testing using the Black Box Testing method shows a success rate of 100%, indicating that all system functions run according to the specified requirements.
Analisis dan Implementasi Singular Value Decomposition (SVD) untuk Sistem Rekomendasi Anime Berbasis Collaborative Filtering
The rapid growth of digital platforms has significantly increased the number of available anime titles, making it difficult for users to choose content that matches their preferences. In addition, the diversity of genres and content characteristics accessible to different age groups highlights the need for a system that can provide recommendations more accurately and efficiently. This study aims to develop an anime recommendation system based on collaborative filtering by applying the truncated Singular Value Decomposition (SVD) method to address the data sparsity problem in the user–item matrix. The dataset was collected from the AniList platform and consists of 30 users, 100 anime titles, and 1,230 explicit rating records. The evaluation was conducted using a hold-out scheme with an 80% training set (992 ratings) and a 20% testing set (248 ratings). Prediction performance was measured using an RMSE of 1.6464 and an MAE of 1.2370, while the quality of Top-N recommendations was assessed using Precision@10. The experimental results indicate that the best configuration was achieved at k = 4, which produced the lowest prediction error on the test set and generated relevant recommendations. The average Precision@10 obtained was 0.1897, meaning that approximately 18.97% of the Top-10 recommendations provided by the system were considered relevant.
Aplikasi Monitoring Aset Berbasis QR Code Dengan Rule-Based Alert Engine Sebagai Peringatan Dini
Asset management at PT PLN Nusantara Power Unit Pembangkitan Minahasa is still carried out manually, which has the potential to cause recording errors and delays in data updates. In addition, most QR Code-based asset management systems that have been developed generally only focus on the process of recording and identifying assets, without being equipped with an early warning mechanism. This study aims to develop a web-based asset monitoring application that integrates QR Code technology with a rule-based alert engine using the Extreme Programming (XP) method. The research methods include iterative software development, functional testing using black box testing, system response time analysis, and user satisfaction evaluation through questionnaires. Testing results show that all system functions run according to user requirements with an average response time of less than 5 seconds, thus supporting real-time use. The rule-based alert engine is capable of automatically detecting asset conditions that require attention and generating notifications as a form of early warning. User evaluation shows a high level of satisfaction with the ease of use and functionality of the system. Based on these results, the developed application has been proven to improve the accuracy of recording and efficiency of asset management compared to manual methods, and has the potential to become an adaptive digital solution in office asset management.
Analisis Komparasi Algoritma KNN dan Naive Bayes untuk Klasifikasi Pasien Rehabilitasi Narkoba di XYZ
The development of information technology in the field of data mining opens up significant opportunities to optimize data classification in the healthcare and rehabilitation sectors. LRPPN BI Medan currently faces challenges in determining rehabilitation programs because the decision-making process is still subjective and has not yet systematically utilized historical data. Inaccuracies in determining these programs can reduce recovery effectiveness and increase the potential for patient relapse. This study aims to apply and test the effectiveness of the KNN and Naïve Bayes algorithms in classifying rehabilitation programs based on patient criteria, such as duration of drug use, URICA test results, medical history, and addiction level. This study uses a quantitative approach with the Waterfall development method. This solution is proposed to overcome subjectivity through a data-based classification system that complements each other in accuracy and processing speed. The results of this study show that the Naive Bayes algorithm has a higher accuracy rate of 96.43%, while KNN has an accuracy of 94.29%.
Klasifikasi Model Konten Kuliner Viral UMKM di TikTok Menggunakan Algoritma Naive Bayes
This study examines the development of a classification model for viral culinary content of Micro, Small, and Medium Enterprises on the TikTok platform based on video caption text. The main problem lies in the high variation of promotional language, the use of trending terms, and unstructured text formats, which make the identification of viral culinary categories difficult to perform manually and inconsistently. This study aims to design a systematic classification model to automatically and measurably group TikTok captions of enterprises into viral culinary categories. The dataset consists of 800 captions collected through scraping using the Apify API. The model development process includes preprocessing stages such as cleaning, case folding, tokenizing, normalization, stopword removal, stemming, and detokenization to produce standardized text. Feature weighting is then performed using TF-IDF, followed by model construction using the Naïve Bayes algorithm. The resulting model classifies data into ten viral culinary categories, namely Donat Mochi, Cireng, Risol, Kentang Curly, Lukchup, Dimsum Mentai, Es Teh Jumbo, Indomie Telur, Mochi Daifuku, and Dessert Box. Evaluation using a confusion matrix and classification report shows an accuracy of 0.74 or 74 percent. These results indicate the model supports automated analysis of viral culinary trends.
Perbandingan CNN Dan ResNet50 Dalam Klasifikasi Tuberkulosis Pada Citra X-Ray Paru
Tuberculosis (TB) remains a global health problem and requires rapid and consistent early screening. Chest X-rays are widely used because they are practical and economical, but manual interpretation is highly dependent on experts, which can lead to subjectivity, fatigue, and delayed diagnosis. This study aims to compare the performance of a basic Convolutional Neural Network (CNN) and a transfer learning-based ResNet50 in classifying lung X-ray images into two classes, namely TB and Normal, as well as to assess the trade-off between accuracy and computational efficiency. The dataset used is a balanced subset of 1,000 images (500 TB and 500 Normal) divided into 70% training data, 15% validation, and 15% testing with a fixed seed to ensure reproducible experiments. Preprocessing was performed by resizing the images to 224×224 pixels and normalizing the pixel values. ResNet50 used a preprocessing scheme in accordance with the pretrained model. Evaluation was performed using a confusion matrix and accuracy, precision, recall, and F1-score metrics. The test results show that CNN achieved an accuracy of 98.00% with three classification errors, while ResNet50 achieved an accuracy of 99.33% with one classification error and average precision, recall, and F1-score metrics above 0.99. In terms of efficiency, the CNN training time was approximately 40.46 seconds, while ResNet50 took a total of approximately 226.99 seconds. In the robustness test, the CNN inference time was approximately ±100 ms/image and ResNet50 was approximately ±1,900 ms/image. These findings indicate that ResNet50 excels in accuracy and generalization stability, while CNN is more efficient for fast response and limited resource requirements.
