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.
Analisis Prediksi Resiko Perceraian Menggunakan Algoritma Random Forest dengan Optimasi Hyperparameter Random Search
Divorce is a social problem that continues to increase and has negative impacts on the psychological, social, and economic conditions of individuals and families. This study aims to build a divorce risk prediction model using the Random Forest algorithm with hyperparameter optimization using the Random Search method. The dataset was obtained from the Kaggle platform with 170 samples and 54 psychological-behavioral attributes of couples. The research stages included data preprocessing, dataset splitting (80:20), baseline model development, hyperparameter optimization with Random Search, and evaluation using accuracy, precision, recall, and AUC-ROC metrics. The results showed that the model achieved 94.12% accuracy on the test data with 97% recall that minimizes false negative risk. Hyperparameter optimization successfully improved the model's internal stability with a cross-validation average of 98.57%, although the test accuracy was equivalent to the baseline model. A gap of 4.45% between validation and test accuracy indicates potential overfitting, which is common in small datasets. Feature importance analysis revealed five dominant psychological factors: willingness to compromise, effective communication, conflict resolution, alignment of life values, and forgiveness ability. This research contributes to the development of an early detection system for divorce risk based on machine learning and provides an empirical basis for more targeted counseling interventions
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Sistem Pakar Berbasis Web untuk Diagnosis Stunting pada Balita Menggunakan Metode Naïve Bayes
Stunting is a health problem caused by chronic malnutrition that affects children's physical growth and cognitive development. This condition has become a serious concern because it impacts the quality of human resources in the future. This study aims to develop an expert system for diagnosing Stunting using the Naïve Bayes method to assist healthcare workers in the early detection of at-risk toddlers. The research data were obtained from Posyandu in Babul Makmur District, Southeast Aceh Regency, consisting of 170 training data and 30 testing data. The system was developed using the Python programming language with the Flask framework and SQLite database. The input variables consisted of seven symptoms (G01–G07), including age, weight, height, gender, and other supporting factors. The testing results showed that the Naïve Bayes method achieved an accuracy of 86.66%, with 26 out of 30 test data correctly classified according to expert diagnoses. This system can be used as a decision-support tool for healthcare workers to accelerate diagnosis and improve the effectiveness of Stunting management, particularly in areas with limited healthcare resources.
Data Mining Dengan Pendekatan Multiple Linear Regression Untuk Prediksi Hasil Panen Padi
The rice agricultural sector plays an essential role in Asahan Regency, but current harvest predictions are still carried out conventionally and subjectively. This causes data inaccuracies that impact uncertainty in logistics planning and production policies by the local government. Therefore, this study aims to build a more accurate rice harvest prediction model to assist the Department of Agriculture of Asahan Regency in making strategic decisions. The research methodology used is data mining techniques by implementing the multiple linear regression method, utilizing historical data on land area and rainfall to predict harvest yields. The main results of this study indicate that the web-based prediction model designed is capable of performing valid calculations, producing a harvest projection for 2025 of 54,308.79 tons that aligns with mathematical model calculations. The implication of this research is that the relevant agencies have a reliable decision support tool for planning food security, irrigation systems, and fertilizer provision more efficiently, thereby minimizing errors caused by manual calculations
Analisis Faktor Keberhasilan Penjualan Kerajinan Tangan menggunakan Decision Tree dengan Optimasi Grid Search
This study is motivated by the limited ability of handicraft Micro, Small, and Medium Enterprises (MSMEs) to analyze the key factors influencing sales success on e-commerce platforms, despite the availability of historical transaction data. Previous studies generally applied classification algorithms without systematic hyperparameter optimization, potentially leading to suboptimal models and overfitting issues. To address this gap, this research proposes the implementation of a Decision Tree algorithm optimized using Grid search Cross-validation. The dataset was obtained from the Brazilian e-commerce platform (Olist Dataset), specifically the ‘artes’ category as a proxy for handicraft products, with an 80:20 split for training and testing data. The optimization process explored 576 parameter combinations to determine the best configuration. The optimized model achieved an accuracy of 97.61% with a simplified tree structure (max_depth=None), enhancing interpretability. Feature importance analysis product_height_cm as the most dominant factor (64.23%), followed by product_height_cm, product_width_cm, Freight_value, product_weight_g, and price. These findings demonstrate that the combination of Decision Tree and Grid search effectively produces an accurate and interpretable predictive model, providing strategic decision-making support for handicraft MSMEs in digital marketplaces.
