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.
Perancangan Sistem Pelayanan Klinik Gigi Berbasis Web dan Mobile dengan Integrasi Sistem Antrian Digital menerapkan Algoritma First Come First Served (FCFS)
Efficient and responsive dental clinic services are essential for improving patient satisfaction and ensuring streamlined healthcare delivery. However, many independent dental clinics still rely on manual procedures for patient registration, scheduling, and queue management, which often lead to long waiting times, unpredictable service flow, and a high risk of administrative errors. These issues negatively impact the overall quality of service and reduce operational effectiveness. This study aims to design and implement a web- and mobile-based dental clinic service system integrated with a digital queuing mechanism to address these challenges. The system was developed using Flutter as the patient-facing mobile interface, Flask as the backend service responsible for managing business processes, and Supabase as the real-time database and user authentication platform. Key features of the system include patient registration and login, service booking, automated scheduling based on reservation time or arrival order, real-time queue monitoring, electronic medical record management, and integrated digital payments to facilitate transactions. Based on functional and performance testing, the system was able to reduce service processing time by up to 80% compared to manual methods, minimize data entry errors, and significantly improve the accuracy of queue management. User feedback also indicated that the system provides a more convenient and informative experience, as patients can easily monitor their queue position through the mobile application. Overall, the proposed system effectively enhances service efficiency, data accuracy, and the quality of dental clinic operations, while also demonstrating strong potential for further development and scalability across larger healthcare facilities
Copyright (c) 2026 JURNAL RISET KOMPUTER (JURIKOM)
Penggunaan Algoritma Stacking Classifier Pada Sistem Deteksi Risiko Kardiovaskular
Cardiovascular disease is a leading cause of global death. However, the complexity of medical data often makes conventional models fail to capture hidden patterns, resulting in suboptimal predictive performance. This study evaluates the effectiveness of a hybrid model that integrates K-Modes Clustering with the Stacking Classifier algorithm and tests whether the model's complexity can provide significant performance improvements compared to a single model. The methodology involves data preprocessing including outlier handling, clinical feature engineering, and cluster feature extraction using K-Modes (K=2). The Stacking Classifier architecture is built using five optimized heterogeneous base-learners (CatBoost, Decision Tree, MLP, SVC, Logistic Regression) and XGBoost as a meta-learner, validated through Stratified 5-Fold Cross-Validation. The results showed that although K-Modes effectively mapped clinically valid risk categories, the Stacking Classifier model (87.99% accuracy and 95.89% ROC-AUC) was not able to surpass the performance of the best single model, namely CatBoost (88.03% accuracy and 95.90% ROC-AUC). The most significant finding lies in the computational time efficiency, where the Stacking Classifier algorithm required 560 times longer computational time (7587.7686 seconds) than CatBoost (13.4635 seconds) without providing a commensurate performance improvement. This indicates that Boosting-based algorithms are able to capture complex patterns without requiring additional ensemble layers, so that an optimized single model is more recommended for real-world implementations by providing the best balance between prediction accuracy and computational time efficiency.
Implementasi Logika Fuzzy Mamdani pada Aplikasi Sistem Pakar Diagnosis Penyakit Ternak Babi Berbasis Web
Diseases in pig livestock are a major cause of economic losses for farmers, especially when treatment is delayed, leading to disease spread and increased mortality rates. In addition, high consultation costs and limited access to animal health service centers (puskeswan) often result in farmers performing manual diagnosis, which is inaccurate and may worsen the livestock’s condition. This study develops a web-based expert system for diagnosing pig diseases using the Mamdani fuzzy logic method as a solution to assist farmers in conducting independent and early diagnosis based on observable symptoms. The system is built using Python with the Flask framework, Bootstrap for the user interface, and Supabase as a cloud-based database. The diagnosis process consists of three main stages: symptom fuzzification, rule-based fuzzy inference, and defuzzification using the centroid method. Testing was conducted on 8 types of diseases and 45 symptoms using validated test data, resulting in an accuracy of 100%, with the system consistently producing diagnostic outcomes that match the symptom inputs. The system also includes an online consultation feature with experts, enhancing accessibility and effectiveness in identifying diseases in pig livestock.