Perbandingan Kinerja Identifikasi Model VGG-19 Dengan Inception V3 Dalam Klasifikasi Penyakit Appendicitis
Appendicitis is a surgical emergency that requires rapid and accurate diagnosis. However, limitations in ultrasound (USG) image interpretation often pose a risk of misdiagnosis, particularly in scenarios with limited medical data. This study aims to determine the most effective classification model for a clinical decision support system by comparing two transfer learning-based Convolutional Neural Network (CNN) architectures: VGG-19 and InceptionV3. Utilizing a dataset of 2,168 images split into 70% training, 10% validation, and 20% testing data, the models were evaluated using metrics such as accuracy, precision, recall, F1-score, and Area Under Curve (AUC). The results demonstrate that InceptionV3 delivered significantly superior performance, achieving an accuracy of 0.9033%, an F1-score of 0.8946% for the appendicitis class, and an AUC of 0.9502%. In contrast, VGG-19 only reached an accuracy of 0.8255%, with a recall for the appendicitis class as low as 0.8019%. The poor recall performance of VGG-19 indicates a high risk of missed diagnosis. This research contributes by recommending a more reliable and effective model to support AI-based appendicitis identification, specifically in limited data scenarios.
Implementasi Algoritma CNN dengan Arsitektur MobileNet untuk Klasifikasi Citra Daun Herbal
Indonesia's diversity is very rich, one of which is that Indonesia has herbal plants which people believe are natural medicines for curing diseases. Herbal leaves show different variations in size and shape for each type, indicating that each leaf has special characteristics, shape, texture and size. Researchers used one of the Deep Learning methods, namely Convolutional Neural Network (CNN) for classifying herbal leaves. The field of image classification has found CNNs to be quite effective for image classification. CNN is a type of neural network with convolutional layers that has the ability to automatically extract important features from images. MobileNet is a CNN structure created by Google. MobileNet has advantages in efficient use of computing resources. Specifically, in the MobileNet network model, an attention module was added to improve the model's ability to extract more detailed image features, and dropout technology was added to prevent overfitting. This research method includes image preprocessing, training a convolutional neural network-based model, and evaluating its performance using accuracy, precision, recall, and F1 score metrics. Training was conducted for 20 epochs, and testing was conducted using data separated from the training data. The evaluation results show that the MobileNet model has the ability to extract visual features and produce herbal leaf image classification with an accuracy rate of 97.50% and precision, recall, and F1 scores of 98% each. The proposed model can be used in mobile-based herbal leaf identification applications due to its high performance and lightweight architecture. The stable accuracy curve at the final epoch indicates that the model does not experience significant overfitting and is able to generalize well to the test data
Pemodelan Distribusi Waktu Kedatangan Dalam Teori Antrian Dengan Pendekatan Simulasi Monte Carlo
The study of queuing systems has an important role in the development of applied mathematics, especially in probability theory and stochastic processes. Classical models such as M/M/1 generally assume a Poisson arrival process so that the inter-arrival time is exponential, but in real service systems the arrival pattern is often non-Poisson with excessive variance and long tails of the distribution. This research proposes Monte Carlo simulation-based inter-arrival time distribution modeling in two scenarios: constant arrival rate (Scenario 1) and variable (Scenario 2). The interarrival data from the simulation results were analyzed using descriptive statistics and validated with the Kolmogorov–Smirnov (K–S) goodness of fit and Chi square tests for four candidate distributions: Exponential, Gamma, Weibull, and Lognormal. Descriptively, Scenario 1 has a mean of 1.9790 and a variance of 1.3238, while Scenario 2 has a mean of 2.0076 and a variance of 2.4025 and higher skewness and kurtosis. The K–S test results show that the exponential distribution is rejected in Scenario 1 (D = 0.1708; p < 0.001) and Scenario 2 (D = 0.0906; p = 0.0135). In Scenario 1, the Gamma distribution provided the best fit (K–S D = 0.0265; p=0.9808; Chi square = 19.8667; p = 0.2811). In Scenario 2, the Lognormal distribution was the most appropriate (K–S D = 0.0230; p = 0.9963; Chi square = 7.3333; p = 0.9788). These findings confirm that the exponential Poisson assumption is not always representative and that choosing a validated arrival distribution (Gamma/Lognormal) can increase the accuracy of queuing system analysis in both stable and dynamic conditions.
Perbandingan Performa Algoritma Random Forest dan XGBoost dalam Memprediksi Hujan di Area Gunung Ungaran
Hiking activities in Mount Ungaran are frequently hindered by extreme and unpredictable weather changes, which potentially endanger the safety of hikers. One of the primary challenges in developing an automated rainfall prediction model for this region is the class imbalance in historical meteorological data, where the number of non-rainy days significantly dominates rainfall events. This condition often causes machine learning models to become biased toward the majority class, leading to a failure in detecting actual rainfall events (false negatives). This study aims to address this issue through a comparative analysis of the performance of two popular ensemble algorithms, namely Random Forest and Extreme Gradient Boosting (XGBoost). Specifically, this research investigates the impact of applying the Synthetic Minority Oversampling Technique (SMOTE) to balance the training data distribution in order to enhance minority class detection accuracy. Using the ERA5 reanalysis daily dataset for the 2019–2023 period with input variables including temperature, humidity, air pressure, and wind speed, the models were trained and validated using a time-based split method with an 80:20 ratio. Performance evaluation was conducted comprehensively using accuracy, precision, recall, and F1-score metrics. The results provide strong empirical evidence that the application of SMOTE yields the most optimal impact on the XGBoost algorithm. The combined XGBoost-SMOTE model successfully achieved the best performance with an accuracy of 80.50% and an F1-score of 83.23%, outperforming the Random Forest model which remained at an accuracy of 78.21%. In conclusion, the integration of boosting methods with data resampling techniques proves to be highly effective in improving rainfall prediction reliability in regions with complex topography.