Pemodelan Intrusion Detection System Menggunakan CNN-LSTM dengan Selective SMOTE Untuk Deteksi Serangan Pada Data Tidak Seimbang
An Intrusion Detection System (IDS) is a critical component in safeguarding network security against increasingly complex cyberattacks. One of the main challenges in developing machine learning-based IDS is data imbalance, which reduces the model’s ability to detect attacks, particularly in the minority class. This study proposes improving the performance of deep learning-based IDS using a hybrid CNN-LSTM architecture combined with the prevalence ratio-based Selective SMOTE method, which is an oversampling approach performed selectively based on the imbalance level of each class. The dataset used is NSL-KDD, with preprocessing steps including categorical feature encoding and numerical feature normalization. Evaluation was conducted by comparing the baseline CNN–LSTM model and the CNN-LSTM with Selective SMOTE using the metrics accuracy, precision, recall, specificity, and F1-score. Experimental results show that the baseline model achieved an accuracy of 0.9947 with a macro recall of 0.8080, while the application of Selective SMOTE improved the macro recall to 0.8929 and the F1-score to 0.8515, particularly for minority classes such as U2R and R2L. Although accuracy decreased slightly to 0.9946, the specificity remained high at 0.9981 with a low false positive rate. These results indicate that the Selective SMOTE method is effective in improving attack detection sensitivity without significantly degrading the overall performance of the IDS system.
Prediction of Burnout Syndrome Risk in University Students Using the C5.0 Algorithm
Burnout among university students is a serious issue that can reduce learning motivation, academic performance, and mental health. Approximately 25–30% of students experience burnout symptoms, which negatively affect concentration and academic productivity. Early detection is still limited due to the lack of accurate data analysis. This study aims to predict the risk level of student burnout using the C5.0 algorithm as a classification method capable of handling both categorical and numerical data. The research data were obtained from 306 students at Universitas Islam Negeri Sumatera Utara through an online questionnaire based on the Maslach Burnout Inventory–Student Survey (MBI-SS). The data were processed through cleaning, encoding, and splitting into training and testing sets using Python. The results show that the model achieves excellent classification performance, with an accuracy of 99.25% on the training set (precision 99.72%, recall 99.45%) and 97% on the testing set (precision 100%, recall 96%). The model also identifies the most influential attributes contributing to burnout, such as stress level and emotional exhaustion. The main contribution of this study is the development of an accurate and interpretable machine learning-based model for predicting student burnout risk. These findings provide practical implications for educational institutions in supporting early detection and designing data-driven preventive interventions, such as counseling services and stress management programs.
Clustering YouTube Comments on Mental Health in Indonesia Using the K-Means Algorithm
This study aims to analyze mental health expressions in Indonesian-language YouTube comments using a text mining approach and the K-Means clustering algorithm. The increasing use of social media as a platform for expressing psychological conditions has resulted in large volumes of unstructured textual data that are difficult to analyze manually. Therefore, this study applies text preprocessing techniques, including case folding, tokenization, stopword removal, and stemming, followed by Term Frequency–Inverse Document Frequency (TF-IDF) weighting to transform textual data into numerical representations. The clustering process is performed using the K-Means algorithm, and the optimal number of clusters is determined using the Elbow Method and Silhouette Coefficient. The results show that the optimal number of clusters is k = 3, with the highest Silhouette Coefficient value indicating good cluster quality. A total of 2,411 YouTube comments were successfully grouped into three clusters, representing different types of mental health expressions, namely complaint expressions, personal experience narratives, and general responses. This study contributes by providing a social media comment clustering model to analyze mental health expressions in the Indonesian digital context. The results demonstrate that the K-Means algorithm can effectively identify meaningful patterns in large-scale textual data without requiring labeled datasets, making it useful for supporting data-driven mental health analysis.
Analisis Perbandingan Pelabelan Inset Lexicon dan MBERT pada Sentimen Danantara Menggunakan SVM dengan Kernel Trick
This study aims to compare the Inset Lexicon and MBERT sentiment labeling methods for analyzing sentiment related to the Danantara issue using a Support Vector Machine (SVM) with Linear, RBF, Polynomial, and Sigmoid kernels. The main issues in this study are the suboptimal sentiment labeling methods for Indonesian-language data that can accurately capture linguistic context, as well as the uncertainty regarding the best labeling method when combined with various TF-IDF-based SVM kernels. Model evaluation uses metrics such as Accuracy, Precision, Recall, F1-score, and Cross-validation (CV). The results show that the Inset Lexicon labeling method with a Linear kernel yields the highest Accuracy of 81% with a CV of 78%, Precision of 87%, and Recall of 92%. The RBF kernel achieved an Accuracy of 78% with a CV of 76%, followed by the Sigmoid kernel at 79% Accuracy with a CV of 76%, and the Polynomial kernel at 65% Accuracy with a CV of 65%. The highest F1-score for the negative class using the Linear kernel reached 89%. Meanwhile, in MBERT labeling, the highest accuracy was achieved by the RBF kernel at 72% with a CV of 71%, followed by the Linear kernel with an accuracy of 71%, then the Polynomial kernel with an accuracy of 68%, and the Sigmoid kernel with an accuracy of 67%. The highest F1-score was found in the negative class at 79% using the RBF kernel. Overall, the negative class showed the most consistent performance, while the neutral class had the lowest Recall and F1-score values for almost all kernel types. These findings confirm that an in-depth comparative analysis between lexicon-based and deep learning-based approaches demonstrates that methods such as the Inset Lexicon can deliver better and more stable performance on Indonesian-language data.
