Mobile and Forensics

Performance Analysis of Random Forest Algorithm with Smote for Multi-Class Attack Detection

2025-10-01T12:46:45+00:00

The increasing sophistication of cyberattacks necessitates the development of detection systems capable of accurately identifying various threat types. Data imbalance within attack logs presents a substantial challenge that can undermine the effectiveness of detection models. This study introduces a multi-class cyberattack detection model employing the Random Forest algorithm, optimized through the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The innovative aspect of this research lies in integrating Random Forests and SMOTE to improve multi-class classification accuracy on local attack log datasets. This approach remains sparsely explored in academic research. The dataset consists of 3000 cyberattack logs from the Information Systems Bureau of Muhammadiyah University Purwokerto, spanning 10 cyberattack categories. The research process involved data collection, pre- processing, division, model training, and evaluation. Results indicate that the model achieved an average F1-macro score of 76% and a weighted average of 93%, with the " Threat Level Medium " feature identified as the most influential predictor. These findings suggest that the combination of Random Forest and SMOTE effectively enhances multi-class detection performance and presents promising prospects for log-based cybersecurity systems in educational and industrial environments.

Enhancing Early Diabetes Detection Using Tree-Based Machine Learning Algorithms with SMOTEENN Balancing

2025-10-01T12:53:19+00:00

Diabetes continues to be a critical global health issue, demanding accurate predictive systems to enable preventive interventions. Traditional diagnostic tests lack efficiency for large-scale early screening, which has led to growing interest in artificial intelligence solutions. This research proposed an effective methodology for diabetes classification based on tree-based algorithms enhanced with SMOTEENN balancing. The study employed the Kaggle Diabetes Prediction Dataset with 100,000 instances and eight medical and demographic features. Preprocessing steps included handling missing and duplicate values, encoding categorical variables, and scaling numerical attributes with Min-Max normalization. To address severe class imbalance, SMOTEENN was adopted, producing a cleaner and more balanced dataset. Model evaluation was performed using Stratified 5-Fold cross-validation on six classifiers: Decision Tree, Random Forest, Gradient Boosting, AdaBoost, XGBoost, and CatBoost. Experimental results indicated significant gains after balancing, with ensemble methods outperforming single-tree baselines. Random Forest delivered the best overall performance (98.93% accuracy, 98.96% F1-score, 99.16% recall, 99.94% AUC), followed by CatBoost and XGBoost with comparable results above 99% AUC. While Decision Tree benefited most from SMOTEENN in relative terms, it remained less competitive. Analysis of the importance of the analysis revealed HbA1c level and blood glucose level as dominant predictors, validating clinically meaningful learning. These findings suggest that integrating hybrid resampling with ensemble tree classifiers provides reliable and general predictions for diabetes risk. The approach holds promise for deployment in healthcare decision support systems.

Enhancing Offline Shopping Experiences With Real-Time Mobile Apps, Specifically in Batam City

2025-10-21T04:56:02+00:00

Information asymmetry in the offline retail market imposes substantial "search costs" on purchasing professionals, who frequently lack visibility into real-time product availability across physical stores. Existing solutions, such as generic store locators, fail to provide inventory context, while traditional e-commerce platforms are unable to meet immediate, same-day procurement needs due to logistical delays. This research addresses this gap by developing a Real-Time Location-Aware mobile artifact aimed at optimizing offline procurement efficiency in Batam City. Grounded in Design Science Research (DSR), the system employs a short-polling architecture implemented via Expo (React Native), Express.js, and PostgreSQL to ensure data freshness. Technical performance testing validated the system's "Near Real-Time" capabilities, achieving an average API response time of 180 ms and a stable synchronization interval of 5 seconds under 4G network conditions. Furthermore, a usability evaluation involving 40 purchasing professionals yielded an average System Usability Scale (SUS) score of 71.81, categorizing the application as "Good." These results empirically demonstrate that lightweight polling architectures can effectively mitigate cognitive load and search latency, offering a scalable software engineering solution for the "Offline-to-Offline" (O2O) retail sector.