Klasifikasi Komentar Toksik Berbahasa Indonesia di Media Sosial Berbasis Fine-Tuning IndoBERT
Social media has become a primary platform for Indonesian society to interact and exchange information online. However, freedom of expression in digital spaces is often misused through the use of harsh, offensive, and hateful language. This study aims to develop a toxic comment classification model for the Indonesian language using the IndoBERT architecture through a fine-tuning process. IndoBERT was selected for its capability to understand bidirectional semantic context and its pretraining on a Bahasa Indonesia corpus, making it suitable for handling informal language styles, abbreviations, and common code-mixing phenomena in social media texts. The dataset used in this study is the Indonesian Abusive and Hate Speech Twitter Text, consisting of 12,942 entries 11,647 for training and 1,295 for validation. The research was conducted online using Google Colaboratory with GPU acceleration. The research stages included data preprocessing, tokenization, model training, and evaluation using precision, recall, F1-score, and confusion matrix as metrics. Evaluation results show that the fine-tuned IndoBERT model achieved high performance, with an average precision of 0.8842, recall of 0.884, F1-score of 0.883, and accuracy of 0.8834. These results indicate balanced performance across classes and strong model stability in detecting both toxic and non-toxic comments. This study contributes to the development of an automated Indonesian-language content moderation system, which can be deployed as a comment detection module via API. Although limited to Twitter data and binary classification, this model has the potential to be extended toward multi-class and cross-platform classification in supporting safer and healthier digital spaces in Indonesia.
Analisis Perbandingan Seleksi Fitur dalam Memprediksi Kelulusan Mahasiswa dengan Menngunakan Artificial Neural Network
Student attrition presents a major challenge in higher education due to its direct impact on academic quality and institutional graduation rates. Detecting students who are likely to withdraw at an early stage is therefore essential to ensure that timely interventions can be made. This study investigates how three distinct feature selection techniques—Chi-Square, Information Gain, and ANOVA—affect the performance of Artificial Neural Networks (ANN) in classifying student outcomes. The data used in the experiment were drawn from academic and administrative records, which had been standardized through Min-Max normalization. The results demonstrate that each method contributes positively, with classification accuracies ranging from 88.71% to 91.37%. Information Gain emerged as the most effective approach, yielding the highest accuracy at 91.37% and a recall score of 97.29%, largely due to its capability to reduce entropy and isolate the most informative variables. ANOVA also performed consistently well with 90.82% accuracy, while Chi-Square was comparatively less effective, potentially due to its reliance on categorical variables that may not capture predictive nuances. These findings emphasize the strategic importance of applying robust feature selection to improve ANN-based prediction models. Ultimately, this research supports the design of data-driven systems aimed at reducing student dropout rates and strengthening academic retention strategies across higher education institutions.
Optimasi IndoBERT untuk Pengenalan Entitas Bernama Bahasa Indonesia pada Data Media Sosial dengan Penalaan Hiperparameter Optuna
Named Entity Recognition (NER) merupakan salah satu tugas fundamental dalam pemrosesan bahasa alami yang berperan penting dalam ekstraksi informasi terstruktur dari teks tidak terstruktur. Pada Bahasa Indonesia, kinerja model NER berbasis pre-trained BERT sangat dipengaruhi oleh konfigurasi hiperparameter pada tahap fine-tuning. Namun, banyak penelitian masih menggunakan konfigurasi bawaan atau penyesuaian terbatas, sehingga potensi peningkatan kinerja dan stabilitas model belum sepenuhnya dimanfaatkan. Penelitian ini bertujuan untuk mengevaluasi dampak optimasi hiperparameter berbasis Optuna terhadap kinerja dan stabilitas pelatihan model pre-trained BERT untuk tugas NER Bahasa Indonesia. Model yang digunakan adalah IndoBERT (indobenchmark/indobert-base-p1) yang difine-tune untuk mengenali entitas Person (PER), Organization (ORG), dan Location (LOC) dengan skema pelabelan BIO. Metode optimasi hiperparameter dilakukan menggunakan pendekatan Bayesian berbasis Named Entity Recognition (NER) is a fundamental task in natural language processing for extracting structured information from unstructured text. In Indonesian, particularly for informal and diverse social media text, the performance of NER models based on Bidirectional Encoder Representations from Transformers (BERT) is strongly influenced by hyperparameter configurations during fine-tuning. However, many studies still rely on default settings or limited adjustments, so the potential improvements in performance and training stability have not been fully exploited. This study evaluates the impact of hyperparameter tuning using Optuna with a Tree-structured Parzen Estimator (TPE) on the performance and training stability of IndoBERT (indobenchmark/indobert-base-p1) on Twitter/X data. The main contribution of this work is an empirical evaluation of how hyperparameter tuning improves IndoBERT’s performance and training stability, and the resulting recommendations of reliable configurations for reproducible experiments and practical deployment of Indonesian NER. The dataset is annotated using the Begin–Inside–Outside (BIO) labeling scheme for three entity types: person (PER), organization (ORG), and location (LOC). The optimization objective is defined as the F1-score on the validation set. The results show that the Optuna configuration achieves a precision of 0.9338, recall of 0.9312, F1-score of 0.9325, and accuracy of 0.9854 on the test set, outperforming the baseline with an F1-score of 0.9253 and accuracy of 0.9837. Multi-seed evaluation indicates consistent improvements, with an average F1 of 0.9302 ± 0.0016 compared to 0.9238 ± 0.0009 for the baseline. These findings confirm that Optuna-based hyperparameter tuning improves both the performance and reliability of IndoBERT for Indonesian NER on social media text.
