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All Journal Jurnal Pilar Nusa Mandiri JTERA (Jurnal Teknologi Rekayasa) JOURNAL OF APPLIED INFORMATICS AND COMPUTING CSRID (Computer Science Research and Its Development Journal) EXPLORE Jurnal Computer and Technology Explore
Muahidin, Zumratul
Universitas Amikom Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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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.
Anomaly-Based DDoS Detection Using Improved Deep Support Vector Data Description (Deep SVDD) and Multi-Model Ensemble Approach Imran, Bahtiar; Samsumar , Lalu Delsi; Subki, Ahmad; Wahyuni, Wenti Ayu; Muahidin, Zumratul; Karim, Muh Nasirudin; Yani, Ahmad; M. Zulpahmi
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.v10i1.11863

Abstract

Distributed Denial-of-Service (DDoS) attacks remain a critical threat to network infrastructure, demanding robust and efficient detection mechanisms. This study proposes an enhanced Deep Support Vector Data Description (Deep SVDD) model for unsupervised DDoS detection using the UNSW-NB15 dataset. The approach leverages a deep encoder architecture with batch normalization and dropout to learn compact latent representations of normal traffic, minimizing the hypersphere volume enclosing benign flows. Only normal samples are used during training, adhering to the unsupervised anomaly detection paradigm. The model is evaluated against five established baselines—Isolation Forest, Local Outlier Factor (LOF), One-Class SVM, Autoencoder, and a simple ensemble—using AUC, F1-score, and recall as primary metrics. Experimental results demonstrate that Deep SVDD significantly outperforms all baselines, achieving superior class separation, high detection sensitivity, and computational efficiency (0.0004 GFLOPs). Notably, while LOF exhibited a deceptively high F1-score, its AUC near 0.5 revealed poor discriminative capability, highlighting the risk of relying on single metrics. The ensemble approach failed to improve performance, underscoring the limitation of naive score averaging when weak detectors are included. Visualization of score distributions and ROC curves further confirms Deep SVDD’s ability to effectively distinguish DDoS from benign traffic. These findings affirm that representation learning in latent space offers a more reliable foundation for anomaly detection than traditional distance-, density-, or reconstruction-based methods. The proposed model presents a promising solution for real-time, low-overhead intrusion detection systems in modern network environments. Future work will explore adaptive ensembles, self-supervised pretraining, and deployment on edge devices.
Co-Authors Abda Abda Adiatma, Biva Candra Lutfi ahmad yani Asro Nasiri Azkiyah, Eli Baiq Norma Alyanesti Abella Efendi, Muhamad Masjun Efendi, Muhammad Masjun erniwati, surni Imran, Bahtiar Karim, Muh Nasirudin Kusrini Kusrini Kusrini Kusrini, Kusrini Lalu Delsi Samsumar, M.Eng. M. Zulpahmi Mutaqin, Zaenul Rudi Muslim Samsumar , Lalu Delsi Subki, Ahmad wahyuni, wenti ayu Zaeniah Zaeniah Zaeniah Zulpan Hadi