Analisis Performa Class Weight Dan Focal Loss Pada Model Indobert Untuk Klasifikasi Teks Depresi Berbahasa Indonesia
The development of depression detection can be done using exploration of social media content. However, the classification of depression indicative texts faces a major challenge in the form of class distribution imbalances, which can degrade the model's generalization capabilities. This study aims to analyze how the method of overcoming class imbalance affects the performance of the IndoBERT model in the classification of Indonesian depression indication texts by emphasizing the analysis of training stability based on the dynamics of training loss and validation loss. The dataset used consists of 3,863 data, data that has gone through the process of cleaning, removing duplicate data, tokenization, encoding, and dividing data into stratification into training data, validation data, and test data. The IndoBERT-base-p1 model was fine-tuned using three training scenarios, namely baseline, class weight, and focal loss with an early stopping mechanism based on validation loss. The test results showed that the baseline IndoBERT scenario produced an accuracy of 77.52%, a weighted precision of 0.7752, a weighted recall of 0.7752, a weighted F1-score of 0.7737, and a ROC-AUC of 0.8528 with a relatively stable training pattern. The class weight method produced an accuracy of 74.68%, a weighted F1-score of 0.7467, and a ROC-AUC of 0.8342 which showed an increase in class discrimination ability but accompanied by a decrease in overall accuracy. Meanwhile, the focal loss method produced an accuracy of 72.87%, a weighted F1-score of 0.7291, and a ROC-AUC of 0.8188 with more balanced training characteristics than the weight class. The findings suggest that handling classroom imbalances does not necessarily improve global performance, so model evaluations need to consider a balance between accuracy, sensitivity, and stability of training.
Penerapan Algoritma C4.5 dalam Klasifikasi Tingkat Konsumsi Harian Masyarakat untuk Mengurangi Food Waste pada UMKM di Kota Medan
This study addresses the issue of inaccurate production forecasting among breakfast-serving SMEs in Medan due to fluctuations in demand, which often result in food waste and financial losses. The objective of this study is to develop a classification model for daily consumption levels (high, medium, low) to serve as the basis for production recommendations. The method used is the C4.5 Decision Tree algorithm with a dataset of 92 daily operational records covering the attributes of production catesgory, menu type, weather conditions, and operational days. The data was preprocessed through attribute categorization, then analyzed using entropy and gain ratio calculations to form a decision tree. Model evaluation was performed using 10-fold cross-validation in RapidMiner. The results showed that the production category attribute had the highest gain ratio, making it the root of the decision tree. The resulting model achieved an accuracy of 57.56% with a deviation of ±7.85%, performing best in the moderate consumption class. The primary contribution of this study is the generation of IF–THEN-based decision rules that can be practically applied by SMEs to adjust daily production volumes based on operational conditions, thereby helping to reduce potential food waste without requiring complex calculations.
Implementasi Sistem Deteksi Visual Cacat Pengelasan Menggunakan Metode Image Processing Berbasis Raspberry Pi
This study develops a visual welding defect detection system based on Raspberry Pi by utilizing Gray Level Co-occurrence Matrix (GLCM), Histogram of Oriented Gradients (HOG), Principal Component Analysis (PCA), and Support Vector Machine (SVM) methods to identify various types of welding defects, including porosity, undercut, burn-through, overlap, and spatter. The system is designed to operate automatically and in real-time through several processing stages, including image acquisition, preprocessing to enhance image quality, Region of Interest (ROI) segmentation, feature extraction of texture and shape, dimensionality reduction using PCA, and multiclass classification using SVM. In addition, this study aims to evaluate the effect of image acquisition conditions on system performance, particularly variations in lighting, distance, and camera angle, which are critical factors in industrial implementation. Experiments were conducted under several scenarios to determine the optimal parameters that yield the best performance. The results show that the optimal condition is achieved at a lighting level of 50 lux, a camera distance of 10 cm, and a viewing angle of 20°. Under these conditions, the system achieves an accuracy of 100% for normal part classification and 94.4% for multiclass classification. The precision and recall values both reach 94%, with an F1-score of 93%, indicating a balanced performance in detecting different types of welding defects. Overall, the results demonstrate that the proposed system has strong potential as an effective, efficient, and real-time automated inspection solution for welding quality in industrial manufacturing environments.