Perbandingan Algoritma Machine Learning untuk Klasifikasi Kopi Menggunakan Data Sensor Electronic Nose dan Tongue
Coffee is a leading Indonesian commodity with a diversity of aromas and flavors influenced by variety and region of origin. However, the process of identifying and classifying coffee types is still often carried out conventionally through sensory testing, which is subjective, time-consuming, and dependent on panelist expertise. This situation encourages the need for a more objective and consistent automated approach based on sensor technology and machine learning. This study aims to compare the performance of several machine learning algorithms, namely Logistic Regression, Support Vector Classifier (SVC), and Random Forest, in classifying Indonesian coffee types using multisensor Electronic Nose and Electronic Tongue data. The data used comes from gas, temperature, and pH sensors with a total of 1,503 samples representing ten coffee classes. The preprocessing stage includes data cleaning using the Interquartile Range (IQR) method to remove outliers and noise reduction using the Moving Average method. The results show that the application of data cleaning and noise reduction significantly improves the performance of all classification models. Among the algorithms tested, Random Forest showed the most stable and superior performance in classifying coffee types. These findings confirm that the combination of appropriate data preprocessing and appropriate algorithm selection plays a crucial role in improving the accuracy of machine learning-based coffee classification systems.
A Optimization of Sales Strategies and Inventory Forecasting for Processed Banana Products Utilizing the Conceptual Framework of Economic Efficiency and Accounting Precision Based on Simple Moving Average
Fluctuations in demand for processed banana products often lead to inaccurate inventory planning at the MSME scale, resulting in decreased operational efficiency and potential accounting inaccuracies in inventory valuation and the calculation of Cost of Goods Sold (COGS). The calculation of raw material stock forecasting for 2024-2025 produces the following predicted values: 124 bunches of bananas, 80 pieces of chocolate, 81 kg of cooking oil, and 42 kg of granulated sugar. This simple, fast, and accurate forecasting process enables producers to more accurately predict product demand, ultimately reducing the risk of overstocking or shortages. This study aims to optimize sales strategies and inventory forecasting for processed banana products through a conceptual framework that integrates economic efficiency. The method used is the Simple Moving Average (SMA) to forecast inventory needs based on historical sales data at the BananaChips MSME, by testing several variations of the forecasting period to obtain the most stable and representative results. Overall, the recapitulation results show that the Cooking Oil raw material has the highest forecasting accuracy, with the lowest MAPE of 1.81% (MAD 1.50, MSE 5.20). Meanwhile, Granulated Sugar raw material recorded the highest MAPE value of 5.08% (MAD 2.25, MSE 9.73), followed by Chocolate (MAPE 2.43%) and Banana (MAPE 2.18%). The implementation results show an increase in stock management efficiency of up to 20% and a 15% decrease in excess raw materials. These findings indicate that integrating SMA forecasting with an economic efficiency framework and accounting accuracy can improve the quality of inventory and sales decision-making, thereby strengthening the profitability and sustainability of the banana-processed product business at the Bananachips MSME
Analisis Komparatif Model Random Forest dan XGBoost untuk Klasifikasi Penyakit Jantung Berbasis Data Klinis
Heart disease remains a major global health challenge due to its increasing prevalence, highlighting the need for accurate and reliable early diagnostic systems. This study aims to analyze and compare the performance of Random Forest (RF) and XGBoost algorithms for heart disease classification, and to identify the most suitable model for data-driven clinical decision support. Addressing a research gap in ensemble learning studies, this research conducts a comprehensive comparative evaluation using the UCI Heart Disease Dataset. The proposed methodology includes data preprocessing, feature encoding, normalization, class imbalance handling using the Synthetic Minority Oversampling Technique (SMOTE), and hyperparameter optimization based on RandomizedSearchCV. Model performance is evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and Matthews Correlation Coefficient (MCC), supported by Feature Importance analysis. The results demonstrate that both ensemble models achieve strong predictive performance, with consistently high F1-scores above 0.88. XGBoost exhibits superior overall performance, achieving the highest F1-score of 0.8995 and Precision of 0.8785, making it more effective in minimizing False Positive predictions. In contrast, Random Forest shows superior sensitivity, with the highest Recall of 0.9510 and ROC-AUC of 0.9582, along with better cross-validation stability. These findings indicate that the selection of heart disease classification algorithms should be aligned with specific clinical objectives, and the results of this study are expected to contribute to the development of effective machine learning–based clinical decision support systems.
Analisis Sentimen Multi-Platform Media Sosial pada Program Makan Bergizi Gratis Menggunakan Ensemble IndoBERT-SVM
Sentiment analysis of public policy on social media faces significant challenges due to linguistic heterogeneity across platforms and limitations of single models in capturing the diversity of opinion expressions. Previous studies tend to employ single-platform and single-model approaches that potentially generate representational bias and accuracy degradation of up to 12–15% when applied to different platform contexts. This study aims to develop a soft voting-based ensemble model that integrates Support Vector Machine (SVM) and IndoBERT to analyze public sentiment toward the Free Nutritious Meal (MBG) Program across multiple platforms, and to evaluate the effectiveness of the ensemble approach compared to single models in addressing variations in linguistic characteristics of digital platforms. The research dataset consists of 7,500 comments from X, TikTok, and YouTube collected from January 6 to September 28, 2025, processed through informal Indonesian language preprocessing, lexicon-based labeling, and stratified split division. Results demonstrate that SVM performs optimally on TikTok (accuracy 98.1%, macro F1 98.0%) but weakly on the neutral class in X (F1 51.0%), while IndoBERT excels in handling pragmatic ambiguity in X (neutral F1 74.0%) despite slightly declining on TikTok (macro F1 93.0%). The ensemble model produces the most balanced performance with accuracies of 92.53% (YouTube), 95.73% (TikTok), 92.53% (X), and macro F1 scores of 85.07%, 94.33%, 84.92% respectively. The contributions of this research include the development of a multi-platform sentiment analysis approach that addresses single-platform bias, improved classification generalization capability across heterogeneous digital ecosystems, and provision of evidence-based evaluation instruments for improving government policy implementation and communication.