Analisis Perbandingan Metode Random Forest, XGBoost, dan Logistic Regression Untuk Klasifikasi Deteksi Dini Penyakit Diabetes
Diabetes Mellitus is a chronic disease with a continuously increasing prevalence, posing serious challenges to public health and contributing significantly to the global economic burden. The often non-specific nature of early symptoms increases the risk of delayed diagnosis, highlighting the need for accurate early detection approaches to support clinical decision-making. This study aims to analyze and compare the performance of three machine learning algorithms Logistic Regression, Random Forest, and XGBoost in classifying diabetes risk based on several clinical parameters, including age, body mass index (BMI), blood pressure, glucose level, and HbA1c. The dataset used in this research was obtained from the Diabetes Prediction Dataset, consisting of 100,000 records. The research process involved handling missing data, applying One-Hot Encoding to categorical variables, normalizing numerical features, and addressing class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics to provide a comprehensive assessment. The experimental results indicate that XGBoost achieved the best performance, with an accuracy of 96.88% and a ROC-AUC value of 98.00%. Meanwhile, Random Forest attained an accuracy of 95.68% with an F1-Score of 74.76%, while Logistic Regression recorded an accuracy of 88.96% and the highest recall value of 89.12%. These findings suggest that ensemble learning methods, particularly boosting approaches, are more effective in improving the accuracy of diabetes and non-diabetes classification. The primary contribution of this study lies in providing a multi-metric comparative analysis that can serve as a reference for selecting the most effective machine learning model in the development of medical decision support systems for early diabetes detection.
Analisis Komparatif Model Regresi Machine Learning untuk Prediksi Prestasi Akademik Siswa dengan Optimasi Hyperparameter
Low accuracy in the early identification of at-risk students often hinders timely academic intervention. This study analyzes and compares seven machine learning algorithms to predict student academic achievement, aiming to provide a foundation for a reliable early warning model. The dataset includes 2.392 students with 15 features covering demographics, learning behavior, and environmental support. Model training was performed using GridSearchCV optimization combined with stratified cross-validation to mitigate overfitting.Performance was evaluated using MAE, RMSE, and R². The results show CatBoost performed the best R² = 0,774; RMSE = 0,581; MAE = 0,306) followed by LightGBM (R² = 0,771) and Gradient Boosting (R² = 0,767), while MLP showed the lowest performance. Feature importance analysis placed GPA as the dominant predictor, followed by absenteeism and weekly study time. These findings affirm the superiority of boosting-based models in capturing complex nonlinear relationships and provide a practical framework for educational institutions to build data-driven early warning systems.
Copyright (c) 2025 JURNAL RISET KOMPUTER (JURIKOM)
Rekomendasi Klasifikasi Dan Desain Otomatis Menu Restoran Kopi XYZ Berbasis Web Menggunakan Metode Naïve Bayes
Abstract The advancement of information technology has accelerated digital transformation in the culinary industry, particularly in menu management, which demands fast, structured, and error-minimized processes. In many MSMEs, menu classification and design activities are still performed manually, resulting in inefficiencies. This study developed a web-based system capable of automatically classifying menu categories using the Multinomial Naïve Bayes algorithm and generating menu designs automatically. The dataset consists of 148 menu items covering food, beverages, and snacks. The features used include text-based menu names processed using TF-IDF, as well as numerical price attributes. The data were split into 80% training and 20% testing portions. The results show that the Multinomial Naïve Bayes model achieved the best performance with an accuracy of 93.24%, a precision of 0.92, a recall of 0.93, and an F1-score of 0.92. These values demonstrate the model’s ability to consistently recognize word patterns representing menu categories. The system also successfully generated menu template designs automatically based on the classification results. This research contributes to the application of data mining in the culinary sector and supports MSMEs in improving the effectiveness of menu management.
Pengembangan Sistem Deteksi Plagiarisme Dokumen Jurnal Berbasis Bidirectional Encoder Representations from Transformers Dan Cosine Similarity
The development of digital technology has had a significant impact across various fields, including education and the management of scientific documents. The ease of access to online journals has introduced a new challenge—an increase in the potential for plagiarism. To address this issue, an automated system capable of detecting document similarity quickly and accurately is required. This study aims to develop a plagiarism detection system based on Cosine Similarity and Bidirectional Encoder Representations from Transformers (BERT). The research stages include text preprocessing, word weighting using Term Frequency–Inverse Document Frequency (TF-IDF), Cosine Similarity computation, BERT model training, and model performance evaluation. The results show that integrating BERT with TF-IDF significantly improves performance compared to using BERT alone. Based on the experiments, the BERT model with TF-IDF achieved the highest accuracy of 0.9621 in a 10:90 data split scenario, with a precision of 0.8141, recall of 0.7302, and F1-score of 0.8022. Meanwhile, the BERT model without TF-IDF only achieved an accuracy of 0.8529. The application of Cosine Similarity with a threshold value of 0.6 also proved effective in identifying plagiarized and non-plagiarized documents. These findings demonstrate that combining BERT and TF-IDF enhances the accuracy of plagiarism detection systems by simultaneously capturing semantic context and word weighting.
