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All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial MULTINETICS Simpatik: Jurnal sistem Informasi dan Informatika J-Intech (Journal of Information and Technology) Ladang Artikel Ilmu Komputer Journal of Entrepreneurship and Community Innovations Journal of Information Technology
Dede Wintana
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Library & Information Science Social Sciences

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Improving Software Defect Prediction Performance Using C4.5 Based Ensemble Learning with AdaBoost and Bagging Techniques Dede Wintana; Dinar Ismunandar; Eka Herdit Juningsih
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/k7fyc413

Abstract

Software defect prediction (SDP) plays a crucial role in improving software quality by enabling the early detection of faulty modules during the development phase. However, class imbalance within software defect datasets remains a significant challenge that adversely impacts prediction accuracy. This study aims to address this issue by implementing ensemble learning methods—specifically Bagging and AdaBoost—combined with the C4.5 decision tree algorithm to enhance classification performance. The research utilized five well-known datasets from the NASA MDP Repository (CM1, JM1, KC1, KC2, and PC1), each containing comprehensive software metrics and defect labels. The methodology involved several stages: data preprocessing (normalization and discretization), model training using 10-fold cross-validation, and performance evaluation through metrics such as accuracy and Area Under the Curve (AUC). Results indicate that both ensemble methods outperformed the standalone C4.5 algorithm across all datasets. Notably, the AdaBoost + C4.5 model yielded the highest accuracy in most scenarios, with the PC1 dataset reaching 97.20% accuracy. In comparison, C4.5 alone and C4.5 with Bagging recorded lower values, demonstrating the significant impact of adaptive weighting in AdaBoost. These findings affirm that ensemble learning, particularly AdaBoost, effectively mitigates the impact of class imbalance and improves prediction performance in SDP tasks.
Optimization of Android Malware Detection Based on Permissions Using LightGBM with Hyperparameter Tuning and Chi-Square Feature Selection Dede Wintana; Satia Suhada; Gunawan
J-INTECH ( Journal of Information and Technology) Vol 14 No 02 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i02.2306

Abstract

This study aims to develop an efficient Android malware detection framework using the LightGBM algorithm optimized through Chi-Square feature selection and hyperparameter tuning. Although machine learning techniques have been widely applied in Android malware detection, many previous studies primarily focus on classification accuracy while paying limited attention to feature reduction efficiency, computational complexity, and permission-based feature interpretability. Therefore, this study proposes a lightweight permission-based detection framework that integrates Chi-Square feature selection with optimized LightGBM classification to improve detection performance while reducing irrelevant features. The dataset used was obtained from Kaggle and consists of 29,300 Android applications, including 14,630 benign applications and 14,700 malware samples. Each application is represented using 86 binary permission-based features extracted from the AndroidManifest.xml file. The research stages include preprocessing, feature selection, training-testing data splitting, hyperparameter optimization, and model evaluation. Experimental results show that the proposed model achieves an accuracy of 0.96, precision of 0.95, recall of 0.96, F1-score of 0.96, and ROC-AUC of 0.9885. These findings indicate that the proposed framework effectively distinguishes malware from benign applications while maintaining computational efficiency and improving permission-based malware detection interpretability. The novelty of this study lies in the integration of Chi-Square feature selection and Grid Search-optimized LightGBM to develop a lightweight permission-based Android malware detection framework that reduces feature dimensionality while maintaining high detection performance and interpretability.
Co-Authors Ami Rahmawati Ami Rahmawati Ami Rahmawati Anak Agung Istri Sri Wiadnyani Apip Supiandi Daniel Ratag Diah Ayu Ambarsari Dinar Ismunandar Eka Herdit Juningsih Ghani, Muhammad Abdul Gunawan Gunawan Gunawan Gunawan Gunawan Gunawan Gunawan Gunawan Hamdun Sulaeman Hamdun Sulaiman Hamdun Sulaiman Hikmatulloh Hikmatulloh Hikmatulloh Hikmatulloh Jajang Jaya Purnama Jasisca Marleftan Moh Yusup Nurhadi Muhammad Abdul Ghani Novita, Yati Nugraha, Setyo Bagus Nurul Ichsan Pribadi, Denny Rahmawati, Ami Ramdhan Saepul Rohman Ramdhan Saepul Rohman, Ramdhan Saepul Risma Nurfauzia Rusda Wajhillah Saeful Bahri Saeful Bahri Saeful Bahri Saeful Bahri Satia Suhada, Satia Setyo Bagus Nugraha Sulaeman, Hamdun surendi Taufik Hidayatulloh Taufik Hidayatulloh Veithzal Rivai Zainal yati novita, surendi Yusti Farlina