Penerapan Data Mining Menggunakan Teknik Classification Untuk Melihat Potensi Kepatuhan Wajib Pajak Badan
The application of data mining using classification techniques has significant potential to assist tax authorities in identifying and mapping the compliance levels of corporate taxpayers. This study aims to develop a corporate taxpayer compliance classification model using the Naive Bayes algorithm based on the ratio of Annual Tax Return (SPT) filing and the ratio of tax payments. The data used consist of aggregated data from Tax Service Offices (Kantor Pelayanan Pajak/KPP) for the 2022–2024 period obtained from the Directorate General of Taxes. The research stages follow the Knowledge Discovery in Databases (KDD) methodology, which includes data selection, preprocessing, transformation, modeling, and evaluation. The experimental results indicate that the Naive Bayes model is able to classify compliance levels with an accuracy of 100%, precision of 1.00, recall of 1.00, and an F1-score of 1.00. These findings suggest that the SPT filing ratio is the dominant factor in determining corporate taxpayer compliance. The proposed model can be utilized as a decision support system to assist tax authorities in determining supervision and guidance priorities for corporate taxpayers
Evaluasi Pengaruh RFE Terhadap Kinerja Random Forest dengan SVM pada Klasifikasi Kemiskinan Kabupaten/Kota Indonesia
Poverty is a socio-economic issue that remains a concern in Indonesia, with differences in development characteristics between districts/cities causing wide variations in indicators that are intercorrelated. Feature redundancy and the existence of extreme values have the potential to reduce the generalization ability of classification models and reduce the interpretability of results. Therefore, an approach is needed that not only produces high accuracy but is also capable of identifying the most relevant indicators. Therefore, an approach is needed that not only produces high accuracy but is also capable of identifying the most relevant indicators. This study aims to evaluate the effect of Recursive Feature Elimination (RFE) on the performance of Support Vector Machine (SVM) and Random Forest in classifying the poverty status of districts/cities in Indonesia. The dataset used consists of 514 observations with two target classes, namely non-poor and poor. The preprocessing stage included data cleaning and outlier handling using the IQR capping method, then the data was divided into 80% training data and 20% test data. Testing was conducted on four scenarios: SVM, SVM+RFE, Random Forest, and Random Forest+RFE. Evaluation used a confusion matrix, accuracy, precision, recall, and F1-score. The results show that RFE does not change the accuracy of SVM (0.971), but improves the performance of Random Forest from 0.981 to 0.99 and improves the precision of the minority class. The Random Forest+RFE combination is the most effective and efficient configuration for regional poverty classification.
Studi Komparatif Algoritma Random Forest dan Logistic Regression dalam Analisis Sentimen Ulasan Aplikasi E-Wallet Dana
The increasing use of digital wallets in Indonesia has led to a growing number of user opinions expressed, including on the DANA platform in the Play Store. These reviews reflect users' experiences and satisfaction levels, necessitating sentiment analysis to comprehend public opinions about the application’s service quality. The study conducts an analytical comparison between Random Forest and Logistic Regression methods in classification sentiments for DANA application. Data was obtained through scraping techniques, resulting in 2,068 reviews after the cleaning process. The analysis stages include text preprocessing, labeling based on review scores, weighting using TF-IDF, and modeling with both algorithms. The evaluation results demonstrate that Random Forest obtains an accuracy of 86.23%, while Logistic Regression obtains an accuracy of 84.54%. Both models are capable of classifying positive sentiments well but are less optimal in detecting negative sentiments. Random Forest shows higher performance compared to Logistic Regression within the task in sentiment analysis for DANA app reviews. Thus, we can conclude that using the random forest algorithm is able to produce accurate sentiment analysis and can act as a basis for making decisions in further research
Implementasi Arsitektur Keamanan Terintegrasi IDS, WAF Dan FIM Berbasis Wazuh Pada Platform Open Jurnal System Mengacu Pada NIST Cybersecurity Framework
The utilization of Open Journal Systems (OJS) as a scientific publishing platform faces significant security threats, including SQL Injection, Cross-Site Scripting (XSS), and webshell injection, which may compromise data integrity and service availability. This study aims to design and evaluate an integrated security architecture based on Wazuh through the implementation of an Intrusion Detection System (IDS), Web Application Firewall (WAF), and File Integrity Monitoring (FIM) using the NIST Cybersecurity Framework approach. The research methodology includes vulnerability identification across 11 journals in 7 universities, the development of a defense-in-depth architecture, and controlled penetration testing based on OWASP Top 10 scenarios. Testing results from 30 attack scenarios demonstrate a 100% detection rate for SQL Injection and webshell injection, and an 80% detection rate for XSS attacks. The system successfully blocks malicious requests with 403 Forbidden responses and generates real-time alerts through centralized log correlation in Wazuh. However, potential false positives were observed in several generic security rules, indicating the need for rule fine-tuning to align with OJS traffic characteristics. Overall, the integrated security approach measurably enhances threat detection and incident response capabilities.
Klasifikasi Spesies Suara Burung Menggunakan YAMNet dan Random Forest untuk Konservasi Alam
Monitoring and identification of bird species is an important aspect of biodiversity conservation, but manual identification methods based on direct observation and expert listening still have limitations in terms of time, cost, and subjectivity. Other challenges arise due to variations in the quality of sound recordings, the presence of environmental noise, and the similarity in vocalization patterns between species that make it difficult to automate the classification process. This study aims to develop an automatic classification system of bird species based on acoustic signals by combining the YouTube Audio Event Network (YAMNet) model and the Random Forest algorithm. YAMNet is utilized to extract spectral log-Mel features that represent the frequency and temporal characteristics of bird sounds, while Random Forest is used as a classifier to determine species based on those features. The dataset used is the Sound of 114 Species of Birds till 2022, which includes species variation, recording duration, and complex acoustic conditions. The results showed that the features produced by YAMNet were able to form separation between species visually through Principal Component Analysis (PCA), although there was still overlap in species with similar vocalization characteristics. Evaluation using the confusion matrix shows that some species can be classified with a high degree of accuracy, while misclassification occurs mainly in species with similar frequency patterns. Receiver Operating Characteristic (ROC) analysis yields Area Under Curve (AUC) values of up to 0.98 in certain species, indicating the model's excellent discriminating ability. These findings suggest that the integration of YAMNet and Random Forest has the potential to be an efficient and reliable solution to support automated bird species identification systems in nature conservation.
