Article Per Year (5 Year)
p-Index From 2021 - 2026
0.702
P-Index
This Author published in this journals
All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
Hartato, Bambang Pilu
Unknown Affiliation
Computer Science & IT Education

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Machine Learning-Based Approach for HIV/AIDS Prediction: Feature Selection and Data Balancing Strategy Rahim, Abdul Mizwar A; Ridwan, Ahmad; Hartato, Bambang Pilu; Asharudin, Firman
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9125

Abstract

HIV/AIDS remains a significant global health challenge, requiring accurate predictive models for early detection and improved clinical decision-making. However, developing an effective predictive model faces challenges such as data imbalance and the presence of irrelevant features, which can compromise model accuracy. This study aims to enhance the performance of AIDS infection prediction models by integrating feature selection, data balancing, and machine learning classification techniques. Feature selection is conducted using Pearson Correlation, Mutual Information, and Chi-Square tests to retain only the most relevant features. Random Oversampling, SMOTE, and ADASYN are employed to address data imbalance and improve model robustness. Nine machine learning algorithms, including Decision Tree, Random Forest, XGBoost, LightGBM, Gradient Boosting, Support Vector Machine, AdaBoost, and Logistic Regression, are tested for classification. Performance evaluation using confusion matrix, precision, recall, F1-score, and AUC-ROC shows that tree-based models (Random Forest, Extra Trees, and XGBoost) achieve the best results, particularly in handling minority class predictions. The study concludes that combining feature selection, data balancing, and machine learning techniques significantly improves predictive performance, making it a valuable approach for early detection and clinical decision support in HIV/AIDS diagnosis. Future research may explore hyperparameter tuning and real-world clinical data integration to enhance practical applicability.
Pengujian YOLOv8 dan Centroid Tracking pada Sistem Deteksi, Klasifikasi, dan Penghitungan Jumlah Kendaraan Dharmasaputra, Kevin Dicky; Hartato, Bambang Pilu
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10873

Abstract

An automatic vehicle detection and counting system is essential for Intelligent Transportation Systems (ITS) to monitor and manage traffic effectively. This study evaluates the performance of the lightweight YOLOv8n (nano) model for vehicle detection and classification, combined with a Centroid Tracking algorithm to improve vehicle counting accuracy. YOLOv8n was selected for its balance between computational efficiency and detection accuracy, making it suitable for devices with limited resources. The research involved collecting a dataset of seven vehicle classes (bus_l, bus_s, car, truck_l, truck_m, truck_s, truck_xl), followed by data preprocessing and training the YOLOv8n model for 40 epochs. Data augmentation techniques were applied to enhance data variability and improve model robustness. The Centroid Tracking algorithm was integrated to maintain vehicle identity across frames and prevent double counting. Model evaluation used precision, recall, F1-score, and mean Average Precision (mAP). Results show YOLOv8n achieved an overall mAP@0.5 of 0.820. The “car” class attained the highest mAP of 0.963, while “truck_s” had the lowest at 0.665, mainly due to imbalanced data distribution. The Centroid Tracking effectively maintained object identities and provided consistent vehicle counts during testing. This combination offers a reliable and efficient system suitable for real-time traffic monitoring, parking management, and enhancing road safety. The YOLOv8n and Centroid Tracking-based system demonstrates strong potential for practical ITS applications, especially on devices with limited computational resources. Future work should focus on expanding the dataset and improving class balance to further enhance detection accuracy and system robustness.
Evaluating the Impact of Random Over Sampling on IndoBERT Performance for Indonesian Sentiment Analysis Alfinsyah, Dimas Ramadhan; Hartato, Bambang Pilu
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11488

Abstract

Sentiment analysis is a prominent research area in natural language processing (NLP). For the Indonesian language, IndoBERT has emerged as a leading model due to its competitive performance. However, its effectiveness is strongly influenced by balanced class distribution. A common challenge arises because user reviews on digital platforms, such as the Google Play Store, often exhibit imbalanced classes. This study investigates the effectiveness of the Random Over Sampler (ROS) technique in improving IndoBERT’s performance under imbalanced data conditions. The dataset consists of 13,821 user reviews of the IDN App collected from the Google Play Store between 2015 and July 2025. Prior to modeling, data preprocessing was performed, including punctuation removal, case folding, stopword removal, tokenizing, normalization, and stemming to ensure textual consistency. Reviews were categorized into two sentiment classes: positive (3–5 stars) and negative (1–2 stars). Two experimental scenarios were conducted: (1) IndoBERT without ROS and (2) IndoBERT with a balanced dataset using ROS. Model performance was evaluated using accuracy, precision, recall, and F1-score, with data split into 70% training, 20% validation, and 10% testing. Results showed a significant improvement after ROS implementation: 94.55% accuracy, 94.61% precision, 94.53% recall, and 94.54% F1-score. Confusion matrix analysis indicated improved classification of the minority class, reducing the error rate by 49%. However, learning curve analysis revealed potential overfitting due to ROS. Further research is needed to optimize ROS strategies for better performance and generalization.
Co-Authors Abdul Mizwar A. Rahim Ahmad Ridwan Alfinsyah, Dimas Ramadhan Asharudin, Firman Dharmasaputra, Kevin Dicky Wicaksono, Aryo Bimo