Algoritma Apriori dan Visualisasi Heatmap GIS untuk Evaluasi Ketimpangan Distribusi Bantuan Sosial
This study integrates the Apriori algorithm and local spatial analytics to assess inequality in social assistance distribution in Gorontalo Province, Indonesia, covering six regencies/cities. The administrative Beneficiary Master List (BNBA) dataset was standardized for association rule mining to identify co-beneficiary patterns across major social assistance schemes, namely PKH, BPNT, BST, and BPUM. In parallel, the data were aggregated at the sub-district level to construct an inequality score based on Principal Component Analysis (PCA) of beneficiary proportions, which was then analyzed using Local Moran’s I (LISA) and Getis–Ord Gi*. The Apriori analysis of the province-wide dataset produced 64 association rules, 74 frequent itemsets, and 38 unique items. The results indicate strong co-beneficiary relationships among BPNT, BST, BPUM, and PKH, with confidence values ranging from approximately 0.60 to 0.95 and lift values exceeding 10. Spatial analysis shows that five of the six regencies/cities exhibit significant positive spatial autocorrelation (p < 0.10), with particularly strong clustering in Pohuwato (I = 0.9681) and North Gorontalo (I = 0.8331), while Gorontalo Regency shows no statistically significant pattern. LISA cluster maps further identify high-high (HH) clusters in parts of Boalemo and North Gorontalo, as well as low-low (LL) and high-low (HL) areas relevant for policy refinement. These findings suggest that integrating Apriori and local spatial analytics provides an effective operational approach for improving targeting accuracy, reducing overlap in assistance allocation, and identifying areas at risk of under-coverage.
Evaluasi Situs Web dengan Metode Cognitive Walkthrough, Heuristic Evaluation, dan User Experience Questionnaire
The Bali High Prosecutor’s Office website, a legal service channel in Bali, has not been evaluated since 2018. A preliminary study revealed several user complaints regarding malfunctioning crucial features, such as confusing workflows and unclear guidance. This can be hindering public access to the legal process in seeking justice. To this purpose, the website’s features were evaluated through research using cognitive walkthrough, heuristic evaluation, and User Experience Questionnaire (UEQ). The evaluation process involved 10 general users to measure task scenario performance, 3 experts to identify qualitative issues, and 23 questionnaire respondents to measure user experience perceptions. The evaluation was conducted in three stages: initial design evaluation, prototype development, and final design evaluation. The initial design usability test revealed significant issues. There were numerous failures in executing task scenarios in the public complaints feature, with an average severity rating of 3,27, which is in the critical category. Furthermore, clarity and accuracy were rated below average, with 50% of the products in the dataset performing worse than the Bali High Prosecutor’s Office website. This research aims to produce evidence-based interface design recommendations capable of improving the digital service quality of the Bali High Prosecutor’s Office. The contribution of this research lies in the development and validation of a high-fidelity prototype, which was evaluated using UEQ and demonstrated significant improvement across all aspects of user experience: attractiveness, clarity, efficiency, accuracy, stimulation, and novelty. This research provides a comprehensive evaluation framework adoptable by other government agencies seeking to enhance their digital public services
Evaluasi Efektivitas Firewall Pre-filtering berbasis eBPF/XDP menggunakan Random Forest untuk Deteksi Anomali Trafik pada Docker Swarm
Overlay networks in container orchestration platforms such as Docker Swarm are vulnerable to volumetric DDoS attacks, while conventional firewall solutions impose high overhead when processing large attack volumes. This paper presents the implementation and evaluation of an eBPF/XDP-based pre-filtering firewall that integrates detection rules derived from a Random Forest model to identify traffic anomalies in Docker Swarm overlay networks. Unlike previous studies that employ a single Decision Tree or process classification in user-space, this research extracts Random Forest rules into per-source-IP thresholds executed directly in the kernel via XDP and stored in an eBPF config_map to enable runtime updates without recompilation. The model was trained on the CIC-DDoS-2019 dataset (174,221 records, 65 features), achieving 99.88% accuracy, 99.90% detection rate, 0.14% false positive rate, and ROC-AUC of 0.9999. Experimental evaluation across seven testing scenarios with 10 iterations demonstrates that the XDP firewall drops over 99.9% of attack packets with a median response time of 0.69 ms, comparable to baseline conditions. CPU overhead remains low (0.92–1.18%) and throughput is maintained at approximately 920 Mbps. Differences between scenarios are statistically significant (p < 0.05) but with negligible practical effect (d < 0.25). Comparative analysis with iptables, both global rate limiting and per-IP hashlimit, indicates that all three approaches (XDP, global iptables, and per-IP iptables) effectively mitigate DDoS with comparable median response times.