Penerapan Metode YOLOv8 untuk Deteksi Jalan Berlubang Berbasis Object Detection
Road infrastructure damage, particularly potholes, is a critical issue that contributes to an increased risk of traffic accidents in Indonesia. This condition highlights the need for a road monitoring system that is capable of operating quickly, accurately, and continuously. This study aims to develop and evaluate an automated pothole detection system based on computer vision using the YOLOv8 (You Only Look Once version 8) method, a deep learning algorithm designed for real-time object detection. Dataset collection was conducted through two approaches, namely acquiring data from the Kaggle platform and capturing real-world road images directly using a smartphone camera to enhance data diversity and represent actual road conditions. All collected images were annotated using the Roboflow platform to generate labeled data suitable for model training. The YOLOv8 model was trained using a total of 345 images, consisting of 241 training images, 69 validation images, and 35 testing images. The training results indicate that the model achieved a mean Average Precision (mAP) of 93%, with a precision of 88% and a recall of 82%, demonstrating strong detection and localization performance. Furthermore, real-time testing using a smartphone camera showed that the system achieved an accuracy of over 85% under real-world conditions. These findings demonstrate that YOLOv8 has strong potential as an efficient and reliable automated pothole detection system, supporting road infrastructure maintenance and enhancing road user safety
Analisis Pola Dan Prediksi Churn: Hybrid Segmentasi SOM+K-Means Dan Klasifikasi Machine Learning
Customer churn is a significant challenge in the banking industry, which can have a substantial impact on the profitability and long-term sustainability. Customer churn management is typically addressed using binary classification approaches, which often fail to provide the depth needed to understand customer characteristics. This study proposes addressing churn through customer segmentation as an preliminary step before classification, offering a clearer and deeper understanding of each segment’s characteristics. The research combines Self-Organizing Map (SOM) and K-Means clustering to create interpretable segments. The SOM+K-Means model is used for segmentation and visual mapping, which helps identify customer groups at risk of churn and the key features influencing these risks. Cluster labels are then used as features for classification using three machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB). In the classification phase, the Synthetic Minority Oversampling Technique (SMOTE) and GridSearchCV are applied to address class imbalance and optimize model parameters. XGB outperformed the other models with an accuracy of 85% and an AUC score of 85%. These results highlight that customer segmentation with SOM+K-Means enables more effective churn management strategies, while XGB proves to be a strong model for churn prediction. This research contributes to the application of clustering and machine learning classification techniques in churn analysis within the banking industry, offering a pathway to better customer retention strategies and lower churn rates.
Evaluasi Komparatif Random Forest, XGBoost, LightGBM, dan K-Nearest Neighbors untuk Prediksi Cuaca di Kota Semarang
Accurate weather predictions play an important role in assisting strategic decisions in various fields, from agriculture to disaster management. However, there is a fundamental challenge in creating automatic prediction models, namely the nature of meteorological datasets, which are often imbalanced in class distribution. This phenomenon causes conventional machine learning algorithms to favor the dominant class and be less capable of detecting the rare class (rain), as seen in the low sensitivity values. This study aims to overcome this bias problem and improve the accuracy of daily rainfall classification using a comparative approach with four algorithms: Random Forest, K-Nearest Neighbor (KNN), LightGBM, and XGBoost. As the main method to overcome data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to generate new samples in the underrepresented class. Model performance was evaluated comprehensively using a confusion matrix, One-vs-Rest (OvR) strategy, and conventional evaluation metrics. The results of the experiments on the baseline model showed a failure to detect the minority class with very low Recall and F1-Score values (< 0.30). The application of SMOTE was proven to significantly improve Recall and F1-Score compared to the SMOTE. LightGBM using SMOTE was recorded as the most superior model that successfully balanced all evaluation metrics.
Klasifikasi Penyakit Daun Mangga Menggunakan CNN Berbasis Transfer Learning Dengan Model Arsitektur VGG16, DenseNet121, dan InceptionV3
Mango is one of the important fruits in Indonesia, but its production is often disrupted by leaf diseases and pests that are difficult to detect early. Manual disease recognition methods usually depend on observers and are not always accurate. This study aims to create an automated system to classify mango leaf diseases, using deep learning techniques based on the Convolutional Neural Network (CNN) algorithm. This study also compares three models, namely VGG16, DenseNet121, and InceptionV3, by applying the transfer learning method. The dataset used consists of 4,000 images divided evenly into 8 categories, consisting of 7 types of diseases (Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, Sooty Mold) and 1 category of healthy plants. Evaluation was carried out using the 5-Fold Cross-Validation method to ensure valid results. The results show that all three models are able to provide an accuracy of more than 90%. The VGG16 model showed the best and most stable performance, with an accuracy of 93.25%, a Precision of 0.93, a Recall of 0.93, an F1-Score of 0.93, and an AUC-ROC of 0.98. Meanwhile, InceptionV3 achieved an accuracy of 92.38% and DenseNet121 reached 91.25%. Therefore, VGG16 is recommended as the primary model due to its better ability to extract texture features and accurately recognize mango leaf diseases. VGG16 architecture is able to outperform complex models in efficiently extracting mango leaf texture features, making it very potential to be used as a basis for real-time plant disease diagnosis applications for farmers
Analisis Sentimen Komentar Cyberbullying Terhadap Fenomena Flexing di Tiktok Menggunakan Artificial Neural Network
The rising trend of flexing on TikTok has created a dynamic digital space that often triggers varied user reactions, including subtle forms of cyberbullying. This study aims to analyze public sentiment toward flexing content and evaluate the performance of the Artificial Neural Network (ANN) algorithm in classifying user comments. A total of 4,013 comments were collected through a scraping process on the TikTok account of Miechel Halim and automatically labeled using a lexicon-based approach. The comments were then pre-processed and transformed into Term Frequency–Inverse Document Frequency (TF-IDF) representations before being split into training and testing datasets with an 80:20 ratio. The ANN model was trained under two scenarios before and after the application of the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Experimental results show that the initial model achieved an accuracy of 89.79%, which increased to 90.04% after SMOTE, accompanied by an improvement in the recall of the negative class. These findings indicate that ANN is effective for sentiment classification of TikTok comments, although informal language patterns and highly imbalanced labels remain challenges in identifying negative or potentially harmful remarks related to cyberbullying.