Prediksi Harga Mobil Global Menggunakan Machine Learning dengan Algoritma Naive Bayes
Determining car prices is one of the major challenges in the global automotive industry because it is influenced by various factors such as technical specifications, vehicle condition, and market dynamics. This issue becomes more complex as the volume of available data increases, requiring methods capable of performing fast and accurate analysis. This study aims to predict car price levels based on vehicle specifications using a Machine Learning approach, with the Naive Bayes algorithm selected as a solution to simplify the price classification process on large-scale data. The dataset used is the Global Car Sales Analysis from the Kaggle platform, which includes attributes such as Manufacturer, Model, Engine size, Fuel type, Year of manufacture, Mileage, and Price. The research methodology consists of data preprocessing, label encoding for categorical attributes, splitting the dataset into training and testing sets, and applying the Naive Bayes algorithm to classify car prices into three categories: Low, Medium, and High. The results indicate that Naive Bayes is capable of predicting car prices with very strong performance, achieving an accuracy of 96%, precision of 0.97, recall of 0.96, and an F1-score of 0.96. The model performs best on the Low category with an F1-score of 0.98, although performance decreases for the Medium and High categories due to imbalanced class distribution. Further analysis also reveals that Engine size, Year of manufacture, and Mileage are the most influential attributes in determining price. Overall, this study demonstrates that Naive Bayes is an effective method for predicting car prices using global automotive data.
Sistem Pengelolaan Inventori Real-Time untuk UMKM Berbasis Flutter dan QR Code Menggunakan Metode R&D
The efficiency and accuracy of stock recording in MSMEs are enhanced through the development of a mobile-based inventory management application, addressing the challenges of manual systems, which are prone to human error and data delays. This study implements a real-time system by integrating three key technologies: Flutter as a cross-platform framework for efficient development, QR Codes for rapid item identification, and Firebase as a database for instant data synchronization. By applying the R&D method through the Waterfall model, the application was developed and tested in a case study at Warung Laras. The application is equipped with essential features for daily operations, including security verification using OTP via WhatsApp, unique QR code generation for each item, and the ability to automatically generate inventory reports in PDF format. The results from Black Box testing and user trials show a significant quantitative impact: data recording accuracy increased to 95%, while the average time per transaction was drastically reduced by 70%, from approximately 30 seconds to just 9 seconds. This finding proves that the technological integration has successfully created a reliable and effective solution to drive digital transformation in MSME-scale stock management
Pemodelan WebGIS untuk Prediksi dan Analisis Spasial Cakupan ASI Eksklusif di Kota Padang
Penelitian ini mengembangkan model Sistem Informasi Geografis (SIG) berbasis web untuk prediksi dan analisis spasial cakupan ASI eksklusif pada bayi di bawah enam bulan di Kota Padang. Model ini dirancang untuk mengintegrasikan pemetaan spasial dengan analisis prediktif sehingga dapat memberikan gambaran menyeluruh mengenai kondisi cakupan ASI eksklusif pada tingkat wilayah. Analisis spasial dilakukan dengan memanfaatkan data historis tahun 2023–2024 untuk memetakan distribusi cakupan pada tingkat kecamatan dan pusat pelayanan kesehatan. Hasil pemetaan ini menggambarkan variasi spasial antarwilayah dan memberikan informasi awal mengenai area dengan cakupan tinggi maupun rendah.Pendekatan Monte Carlo Simulation diterapkan untuk menghasilkan model prediksi berdasarkan data tahun 2024. Hasil simulasi menunjukkan tingkat akurasi sebesar 98,48%, yang mengindikasikan kesesuaian sangat tinggi dengan data aktual tahun 2024. Tingkat akurasi tersebut menunjukkan bahwa model memiliki kapabilitas yang baik dalam merepresentasikan kondisi nyata dan dapat diandalkan sebagai alat analisis prediktif untuk tahun-tahun berikutnya. Temuan ini juga menegaskan potensi metode probabilistik dalam mendukung analisis kesehatan masyarakat yang bersifat dinamis.Integrasi hasil prediksi ke dalam platform SIG berbasis web memungkinkan visualisasi interaktif dan akses data secara real-time. Kehadiran sistem ini diharapkan dapat meningkatkan efektivitas pengambilan keputusan dan perumusan kebijakan berbasis data dalam upaya peningkatan cakupan ASI eksklusif di Kota Padang, terutama bagi pemangku kepentingan di bidang kesehatan masyarakat. Kata Kunci: Analisis_Spasial; ASI_Eksklusif; Sistem_Informasi_Geografis_(SIG); Simulasi_Monte _Carlo; Kota_Padang