Impementasi Metode SAW pada Sistem Pendukung Keputusan untuk Penentuan Prioritas Pendampingan Bayi Dibawah Dua Tahun di Kota Kediri
Determining the priority of assistance for children under two years old (Baduta) is a crucial step in accelerating stunting reduction programs. However, in practice, this process is still conducted manually, leading to potential subjectivity and inaccurate targeting. This study aims to implement the Simple Additive Weighting (SAW) method within a Decision Support System to determine the priority of Baduta assistance in Kediri City in an objective and systematic manner. The dataset consists of 300 Baduta, evaluated based on criteria including body weight, height, attendance at community health posts, and access to health service referrals. Preliminary results indicate that the normalization process successfully transformed the data into a standardized scale (0–1), enabling proportional comparison across all criteria. The weighting process shows that height and body weight have dominant contributions to the preference value calculation. The final results demonstrate that the system produces preference values ranging from 0.46 to 0.83, where lower values indicate higher priority for assistance. Furthermore, the system successfully identifies the top 10 priority Baduta as the primary targets for intervention. The implementation of the system also improves decision-making efficiency compared to manual methods and produces more consistent and objective rankings. The main contribution of this study lies in the integration of the SAW method into a dashboard-based system to support more accurate and measurable decision-making for prioritizing Baduta assistance.
Integrasi Faktor Iklim dan Lingkungan untuk Prediksi Risiko DBD di Kota Palembang Menggunakan Pendekatan GeoAI Berbasis LSTM
Dengue Hemorrhagic Fever (DHF) remains a significant vector-borne disease threat to public health in Palembang. This study aims to analyze the environmental and demographic factors influencing DHF risk and predict risk trends using a GeoAI approach. Four primary variables land surface temperature, rainfall, population density, and residential area were integrated to develop a DHF risk index for the 2020 - 2025 period. The analysis reveals that the risk index consistently falls within the high category across all regions, showing a gradual upward trend from 0.517 in 2020 to 0.527 in 2025. To project future risks, a Long Short-Term Memory (LSTM) model was employed. Model evaluation demonstrated robust performance with a Mean Squared Error (MSE) of 0.0028, a Root Mean Squared Error (RMSE) of 0.052, and a Mean Absolute Error (MAE) of 0.031, indicating low error rates and stable predictive capability. Prediction results suggest that DHF risk is expected to continue increasing through 2029, particularly in sub-districts with high population density and expanding residential areas. This research provides a scientific contribution by developing a predictive model that is more adaptive and precise than conventional statistical approaches. Through the integration of artificial intelligence and spatial data (GeoAI), this model effectively captures non-linear patterns and spatio temporal dynamics, serving as a sustainable early warning system.
Implementasi GIS Dan Metode MOORA Dalam Pemetaan Dan Pemilihan Lokasi Terbaik Penanaman Pohon
Amid rapid technological advancement, the use of digital systems such as Geographic Information Systems (GIS) plays a crucial role in enhancing the effectiveness of environmental conservation programs by enabling spatial analysis and efficient determination of suitable tree-planting locations. KSE UINSU (Karya Salemba Empat, State Islamic University of North Sumatra) annually conducts tree-planting activities involving hundreds of students across various regions in Indonesia, particularly in areas requiring reforestation, such as degraded land, flood-prone zones, coastal abrasion areas, and landslide-prone regions, including the City of Medan and Deli Serdang Regency. However, previous practices in location selection were often carried out without adequate data, resulting in time-consuming field verification and uncertainty regarding land suitability, soil conditions, accessibility, and regional characteristics. Therefore, a more systematic and data-driven approach is required to support faster, more accurate, and measurable decision-making. This study adopts a quantitative approach by integrating Geographic Information Systems (GIS) and the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method to identify the most suitable locations for tree planting in the City of Medan and Deli Serdang Regency
Implementasi BERT dan IndoRoBERTa untuk Klasifikasi Sentimen Opini Publik tentang Kecerdasan Buatan dalam Pendidikan di YouTube
This study aims to analyze the sentiment of Indonesian-language YouTube comments related to artificial intelligence (AI) in the field of education using a Transformer-based deep learning approach, namely Bidirectional Encoder Representations from Transformers (BERT) and IndoRoBERTa. The research data were obtained through the YouTube Data API, consisting of 10,834 comments reflecting public opinion on the implementation of AI in education. The dataset was manually labeled into three sentiment categories: positive, neutral, and negative, followed by a preprocessing stage including case folding, text cleaning, text normalization, tokenization, and topic filtering. The experimental results show that in the baseline scenario without fine-tuning, both models achieved low performance with accuracy below 41%. However, after fine-tuning, a significant improvement was observed, where IndoRoBERTa achieved an accuracy of 91.54% with an F1-score of 0.9134, while BERT reached an accuracy of 84.63% with an F1-score of 0.8413. These results indicate that Transformer-based models adapted to specific datasets are capable of better capturing the contextual and linguistic characteristics of informal and unstructured Indonesian text. In addition, IndoRoBERTa demonstrates more stable performance in handling class imbalance compared to BERT. Overall, this study demonstrates that Transformer-based approaches are effective for sentiment analysis in social media and can be used to more accurately and comprehensively understand public perceptions of the implementation of artificial intelligence in education.