Supervised Machine Learning Algorithms untuk Klasifikasi Penyakit Jantung
Heart disease is one of the leading causes of death worldwide, requiring accurate predictive methods to support early detection and clinical decision making. This study aims to analyze and compare the performance of three supervised machine learning algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF), in classifying heart disease using the Cleveland Heart Disease dataset consisting of 303 patient records with 13 clinical features. The research stages include data preprocessing, splitting the dataset into 80% training data and 20% testing data, model training, and hyperparameter optimization using GridSearchCV with 5-fold cross-validation. After optimization, prediction was performed using test data followed by performance evaluation to assess generalization ability. Model performance was evaluated using accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrix. The results show that KNN and Random Forest achieved the highest accuracy of 90.16%. The KNN model obtained a recall value of 1.0000, indicating perfect sensitivity in detecting positive cases, while Random Forest demonstrated a more balanced performance between precision and recall with the highest AUC value of 0.9481. Based on these findings, KNN is considered the most suitable model for medical screening purposes, as it successfully detected all positive heart disease patients without producing false negatives. This study is expected to serve as a reference for implementing clinical databased machine learning as a decision support tool for early heart disease detection.
Analisis Perbandingan Kinerja Arsitektur CNN untuk Klasifikasi Penyakit Tuberkulosis pada Citra Rontgen Thoraks
Tuberculosis (TB) is a chronic infectious disease and one of the leading causes of mortality worldwide. Conventional diagnostic processes are often hampered by high costs and technical complexity; consequently, Chest X-Ray (CXR) examinations combined with Artificial Intelligence (AI)-based computer detection systems have emerged as a more efficient alternative. This study aims to comparatively analyze the effectiveness of three fundamental CNN architectures AlexNet, ZFNet, and ResNet18 in detecting TB from chest X-ray images. The research methodology employs the Knowledge Discovery in Databases (KDD) framework on a public CXR dataset. Hyperparameter optimization was implemented using a Grid Search strategy integrated with 5-Fold Cross-Validation to systematically identify the optimal configuration. Experimental results indicate a significant positive correlation between architectural depth and diagnostic performance. Based on the optimal parameters identified through Grid Search specifically a learning rate of 0.0001 and a batch size of 32 the ResNet18 model demonstrated superior performance, achieving 99.28% scores for Accuracy, and 100% for precision, recall, F1-score and AUC-ROC. The superiority of ResNet18 lies in its residual learning mechanism, which effectively addresses the vanishing gradient problem and facilitates the extraction of complex pathological features. The combination of ResNet18 with Grid Search optimization demonstrates that the synergy of modern architecture and systematic tuning yields a highly reliable Computer-Aided Detection (CAD) system, surpassing the results of previous studies
Studi Komparatif Model Machine Learning untuk Klasifikasi Penyakit Jantung dengan SMOTE pada Data Imbalanced
This study examines the application of the Synthetic Minority Over-sampling Technique (SMOTE) for heart disease classification using four machine learning algorithms, namely Logistic Regression, Random Forest, LightGBM, and XGBoost, based on the Heart Disease UCI dataset consisting of 920 medical records with 16 clinical features. The original severity labels (0–4) are converted into two classes, namely not sick (0) and sick (1–4), to better align with binary decision-making needs in clinical screening. The experiments are conducted in two scenarios: (1) training models on the original data without handling class imbalance and (2) training models with SMOTE applied only to the training data within a pipeline, accompanied by hyperparameter tuning using k-fold cross-validation. Model performance is evaluated using accuracy, precision, recall, F1-score, AUC-ROC, as well as confusion matrix analysis to examine misclassifications, particularly false negatives in the sick class. In the scenario without SMOTE, the best model, Logistic Regression, achieves an accuracy of 84.78%, recall of 84.31%, F1-score of 86.00%, and AUC-ROC of 91.95%, although the number of false negatives remains relatively high. After applying SMOTE, there is an increase in recall and F1-score for the positive class across all models, with the best performance obtained by Random Forest with SMOTE, which achieves an accuracy of 86.96%, recall of 87.25%, F1-score of 88.12%, and AUC-ROC of 93.34%. These findings indicate that the combination of SMOTE and hyperparameter optimization can produce a more balanced and reliable heart disease classification model that is potentially useful as a clinical decision support system in healthcare services.
Analisis Komparasi Arsitektur Deep Learning untuk Klasifikasi Penyakit Daun Cabai
The productivity of chili (Capsicum annuum L.) in Indonesia faces significant challenges due to leaf diseases, which are estimated to reduce harvest yields by up to 35%. Conventional detection methods relying on visual observation often lack accuracy due to the high visual similarity between disease symptoms. This study focuses on a comparative evaluation of three leading Deep Learning architectures VGG16, ResNet50, and InceptionV3 in classifying six types of chili leaf diseases using a public dataset. The research implements a high-resolution image strategy (512 x 512 pixels) to maximize the extraction of disease texture features. The methodology employs a Transfer Learning approach with a standardized hyperparameter tuning scheme. Experimental results indicate that the use of high-resolution images significantly impacts model accuracy. The VGG16 architecture achieved the best performance with a testing accuracy of 99.83% and an F1-Score of 1.00, outperforming ResNet50 (99.75%) and InceptionV3 (84.00%). Confusion Matrix analysis demonstrates that VGG16 possesses superior stability in distinguishing disease classes with high visual similarity, such as Bacterial Spot and Cercospora. The study concludes that architectures preserving deep spatial information, such as VGG16, are more effective for high-resolution image-based plant disease diagnosis compared to more complex architectures that perform aggressive feature compression.