Optimasi Hyperparameter Algoritma Decision Tree dan Random Forest Menggunakan Particle Swarm Optimization Untuk Prediksi Risiko Obesitas Anak
Obesitas pada anak merupakan masalah kesehatan global yang mengalami peningkatan prevalensi signifikan di Indonesia dengan proyeksi mencapai 254 juta kasus pada tahun 2030. Penelitian ini bertujuan mengoptimasi model prediksi risiko obesitas anak menggunakan Particle Swarm Optimization (PSO) pada algoritma Decision Tree dan Random Forest untuk meningkatkan akurasi klasifikasi status gizi anak berdasarkan Permenkes No. 2 Tahun 2020. Metode penelitian menggunakan pendekatan ekperimental dengan dataset 64.506 anak dengan rentang usia 0-5 tahun dari Dinas Kesehatan Provinsi Gorontalo tahun 2024 yang kemudian di balancing menjadi 3.837 sampel. Optimasi PSO dilakukan dengan 40 partikel dan 80 iterasi untuk mencari hyperparameter optimal pada kedua algoritma. Hasil penelitian menunjukkan Decision Tree yang dioptimasi PSO menghasilkan akurasi terbaik sebesar 91.32% pada test set, meningkat 4.51% dari baseline, dengan precision 0.95, recall 0.95 dan F1-score 0.95. Random Forest teroptimasi mencapai akurasi 84.2%, meningkat 2.60% dari baseline. Model Decision Tree + PSO menunjukkan performa superior pada klasifikasi obesitas dengan precision 0.98 dan recall 0.96, serta berhasil mengurangi overfitting dari gap 3.47% menjadi 2.78%. model yang dikembangkan dapat diimplementasikan sebagai alat bantu deteksi dini risiko obesitas anak dalam layanan kesehatan masyarakat untuk mendukung pencapaian Indonesia Emas 2045.
Prediksi Harga Rumah Menggunakan Algoritma Regresi Linier,Random Forest, Dan Gradient Boosting
House price prediction is a crucial issue in the property sector because it is influenced by various interrelated factors, such as building characteristics and environmental conditions. Accurate prediction using conventional approaches is often difficult and can lead to errors in decision-making. Therefore, this study aims to develop and compare the performance of house price prediction models using three machine learning algorithms: Linear Regression, Random Forest, and Gradient Boosting. The dataset used is the Home Value Insights Dataset on Kaggle, which consists of 1,000 houses with eight main attributes. The research stages include data pre-processing, dividing training and test data, model training, parameter optimization using GridSearchCV, and performance evaluation based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) metrics using the 10-Fold Cross Validation method. The test results show that Linear Regression provides the best performance with an R² value of 0.8539 and a lower prediction error rate than Random Forest and Gradient Boosting. Although the ensemble model shows competitive results, increasing model complexity does not result in a significant increase in accuracy, so Linear Regression is considered the simplest, most efficient, and most easily interpreted approach for house price prediction systems on datasets with characteristics that tend to be linear.
Predicting AI Job Salary Classes Through a Comparative Study of Machine Learning Algorithms
The rapid growth of Artificial Intelligence (AI) has brought significant transformation to the global job market, particularly in salary structures across various AI-related professions. This study aims to classify AI job salaries into three categories—Low, Medium, and High—using supervised machine learning algorithms. The dataset, sourced from Kaggle, combines two real-world datasets featuring key attributes such as experience level, job type, education level, technical skills, remote work ratio, and salary in USD. Preprocessing techniques include One-Hot Encoding for categorical data, StandardScaler for normalization, and MultiLabelBinarizer to handle multi-skill entries. Four machine learning models—Logistic Regression, Random Forest, Gradient Boosting, and XGBoost—were trained and evaluated using consistent pipelines, with evaluation metrics including accuracy, precision, recall, and F1-score, applying macro-averaging to address class imbalance. Logistic Regression achieved the highest performance with 85.4% accuracy and 77.6% F1-score, followed by Gradient Boosting with 84.8% accuracy and 76.3% F1-score. High-salary classes were predicted with higher precision and recall than low-salary classes, indicating skewness in class distribution. Feature importance analysis shows that experience, remote work ratio, and key skills such as Python and SQL significantly affect prediction accuracy. This study demonstrates that traditional machine learning methods, when applied with appropriate preprocessing, can effectively support salary classification and labor market analysis in the AI domain.
