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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Informatika Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Jurnal Ilmiah Mandala Education (JIME) EXPLORE Community Development Journal: Jurnal Pengabdian Masyarakat Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Journal Software, Hardware and Information Technology Explore Jurnal Karya untuk Masyarakat (JKuM) E-Amal: Jurnal Pengabdian Kepada Masyarakat Sasambo: Jurnal Abdimas (Journal of Community Service) Jurnal Informatika
Zaeniah Zaeniah
Universitas Teknologi Mataram
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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SemetonBug: Next-Generation Machine Learning-Powered Code Analyzer for Precision Bug Detection and Dynamic Error Localization Erniwati, Surni; Imran, Bahtiar; Muahidin, Zumratul; Zaeniah, Zaeniah; Juhartini, Juhartini
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11837

Abstract

Bug detection in Python programming is a crucial challenge in software development. This research proposes SemetonBug, a machine learning-based system for automatically detecting bugs in Python code. The system utilizes a Random Forest Classifier as the main model, with features extracted from the syntactic structure of the code using an Abstract Syntax Tree (AST). The dataset consists of 200 Python files, divided into 100 files with bugs and 100 files without bugs. The model is optimized using Grid Search Cross Validation, with the best combination of n_estimators = 300, max_depth = 20, min_samples_split = 5, and min_samples_leaf = 2. Evaluation results show that the model achieves 85% accuracy, 0.84 precision, 0.87 recall, and 0.86 F1-score. The detected bugs are stored in an Excel file for further analysis. By leveraging machine learning, SemetonBug enhances efficiency and accuracy in bug identification compared to traditional rule-based methods. These findings highlight the potential of machine learning models in improving software quality and reducing coding errors automatically.
Co-Authors Aerani, Aerani AFRIWAN, OKKY ahmad yani Ahyat, M. Ahyat, Muhammad Akbar, Ardiyallah Dewi, Denda Cindera erniwati, surni Fahrurrazikin, Fahrurrazikin Hafizatulumah, Hafizatulumah Haikal Wahyudi Haliza, Siti Hambali Hambali Hambali Hambali Harmayanti, Windi Ayu Imran, Bahtiar indriana, pathul Irma Suryani Lalu Delsi Samsumar, M.Eng. Lalu Isnaeni Rahman M Multazam M. Budiamin M. Yusuf M. Zulpahmi Masjun Efendi Muahidin, Zumratul MUH. KABUL, LALU Muhammad Ahyat Muhammad Masjun Efendi Muhammad Rijal Alfian Muslihanah, Muslihanah Rudi Muslim Sahid, Lintia Purnama Salman Salman Salman Salman Selamet Riadi Selamet Riadi Subki, Ahmad Suryadi, Emi wahyuni, wenti ayu Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenul Muttaqin Zulkarnaen Zulkarnaen Zulpahmi, M.