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All Journal Jurnal Teknik Elektro Semesta Teknika Jurnal Pilar Nusa Mandiri CogITo Smart Journal Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jutech: Jurnal Teknologi Informasi Jurnal Sistem Informasi Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Sestri, Ellya
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Benchmarking Five Machine Learning Models for Accurate Steel Plate Defect Detection Sestri, Ellya; Karno, Adhitio Satyo Bayangkari; Hastomo, Widi
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.753.382-401

Abstract

Early detection of defects in steel plates is essential to ensure structural integrity and product quality in the metal manufacturing industry. This study compares the performance of five machine learning algorithms Support Vector Classifier (SVC), Nu-Support Vector Classifier (NuSVC), Decision Tree (DT), Random Forest (RF), and CatBoost (CB) to classify seven categories of steel plate defects using 26 technical features from a publicly available dataset on Kaggle. The preprocessing pipeline included outlier detection (IQR method), class imbalance correction using SMOTE, and feature normalization via StandardScaler. The models were evaluated using classification metrics such as Accuracy, Precision, Recall, F1-Score, ROC-AUC, and Log Loss. Results revealed that the CatBoost algorithm achieved the most balanced and consistent performance, with an AUC of 0.93, accuracy of 68.3%, and the lowest Log Loss value (0.786). In contrast, the Decision Tree showed severe overfitting with perfect training performance but poor generalization (Log Loss = 15.72). This study highlights the promise of CatBoost as an interpretable and efficient solution for automated defect detection in steel manufacturing, while also offering transparent reproducibility pathways for further research.
Co-Authors Al Rafli, Muhammad amanah, nurul Ananda Putra, Rian Azis, Nur Basyar Mussafa, Athif Daruningsih, Kukuh Dodi Arif Erlangga Ramadhan, Adhitya Ervin Ervin Fahrul Razi Husnayetti, Husnayetti Ignatius Joko Dewanto, Ignatius Joko Kamilia, Nada Kardian, Aqwam Rosadi Karno, Adhitio Satyo Bayangkari Kasoni, Dian Keviana Agatha, Chalia Masriyanda, Masriyanda Novida, Irma Pribadi, Sarli Amri Teguh Puji Utami Rabiyatul Adawiyah Raihan, Dendi Rasyiddin, Ahmad Reza Fitriansyah Setiawati, Popong Shevti Arbekti Arman Sundoro, Aries Vany Terisia Widi Hastomo YUSUF, DIANA