Penerapan Arsitektur Deep Learning EfficientNetB0 Berbasis Citra Digital untuk Meningkatkan Kinerja Sistem Klasifikasi Sampah Organik, Anorganik, dan B3
Waste management in Indonesia remains a major challenge due to increasing waste volumes and the low efficiency of manual sorting processes at Landfills (TPA). This study aims to improve the performance of an automated waste classification system for three categories: organic, inorganic, and hazardous and toxic waste (B3) using deep learning-based computer vision technology. The proposed method is the EfficientNetB0 architecture with a transfer learning approach, whose performance is compared with four other pre-trained architectures (VGG-16, InceptionV3, MobileNetV2, and ResNet50). The dataset used consists of 7,003 valid images collected from public sources and manual acquisition after a data cleaning process. The dataset is divided into 70% as training data, 20% as validation data, and 10% as test data. Data augmentation and class balancing strategies are used to increase variation and overcome data imbalance between classes. Training is conducted in two stages: Feature Extraction and Fine-Tuning, with consistent hyperparameters for a fair comparison. Performance evaluation is performed using accuracy, precision, recall, and f1-score metrics. The test results show that EfficientNetB0 managed to achieve the best performance with an accuracy rate of 96.87%. Modern architectures like EfficientNetB0 have proven capable of extracting complex features with good computational efficiency, thereby holding the potential for use in AI-based automatic waste sorting systems to support more effective and sustainable waste management.
A Analisis Tingkat Kepuasan Konsumen Pada Pelayanan PT. AXZ Furniture Di Media Internet Menggunakan Metode VADER dan ARM
Bali Art Furniture is one of the companies in Bali that exports furniture and home decoration products. During its operation, this company utilises various internet media platform services such as websites, Facebook Marketplace, Instagram, WhatsApp Business, Pinterest, and Google to communicate online with consumers. As the company has grown over time, it has recruited many employees to increase its capacity to provide services. The company has never conducted a systematic evaluation of customer service satisfaction, either internally or externally. Based on this phenomenon, a customer satisfaction analysis was conducted using customer comment data. Customer service satisfaction was evaluated using the VADER and ARM methods as a basis for comparing the effectiveness of these methods. Based on the analysis of the two methods, the VADER method produced an accuracy of 34%, while the ARM method produced an accuracy of 64%. The evaluation results using the confusion matrix of the VADER model showed that positive comments were more recognisable by the system than negative and neutral comments, as seen from the positive recall value of 0.90, which was greater than the negative and neutral recall values. Meanwhile, the evaluation results using the ARM method showed that neutral comments were more recognisable by the system than positive and negative comments, as seen from the neutral recall value of 0.88, which was greater than the positive and negative recall values. Thus, the highest accuracy results in the ARM model became the guideline in making recommendation results.
Implementasi Model Gpt-3.5 Turbo Untuk Otomatisasi Penilaian Esai Pada Sistem Pembelajaran Daring
Essay assessment in online learning requires significant time, effort, and consistency, which can be challenging to maintain when conducted manually. This study explores the use of the large language model GPT-3.5 Turbo as the core of an automated essay scoring system for online learning platforms. Employing a Research and Development (R&D) approach with the ADDIE development model—comprising Analysis, Design, Development, Implementation, and Evaluation phases—the research adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework for its methodology. The automated essay scoring system utilizing Prompt 4 demonstrated exceptionally high accuracy and reliability. The model achieved an accuracy of 94.3%, an F1-Score of 0.955, and a Cohen’s Kappa value of 0.878. This high Kappa value indicates a very strong agreement between AI-generated assessments and the gold standard validated by educators, surpassing the initial inter-rater agreement among educators themselves, which was only 0.1157. The superior performance of Prompt 4 is also confirmed by the lowest Mean Absolute Error (MAE) of 30.54 and the highest Area Under the Curve (AUC) of 0.956.
Analisis Performa Algoritma Naïve Bayes dan SVM Menggunakan Python Pada Ulasan Sentimen Game Roblox
The imbalance of user reviews in the Roblox game creates accuracy challenges in sentiment classification, where the number of positive reviews significantly exceeds negative ones, causing the model to struggle particularly in identifying negative sentiment. This study aims to compare the performance of the Naïve Bayes and Support Vector Machine algorithms in classifying sentiment on imbalanced data. The research was conducted through several stages, including web scraping, pre-processing, automatic labeling using CNN, data splitting, model training, and performance evaluation using a Confusion Matrix. The findings reveal that Naïve Bayes tends to classify most samples as positive, resulting in very high recall for the positive class, reaching 0.995–0.997, but poor performance on the negative class, leading to consistent imbalance across all test ratios. In contrast, SVM achieves higher accuracy and more stable performance, with a Macro-F1 score of 0.740–0.769 and an AUC-PR of 0.936–0.942. The performance differences between the two models are statistically significant, with p-values of 0.001 and 0.0004, indicating that SVM is more effective in identifying both majority and minority classes. However, in terms of computational efficiency, Naïve Bayes is superior, requiring only 0.003–0.016 seconds of training time. Therefore, SVM is considered more reliable and robust for sentiment analysis on imbalanced data such as Roblox game reviews, whereas Naïve Bayes is more suitable when processing speed is the priority.