Sistem Informasi Prediksi Penjualan Bubuk Teh PT XYZ Menggunakan Metode ARIMA Sebagai Pendukung Pengambilan Keputusan
Sales of tea powder experience significant fluctuations influenced by consumption trends, seasonal factors, and changes in market demand, which may lead to overstock and understock problems. These conditions highlight the need for an accurate and practical forecasting tool that can directly support managerial decision-making. This study aims to develop a web-based sales and revenue forecasting information system by integrating the Autoregressive Integrated Moving Average (ARIMA) method. The dataset consists of historical tea powder sales from May 2023 to March 2025, aggregated into monthly time series data. The research stages include data preprocessing, visualization, stationarity testing using the Augmented Dickey-Fuller (ADF) test, differencing, ARIMA model identification and parameter estimation, forecasting, and model evaluation using MAE, MSE, and MAPE. The evaluation results indicate that ARIMA(1,1,2) is the best-performing model, achieving an MAE of 4.07, an MSE of 3.15, and a MAPE of 147.34%. The forecasting results show fluctuating patterns in future sales and revenue. The main contribution of this research lies in the integration of the ARIMA forecasting model into a web-based information system that enables automatic and real-time prediction, providing practical support for inventory planning and data-driven managerial decision-making.
Evaluasi Kinerja Algoritma AES-128 dan SPECK-128/128 pada Sistem Smart Door Lock Berbasis IoT
The Internet of Things (IoT) is increasingly being used in smart home systems, one of which is the Smart Door Lock. However, data communication on IoT devices is vulnerable to network attacks such as sniffing and man-in-the-middle, especially since many devices are still designed with weak security mechanisms. The main issue studied in this research is how to protect RFID data from being easily intercepted, while maintaining efficiency on devices with limited resources. The proposed solution is the implementation of AES-128 and SPECK-128/128 cryptographic algorithms on the MQTT communication protocol using ESP8266 devices. The research was conducted experimentally with 50 measurements for each algorithm, covering encryption and decryption time parameters, memory usage, and encryption effectiveness against sniffing. The results show that AES-128 has stable performance but requires more execution time and memory because its algorithm is complex with many stages. In contrast, SPECK-128/128 is faster and more memory-efficient thanks to its simple ARX-based structure, although its consistency is slightly lower. Sniffing analysis shows that both algorithms are capable of encrypting all packets with 100% security, so that no plaintext data can be read. Thus, this study confirms that there is a trade-off between security and efficiency
Sistem Presensi Mahasiswa Berbasis Pengenalan Wajah Real-Time dengan Deteksi Anti-Spoofing Menggunakan YOLOv8 dan ArcFace
Student attendance recording is an important aspect in supporting discipline and administrative order in academic environments. Manual attendance methods still have several limitations, such as potential fraud and inefficient recapitulation processes. This study aims to develop a real-time face recognition-based student attendance system by implementing the YOLOv8 algorithm for face detection and ArcFace for identity recognition, complemented with an anti-spoofing feature to prevent fraudulent attempts. The system is designed to detect faces directly, recognize registered student identities, and record attendance automatically. The main contribution of this study lies in the integration of YOLOv8-based face detection, ArcFace-based face recognition, and an anti-spoofing mechanism into a single unified real-time attendance system. Experimental results show that the system successfully recognizes all registered students with a 100% success rate. The YOLOv8 anti-spoofing model demonstrates excellent performance in distinguishing real and fake faces, achieving an mAP@0.5 value of 0.995 and an F1-score close to 1. The system is also able to record attendance time in real time according to the actual time and present attendance data systematically. Based on these results, the developed real-time face recognition attendance system is accurate, secure, and feasible to be implemented as an attendance solution in academic environments
Perbandingan Metode Euclidean dan Manhattan Distance dalam Implementasi Algoritma K-Means dan K-Medoid pada Pengelompokkan Faktor Dominan Perceraian di Kabupaten Bojonegoro
The divorce rate in Bojonegoro Regency continues to increase, driven by various social factors such as constant disputes, economic pressure, and household disharmony. Consequently, an analysis is required to map dominant and non-dominant factors more effectively. This study aims to group the factors causing divorce in Bojonegoro Regency for the 2021–2023 period and determine the most optimal clustering method. The research utilizes K-Means and K-Medoids algorithms with Euclidean and Manhattan distance metrics applied to both raw data and data normalized using the Min–Max Scaler, evaluated via the Silhouette Score. The results indicate that data normalization improves cluster quality, and K-Means with Manhattan distance on normalized data achieves the best performance, yielding a Silhouette Score of 0.849547. Cluster displacement analysis reveals that the grouping patterns remain relatively consistent across years, with "constant disputes" consistently emerging as the dominant factor, while other factors remain in the non-dominant cluster with similar patterns. This study demonstrates that K-Means with Manhattan distance on normalized data is more effective for clustering divorce factors. These findings can serve as a methodological foundation for the local government in formulating data-driven social policies and interventions.
Analisis Perbandingan Algoritma SVM, Logistic Regression, Naive Bayes, dan XGBoost Untuk Deteksi Fake News
The rapid growth of digital technology and internet access has completely changed how information is shared, enabling content to spread quickly across various online platforms. However, these advancements have also made it easier for misleading or entirely fabricated news to circulate, posing serious risks to social stability, political environments, and public health. This study tackles this problem by employing several machine learning-based classification methods for analyzing textual data. Four algorithms Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), and Extreme Gradient Boosting (XGBoost) were applied to detect linguistic patterns that differentiate genuine news from fake content. A major contribution of this research is the creation of a custom dataset gathered directly from Indonesian online news portals, comprising a total of 4,909 entries. The evaluation results demonstrate exceptionally high accuracy across the models: 99.69% for SVM, 99.39% for LR, 99.29% for NB, and 99.19% for XGBoost. To verify reliability, each model was further evaluated using cross-validation, yielding average accuracy scores of 99.57% (SVM), 99.52% (LR), 99.44% (NB), and 99.49% (XGBoost). These findings confirm that all four classifiers are highly effective and well-suited for identifying fake news in text-based data.