Journal of Computer System and Informatics (JoSYC)

Analisis Ketidakseimbangan Tegangan Baterai dengan Pendekatan Random Forest, K Nearest Neighbors untuk Prediksi Balancing Charger

2025-09-12T12:43:30+07:00

Inter-cell voltage imbalance degrades efficiency, accelerates aging, and increases failure risk in electrochemical energy storage systems. This study models and predicts balancing-charger conditions using two machine-learning algorithms Random Forest (RF) and K-Nearest Neighbors (KNN) across packs of 4, 8, 10, and 15 cells with five dataset scales (1,000; 5,000; 10,000; 15,000; and 20,000 samples). Voltage data were obtained through simulation and laboratory measurements on lithium-ion cells within 3.2–4.2 V, then normalized and split into training and testing sets. Performance was evaluated using accuracy, confusion matrices, and feature-importance analysis. Results show RF achieves 0.98 accuracy for 4-cell packs and remains high at 0.93 for 15-cell packs, whereas KNN attains only 0.94 and 0.37 on the same configurations. RF exhibits predictions concentrated along the confusion-matrix diagonal with well-distributed feature weights, indicating robustness to increasing dimensionality. The contributions are threefold: (1) an evaluation framework for comparing classifiers in multi-cell scenarios; (2) empirical evidence of RF’s scalability for detecting balancing conditions from single-voltage inputs; and (3) practical implications for BMS operation more accurate balancing decisions, prioritization of problematic cells, reduced futile equalization cycles, and potential energy savings together with extended service life. These findings recommend RF as a core algorithm for machine-learning-based balancing chargers, particularly for real-world deployment on power-constrained edge devices.

Implementation of Random Forest Optimized with Ant Colony Optimization (ACO) for Breast Cancer Prediction

2025-10-12T11:08:28+07:00

Breast cancer is a significant disease impacting women globally, highlighting the necessity for precise and dependable diagnostic models. This study aims to improve breast cancer prediction by optimizing the Random Forest algorithm using Ant Colony Optimization (ACO). This study uses datasets containing various cell characteristics to build and evaluate models. The ACO algorithm is applied to fine-tune the hyperparameters of the Random Forest model and improve its predictive performance. The experimental results showed that the optimized Random Forest model outperformed the baseline model in all evaluation metrics. The optimized model achieved an accuracy of 94.74%, precision of 97.92%, recall 90.38%, an F1 score of 92.93%, and an AUC score of 0, 9449 compared to the basic Random Forest model, with lower scores across all metrics. This improvement highlights the effectiveness of ACOs in improving model performance, especially in reducing false negatives, which are critical for medical diagnosis. This study demonstrates that ACO successfully fine-tunes Random Forest hyperparameters, achieving superior accuracy compared to baseline and outperforming previous optimization methods such as PSO. These findings confirm that the combination of Random Forest and ACO offers a powerful and effective approach to improving the accuracy of breast cancer predictions, making them a valuable tool for clinical decision-making.

Prediksi Insomnia Berdasarkan Aktivitas Pengguna Twitter Menggunakan Natural Language Processing dan Machine Learning

2025-10-12T11:22:26+07:00

Insomnia is a sleep disorder that is widely experienced by the public and has a significant impact on physical and mental health, as well as productivity. However, early detection of insomnia remains a challenge because its symptoms are difficult to identify directly. This study uses historical data of 13,950 tweets from 4,286 Twitter accounts (January 1–April 30, 2025) to predict potential insomnia using Natural Language Processing (NLP) and machine learning methods. Insomnia labels are determined through an expert-verified keyword-based approach, followed by preprocessing, temporal analysis, and sentiment analysis. Two classification models are used: Support Vector Machine (SVM), which excels at separating classes in high-dimensional data, and Long Short-Term Memory (LSTM), which excels at capturing sequential patterns and temporal context. Preliminary results showed that SVM had 89% accuracy and was superior in the non-insomnia class (precision 0.80, recall 0.97) but suboptimal in insomnia (precision 0.92, recall 0.82), while LSTM had 90% accuracy and was better in insomnia (precision 0.98, recall 0.86) but slightly inferior in non-insomnia (precision 0.81, recall 0.96). Since each model had different strengths, they were combined with a probabilistic ensemble averaging method which resulted in 92% accuracy with balanced improvements in both classes (non-insomnia: precision 0.82, recall 0.99; insomnia: precision 1.00, recall 0.88), making it more reliable than a single model in detecting potential insomnia.

Journal of Computer System and Informatics (JoSYC)

Analisis Ketidakseimbangan Tegangan Baterai dengan Pendekatan Random Forest, K Nearest Neighbors untuk Prediksi Balancing Charger

Implementation of Random Forest Optimized with Ant Colony Optimization (ACO) for Breast Cancer Prediction

Prediksi Insomnia Berdasarkan Aktivitas Pengguna Twitter Menggunakan Natural Language Processing dan Machine Learning

Analisis Ketidakseimbangan Tegangan Baterai dengan Pendekatan Random Forest, K Nearest Neighbors untuk Prediksi Balancing Charger