Eksplorasi Teknik Pre-Processing Berbasis eXtreme Gradient Boosting (XGBoost) pada Serangan DDoS
Distributed Denial of Service (DDoS) attacks represent a critical threat to modern network security, particularly within Internet of Things (IoT) environments characterized by large-scale and heterogeneous traffic patterns. The primary challenges in detecting such attacks involve class imbalance, irrelevant features, and noise within the data, all of which can degrade the performance of machine learning-based detection models. This study evaluates the impact of a pre-processing pipeline—comprising the Synthetic Minority Over-sampling Technique (SMOTE), correlation-based feature selection, and advanced feature selection methods—on the performance of the XGBoost algorithm in detecting DDoS attacks using the CIC-IoT2023 dataset. Experimental results indicate that the XGBoost model trained on RAW data achieves exceptionally high performance, with an accuracy of 0.999983, precision of 0.985531, recall of 0.961390, and an F1-score of 0.999983. However, after applying the pre-processing techniques, all metrics experienced a decline, with accuracy decreasing to 0.958899, precision to 0.865729, recall to 0.748332, and the F1-score to 0.959158. The reduction in recall suggests a higher number of undetected attacks, whereas the drop in precision indicates an increase in false alarms. Nevertheless, the F1-score remaining above 0.95 demonstrates that the model continues to perform effectively overall. These findings reveal that pre-processing does not always lead to performance improvements, especially when the raw dataset is already relatively clean and balanced. This study provides deeper insights into how SMOTE, feature selection, and noise injection influence the generalization of XGBoost on IoT traffic, and emphasizes that the effectiveness of pre-processing is highly dependent on dataset characteristics and the intended application context of intrusion detection systems.
Pengembangan Sistem Pendukung Keputusan Berbasis Machine Learning untuk Prediksi Kinerja Dosen Menggunakan Data Historis Evaluasi Pembelajaran
Lecturer performance evaluation is a crucial component in efforts to improve the quality of higher education. However, traditional evaluation methods still face various challenges, such as subjective assessments, a lack of consistent standards, and lengthy decision-making processes. These conditions highlight the need for a more measurable, accurate, and data-driven evaluation mechanism, particularly in the context of ongoing digital transformation. This study aims to design and develop a lecturer performance prediction system using a machine learning (ML) approach within a Decision Support System (DSS) framework. The research approach involves processing historical lecturer data covering aspects of Teaching (including student evaluation scores, instructional innovation, and attendance levels), Research (number of publications, H-index, and participation in academic conferences), Community Service, and other administrative activities. Predictive models were developed and compared using several machine learning algorithms, namely Random Forest, Support Vector Machine (SVM), Multilayer Perceptron (MLP), and XGBoost. Experimental results show that Random Forest achieved an accuracy of 88.0%, SVM 85.0%, and MLP 87.0%, while XGBoost demonstrated the best performance with an accuracy of 92.0%, precision of 91.0%, recall of 90.0%, and an F1-score of 91.0%. Based on these results, XGBoost was selected as the primary model for the DSS. In addition, the system is equipped with a rule-based module that generates follow-up recommendations based on the model’s prediction results. All system components are implemented in an interactive dashboard using the Streamlit framework, enabling users to input data, monitor prediction outcomes, and obtain decision recommendations in a fast and data-driven manner.
Development of a Smart Coffee Model Based on the Internet of Things (IoT) in West Java Province
Coffee cultivation in fertile regions such as Ciniru District, Kuningan Regency, is still predominantly carried out manually, lacking adequate technological support. However, coffee plants require intensive monitoring of environmental conditions to ensure optimal productivity and quality. The absence of technology adoption in farming practices results in inefficiencies and makes the cultivation process vulnerable to pests and climate-related disruptions. This study aims to develop a smart monitoring system for coffee plantations in Gunung Manik Village, utilizing an Internet of Things (IoT)-based Smart Coffee model. The system is designed to assist coffee farmers in Kuningan in improving yields and providing guidance for crop management through real-time monitoring of soil moisture, environmental temperature, and pest activity. An experimental method was employed in this research, where data is transmitted via radio frequency to a gateway and subsequently forwarded to middleware for further processing. The processed data is then visualized through a dashboard and a mobile-based application. Currently, the system is focused on a single variety of coffee plants. The results demonstrate that the system provides accurate data for irrigation and fertilization scheduling, as well as notifications regarding plant conditions, growth stages, and cultivation status. In conclusion, the coffee plant monitoring system offers a practical digital solution for farmers and is expected to enhance agricultural productivity through the integration of information technology.
