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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Informatika Jurnal Pilar Nusa Mandiri 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)
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 : Jurnal Informatika

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SemetonBug: A Machine Learning Model for Automatic Bug Detection in Python Code Based on Syntactic Analysis Imran, Bahtiar; Riadi, Selamet; Suryadi, Emi; Zulpahmi, M.; Zaeniah, Zaeniah; Wahyudi, Erfan
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.25340

Abstract

Bug detection in Python programming is a crucial aspect of software development. This study develops an automated bug detection system using feature extraction based on Abstract Syntax Tree (AST) and a Random Forest Classifier model. The dataset consists of 100 manually classified bugged files and 100 non-bugged files. The model is trained using structural code features such as the number of functions, classes, variables, conditions, and exception handling. Evaluation results indicate an accuracy of 86.67%, with balanced precision and recall across both classes. Confusion matrix analysis identifies the presence of false positives and false negatives, albeit in relatively low numbers. The accuracy curve suggests a potential overfitting issue, as training accuracy is higher than testing accuracy. This study demonstrates that the combination of AST-based feature extraction and Random Forest can be an effective approach for automated bug detection, with potential improvements through model optimization and a larger dataset.
Co-Authors Aerani, Aerani ahmad yani Ahyat, Muhammad Akbar, Ardiyallah Dewi, Denda Cindera Erniwati, Surni Fahrurrazikin, Fahrurrazikin Hafizatulumah, Hafizatulumah Haikal Wahyudi Haliza, Siti 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 Masjun Efendi Muhammad Ahyat Muhammad Masjun Efendi Muhammad Rijal Alfian Muslihanah, Muslihanah Rudi Muslim Sahid, Lintia Purnama Salman Salman Salman Salman Selamet Riadi Subki, Ahmad Surni Erniwati Suryadi, Emi wahyuni, wenti ayu Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenul Muttaqin Zulkarnaen Zulkarnaen Zulpahmi, M.