Article Per Year (5 Year)
p-Index From 2021 - 2026
2.862
P-Index
This Author published in this journals
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

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
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.
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