Pengembangan Platform Digital Community-Based Tourism Menggunakan Model System Development Life Cycle untuk Pemberdayaan Masyarakat Wisata Bukit Lawang
This study aims to develop a digital platform based on Community-Based Tourism (CBT) to support community empowerment in the Bukit Lawang tourism area, North Sumatra. Despite its rich natural beauty and cultural values, Bukit Lawang’s tourism potential has not yet provided significant economic benefits to local communities due to low digital literacy and limited online promotion access. The research employs the Research and Development (R&D) method using the System Development Life Cycle (SDLC) model, which includes needs analysis, system design, development, testing, and user evaluation. The results show that the developed CBT digital platform successfully integrates destination information, community-based tourism services, as well as reservation and local product promotion systems. User testing indicates a satisfaction rate of 89%, with a 40% increase in community engagement in tourism promotion and management. Furthermore, the platform strengthens networks among local entrepreneurs, visitors, and tourism managers through an effective digital communication system. This research provides a practical contribution to technology-based community economic empowerment and serves as a replicable model for other sustainable tourism destinations in Indonesia.
Redesign Of BUMDes And MSMEs Marketplace Platform Using User Centered Design Method
This research centers on the redesign of Waroeng Kita, a digital marketplace platform developed to facilitate transactions for Village-Owned Enterprises (BUMDes) and Micro, Small, and Medium Enterprises (MSMEs) in rural Indonesia. An initial usability assessment conducted for this study revealed a low System Usability Scale (SUS) score of 49.16, classifying the platform as "Not Acceptable" and highlighting significant usability challenges. To address these issues, the study systematically applied the User-Centered Design (UCD) methodology, following an iterative process aligned with the ISO 9241-210:2019 standard. Across two design iterations , critical usability flaws, including a static cart, unclear input forms, and unfamiliar terminology —were systematically resolved based on direct user feedback. The final evaluation after the second iteration demonstrated a substantial improvement, with the average SUS score increasing to 70. This score elevates the system to a "Marginally Acceptable" status (Grade C), confirming that the UCD approach is highly effective for enhancing system usability, particularly for users in rural settings with limited digital literacy. The study provides practical insights for creating more inclusive digital solutions in similar socio-economic contexts.
Copyright (c) 2025 Muhammad Widya Tirta Cahyatama, Ariq Cahya Wardhana
Credit Card Fraud Detection Using Ensemble Variation: Logistic Regression, Support Vector Classifier and Random Forest
Credit card fraud is a significant threat in the financial industry, causing significant financial losses annually, posing a challenge to both businesses and the financial sector. This requires research and development to identify fraud models that significantly improve over time. The purpose of this research is to develop a machine learning (ML)-based credit card fraud detection system with an ensemble approach to address the challenges of imbalanced data in digital financial transactions. The method used includes four main stages: data collection; SMOTE; Hyperparameter Tuning; and model evaluation. The dataset used is from Kaggle Credit Card Fraud Detection, which has a very low fraud proportion (0.17%). The increase in data volume was carried out using SMOTE on the training data. Three main models (Logistic Regression, Support Vector Classifier, Random Forest) and ensembles (hard and soft voting) were tested with hyperparameter tuning for optimal results. Random Forest performed best with an F1-Score of 0.8482 and an ROC-AUC of 0.9684. This model was able to detect 84% of fraudulent transactions with high precision, surpassing other models in handling imbalanced data. The combined advantages of RF and SMOTE are effective for fraud detection which is relevant for real-time systems in the financial sector.