Analisis Perbandingan Model Machine Learning menggunakan Teknik Stratified K-Fold Cross Validation untuk Klasifikasi Penyakit Jantung
Heart disease is one of the leading causes of death worldwide. Conventional approaches still have limitations, such as subjectivity in interpretation and relatively long analysis times. Therefore, this study proposes using machine learning to improve the accuracy of heart disease risk prediction by comparing the performance of Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms. The research methodology includes data preprocessing, splitting the dataset into training and testing sets, and hyperparameter optimization using Stratified K-Fold Cross Validation with variations of K = 5, 10, 15, and 20. Model evaluation is conducted using accuracy, precision, recall, F1-score, and ROC-AUC metrics to comprehensively and objectively measure classification performance. The results show that the Random Forest algorithm achieves the best performance. At the optimal configuration of K = 15, the model attains an accuracy of 93.17%, a precision of 0.92, a recall of 0.95, an F1-score of 0.94, and an ROC-AUC of 0.97. In addition, this model minimizes classification errors, particularly False Negatives, making it more effective at identifying at-risk patients. The main contribution of this study is demonstrating that the combination of Random Forest and Stratified K-Fold Cross Validation can significantly improve classification performance and produce a model that is accurate, stable, and reliable for implementation in medical decision support systems.
Analisis Sentimen Kritik Dan Saran Peserta Pelatihan Menggunakan Algoritma Naïve Bayes Dan Support Vector Machine
Participant feedback in the form of comments, criticisms, and suggestions is generally unstructured text data, which has not been optimally utilized as supporting information for training evaluation. This study aims to classify the sentiment of training participants’ feedback into positive, negative, and neutral categories using a machine learning approach, as well as to compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms. In addition, this study examines the effect of hyperparameter optimization on the performance of sentiment analysis models. The research methodology includes text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), sentiment classification modeling, and performance evaluation using accuracy, precision, recall, and F1-score metrics. Model optimization is conducted through hyperparameter tuning using Grid Search and Random Search methods. The results show that, out of 487 participant feedback comments, the sentiment distribution is dominated by positive sentiment. Model evaluation indicates that the Support Vector Machine algorithm consistently achieves higher accuracy than Naïve Bayes, with the highest accuracy reaching 79.0%, while Naïve Bayes achieves a maximum accuracy of 65.3%. Furthermore, hyperparameter optimization is shown to improve the performance of both algorithms, particularly for Naïve Bayes. However, the findings are descriptive in nature and are intended to complement, rather than replace, existing survey-based methods or training management evaluation processes.
Brain Tumor Detection and Classification from MRI Images Using a Convolutional Neural Network Approach
Brain tumors are a serious neurological disease that require rapid and accurate diagnosis to improve treatment success. However, conventional interpretation of brain MRI images is often time-consuming and highly dependent on radiologists’ expertise, which may lead to diagnostic inconsistency. This study aims to develop a brain tumor detection and classification model from MRI images using a Convolutional Neural Network (CNN) approach. The dataset consists of four classes, namely glioma, meningioma, pituitary, and no tumor. The research stages include data collection, image preprocessing, model training, and evaluation using accuracy, loss, precision, recall, and F1-score. The results show that the CNN model achieved a training accuracy of 1.0000 at the final epoch, while the testing phase produced an accuracy of 58.75% with a loss value of 1.9600. These findings indicate that the model was able to learn important patterns from MRI images, although the gap between training and testing performance suggests overfitting. This study contributes to the development of AI-based medical image classification for brain tumor identification and shows that CNN has potential as a supportive tool for assisting medical personnel in brain tumor diagnosis. Further improvements can be achieved through data augmentation, hyperparameter tuning, and optimization of model architecture.
Pendeteksian Dini pada Potensi Banjir dengan Berbasis Internet of Things Menggunakan Algoritma Random Forest
Flood is a natural disaster that frequently occurs and causes significant material and social losses. Conventional flood monitoring systems are generally reactive and rely on threshold-based approaches, which limits their effectiveness in supporting early detection. This study proposes an Internet of Things (IoT)-based early flood detection system using the Random Forest algorithm. The developed system collects hydrological data, including water level and flow velocity, through IoT sensors that periodically transmit data to a server for further processing. The collected data are then aggregated and classified into flood and non-flood conditions using a Random Forest model. Model performance is evaluated using accuracy, precision, recall, f1-score, confusion matrix, and 5-fold cross-validation. Experimental results indicate that the proposed model achieves an accuracy of 97.26% with a mean cross-validation score of 0.9863. However, the recall for flood events remains limited due to data imbalance and the relatively small number of flood occurrences. Despite these limitations, the proposed system demonstrates potential to support the development of early flood warning systems and can be further improved by incorporating longer and more diverse historical datasets.
Metode Mobile-D untuk Pengembangan Aplikasi Sales Performance Management Berbasis Real-Time Geotagging dan Area Mapping
Sales often work outside the office to visit clients, hold meetings, and conduct surveys, making their activities difficult to monitor directly. This situation can lead to decreased productivity, missed targets, and reduced company profits. Sales activity management plays an important role in improving the effectiveness of a company’s marketing strategy. However, monitoring sales activities often faces several challenges due to manual recording processes and the lack of accurate location-based information. This study aims to develop a mobile-based Sales Performance Management system that utilizes geotagging and area mapping technologies to support more effective monitoring of sales activities. The system development adopts the Mobile-D method, which emphasizes an iterative and adaptive approach to mobile application development. The proposed system enables sales personnel to record customer visit activities through a mobile device equipped with a geotagging feature integrated with Google Maps services. The collected location data are then visualized through area mapping to provide a clearer overview of sales activity distribution. This functionality allows managers to monitor field activities and evaluate sales performance more efficiently. The implementation results indicate that the system is documenting sales activities accurately while supporting the validation process conducted by managers. In addition, the mapping feature provides useful insights into the spatial distribution of sales activities across different operational areas. Therefore, the developed system can improve the effectiveness of monitoring processes and managing sales personnel performance more efficiently.
Pengaruh Inkonsistensi Switching Light-Dark Mode Antar Aplikasi Terhadap Mental Workload dan Kinerja Pengguna Smartphone
Visual inconsistency in user interface contexts, such as visual theme changes during mode switching (from Light to Dark Mode), is often considered an optional aesthetic element; however, its impact on user cognitive load and performance has not been widely researched. This study aims to analyze the effect of mode switching on subjective cognitive load, task completion time efficiency, and user performance accuracy in an interface interaction context. The method used in this experiment was a Quasi-Experimental Design with a posttest-only control group type, involving 40 respondents divided into two groups: static theme condition and mode switching condition. Subjective cognitive load was measured using the NASA-TLX instrument, while objective performance was evaluated through Task Completion Time and Error Rate. The results of this study indicate that the treatment group experienced an increase in cognitive load, with an average WWL score of 55.23, compared to the control group at 42.50. The frustration dimension was the highest, at 28.0, indicating emotional pressure due to interface inconsistency. Objectively, the application of mode switching slowed down task completion time by 3.65 seconds and increased the error rate by up to two times. Statistical test results also showed differences in all research variables, with a p-value < 0.005 and an effect size value on cognitive load of 0.816 (Large Effect). These findings lead to the conclusion that visual stability is an important factor in interface design, necessary for maintaining navigation efficiency, increasing user comfort, and minimizing interaction errors.
Sistem Pendukung Keputusan Berdasarkan Profil Keuangan Pribadi di Aplikasi SiKencur
Many individuals struggle to manage personal finances due to a lack of tools that provide advice tailored to their actual spending habits. This study develops and evaluates a Decision Support System (DSS) module within a mobile personal finance application called SiKencur. The system classifies users into four financial behavior profiles based on three transaction indicators computed over a six-month window. Testing with 30 users over four weeks yielded a classification accuracy of 82.6%, a usability score of 78.4 out of 100 (“Good” category), and a user satisfaction rating of 4.2 out of 5. The system's key advantage is the integration of personalized financial guidance directly within a single platform a capability not found in comparable applications.