Perancangan Aplikasi e-Lades sebagai Sistem Pelayanan Administrasi Masyarakat Berbasis Website di Desa XYZ menggunakan Metode Rapid Application Development
Public administration services in Desa Amplas are still conducted manually, resulting in delays, lack of transparency. This study aims to design and develop the e-Lades (Electronic Village Service) web-based application to improve efficiency, effectiveness, and accountability of services. The development method used is Rapid Application Development (RAD), allowing fast iterations based on user feedback. The research stages include requirements analysis, system design, development, testing, implementation, and evaluation. The main features include online administrative document registration, population data management, application status tracking, and WhatsApp-based automatic notifications. Trial results show an increase in service processing speed by up to 60% compared to the manual system, and an 85% increase in community satisfaction. This application is expected to serve as a model for digitizing village administrative services that can be replicated in other areas
Deteksi Stres Berbasis Teks pada Dreaddit Menggunakan Fine Tuning DeBERTa-v3
Mental health has become an important issue in the digital era, as psychological expressions are increasingly reflected through social media posts such as Reddit. This study uses the publicly available Dreaddit dataset containing Reddit user texts labeled with stress categories. The objective is to compare two text-based stress detection approaches: fine-tuning the transformer model DeBERTa-v3 and the classical TF-IDF LinearSVC method. Both approaches are implemented as binary classification systems to automatically distinguish stress and non-stress texts. The research workflow includes data preprocessing, tokenization, model training, validation, and evaluation using Accuracy, Precision, Recall, F1-score, and AUROC metrics. DeBERTa-v3 is fine-tuned using contextual representations with a self-attention mechanism, while TF-IDF LinearSVC relies on statistical n-gram weighting. Experimental results show that DeBERTa-v3 achieves superior performance with an Accuracy of 0.830, Precision of 0.802, Recall of 0.889, F1-score of 0.843, and AUROC of 0.918. Meanwhile, TF-IDF LinearSVC obtains an Accuracy of 0.732, Precision of 0.722, Recall of 0.783, F1-score of 0.751, and AUROC of 0.817. The experiments were conducted with consistent training configurations, data splits, and evaluation procedures to ensure a fair comparison. The confusion matrix analysis indicates that DeBERTa-v3 produces fewer false positives and false negatives, demonstrating stronger capability in recognizing implicit stress expressions. These findings highlight the advantages of transformer-based models in capturing emotional and semantic context and indicate the potential for real-time deployment in social-media-based mental health monitoring systems.
Pemetaan Kepribadian RIASEC melalui Klasifikasi Multi-Task Fitur Grafologi Tulisan Tangan Menggunakan ResNeXt50
Personality analysis based on graphology—the study of handwriting characteristics to identify individual personality traits—is an approach that has been increasingly developed in the fields of psychometrics and artificial intelligence. This research proposes a method for mapping Holland’s personality types (RIASEC) through graphology-based handwriting analysis using a deep learning approach. Conventional personality assessments generally rely on self-assessment questionnaires, which are highly subjective. To address this limitation, this study develops a Convolutional Neural Network (CNN) model with a ResNeXt50 architecture based on multi-task learning to classify five graphological features: letter size, writing slant, word spacing, line spacing, and pen pressure. The dataset used in this study was obtained from the IAM Handwriting Database, consisting of 1,533 handwriting images. The data underwent preprocessing steps—including resizing, conversion to tensor format, and normalization—before being trained using a multi-head CNN model with cross-entropy loss for each graphological feature and the Adam optimizer for optimization. After the training process, the model was evaluated using a testing set that had never been used during the training or validation stages to objectively assess its generalization capability. The evaluation results indicate that the proposed model can classify graphological features with an average accuracy above 80% and map the classification results to RIASEC personality types with up to three dominant types. These findings indicate that the ResNeXt50-based multi-task learning approach has the potential to serve as a more objective, efficient, and applicable alternative method for personality assessment in the contexts of career development and education.
Perbandingan Kinerja Algoritma C4.5 dan Naive Bayes Dalam Klasifikasi Data Penjualan Buku PT. XYZ
Book sales data is an important component in supporting marketing strategies and managerial decision-making. The objective of this research is to evaluate and compare the effectiveness of the C4.5 and Naive Bayes in processing book sales data at PT. Sonpedia Publishing Indonesia. The dataset used consists of 299 book sales records, processed using RapidMiner software with two validation methods, namely Split Data (80:20) and 10-fold cross validation. Experimental results reveal that the C4.5 algorithm with the split data method obtained an accuracy 88.33%, precision 94.29%, recall 86.84%, and F-Score 90.41%. Using 10-Fold Cross Validation, the performance decreased with an accuracy 86.60%, precision of 92.53%, recall 85.64%, and F-Score 88,99%. In contrast, the Naïve Bayes algorithm demonstrated better and consistent performance. With the Split Data method (80:20), it obtained an accuracy 90.00%, precision 90.00%, recall 94.74%, and an F-Score 92.31%. Furthermore, its performance improved with 10-Fold Cross Validation, achieving an accuracy 91.29%, precision 92.63%, recall 93.62%, and F1-Score of 93.10%. These findings suggest that naive bayes produces more consistent and accurate classification results compared to C4.5. The research is intended to act as a guide for the development of book sales prediction systems that support the effetiveness and efficiency of bussiness decision making.