Predict Goods Demand Using the XGBoost Method Based on Sales Historical Data
Predicting the demand for goods is an important aspect of inventory management and operational planning because inaccurate predictions can lead to overstock or shortages of goods. This study aims to predict the demand for goods using the Extreme Gradient Boosting (XGBoost) algorithm based on historical sales data. The dataset used contains information on the transaction date, number of sales, stock, price, and time index, which is then processed through the preprocessing and feature engineering stages, including the formation of temporal features and sales lag features. Data sharing is carried out using a time series split approach to maintain the chronological order of the data. The XGBoost model is optimized using GridSearchCV with the TimeSeriesSplit validation scheme. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE). The results showed that the model produced an MAE score of 54.13 and an RMSE of 77.60, while a SMAPE score of 43.13% showed an acceptable relative error rate in highly fluctuating sales data. Feature importance analysis shows that previous period (lag_1) sales and weekly patterns are the most dominant factors in demand predictions. These results prove that XGBoost is effectively used for historical data-driven demand prediction of goods and has the potential to support inventory management decision-making.
Pendekatan Machine Learning Berbasis Fitur Geospasial Imputasi Nilai AADT yang Hilang: Studi Kasus Texas
Annual Average Daily Traffic (AADT) is an important indicator in transportation planning and road network performance evaluation. However, missing values ​​due to sensor interference or unrecorded data can reduce the quality of the analysis. This study aims to estimate missing AADT values ​​using a geospatial feature-based machine learning approach with a case study in Texas, United States. Automatic Traffic Recorder (ATR) data is integrated with road network attributes from OpenStreetMap (OSM) through a spatial join process to produce features such as road classification, number of lanes, and speed limits. A Random Forest model is used to build an estimation model based on valid data (AADT > 0). The evaluation results show a coefficient of determination (R²) of 0.548, indicating that geospatial features can significantly explain variations in AADT. The imputation process successfully produced a dataset with a 100% convenience level and a spatial distribution pattern consistent with the road network hierarchy and metropolitan area. This approach demonstrates that the integration of spatial data and machine learning is effective in improving the integrity of traffic data to support data-driven decision making.
Sistem Deteksi Dan Monitoring Jendela Rumah Berbasis Sensor Magnetik Dengan Logika Fuzzy Mamdani
Home security is often disrupted by false alarms because conventional systems rely solely on binary logic that does not consider the context of time and the magnitude of window opening. This study designs and implements an Internet of Things (IoT)-based window detection and monitoring system that integrates an MC 38 magnetic sensor and a HY SRF05 ultrasonic sensor, with inference processing using Mamdani Fuzzy Logic on a NodeMCU ESP8266 microcontroller. The system is equipped with a DS3231 RTC module and an NTP synchronization mechanism to maintain timeliness, and provides adaptive responses through LED indicators, buzzer sound patterns, Telegram notifications, and a Flutter-based mobile application. The research objective is to produce contextual alarm decisions (Safe, Alert, Danger) to reduce false alarms without sacrificing response speed. The main contribution is the implementation of a time-aware multi-sensor approach and edge processing so that the system is able to assess the level of urgency based on the physical status of the window, the distance of damage, and the time of the incident. Testing was carried out in tightly closed scenarios, small edits during the day, wide edits at night, and disturbances due to wind or vibration. Test results showed a resolution accuracy of 93.85%, an average ultrasonic measurement error of 0.63% (a difference of <0.5 cm at the test distance), and an average notification latency to the app and Telegram of around 5 seconds. These findings demonstrate that the integration of redundant sensors with fuzzy inference improves intrusion detection evidence in smart home windows
Implementasi Algoritma Jaringan Saraf Tiruan untuk Prediksi Hasil Panen Kakao Desa Minanga
Cocoa production in Desa Minanga exhibits unstable fluctuations, which directly impact the uncertainty of farmers' incomes and pose challenges in financial planning. This study aims to apply the Backpropagation Artificial Neural Network (ANN) algorithm as an instrument for predicting cocoa yields. The research data covers the period from 2019 to 2023, comprising a total of 2,980 data points. The predictive model was developed using eight main criteria: land area, number of plants, seed type, fertilizer type, pest/disease attacks, mitigation efforts, rainfall levels, and previous harvest yields. Testing was conducted using three training-to-testing data ratio scenarios: 70:30, 80:20, and 90:10. These variations were used to evaluate the model's stability and performance in identifying data patterns. Furthermore, comprehensive testing was performed on various network architecture parameters, learning rates, and target errors, utilizing the binary sigmoid activation function to assess the model's stability and accuracy in recognizing complex data patterns. The research results indicate that the optimal model configuration was achieved with a 90:10 data ratio, a 7-6-1 network architecture, a learning rate of 0.4, and a target error of 0.0001. This model achieved an accuracy rate of 98.09% with a Mean Absolute Percentage Error (MAPE) of 1.91%. The findings demonstrate that the Backpropagation ANN is effective and can serve as an alternative method for predicting cocoa yields in Desa Minanga.