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All Journal JURIKOM (Jurnal Riset Komputer) Journal of Electrical Engineering and Computer (JEECOM)
Mochamad Imamudin
Universitas Islam Negeri Maulana Malik Ibrahim, Malang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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Analisis Pengaruh Preprocessing Regex dan Cosine Similarity terhadap Performa IndoBERT dalam Klasifikasi Berita Hoaks Berbahasa Indonesia Minggar P. D. Ramadhan; Zainal Abidin; Mochamad Imamudin
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9730

Abstract

The rapid growth of online media has significantly improved access to information, but it has also accelerated the spread of misinformation and hoax news. In hoax detection research, datasets are commonly derived from fact-checking platforms, which typically contain structured components such as claims, narratives, and clarification statements explicitly indicating that certain information is false. The presence of such clarification sentences has the potential to cause bias, a condition in which the model learns text patterns that explicitly indicate the label, thereby reducing the model's ability to fully understand the content of the news. This study aims to analyze the impact of preprocessing techniques based on regular expression (regex) and cosine similarity on the performance of the IndoBERT model for Indonesian hoax news classification. Both approaches are employed to identify and handle clarification sentences, enabling the model to focus more on contextual and semantic understanding of the news content. Experimental results show that the cosine similarity-based preprocessing outperforms the regex-based approach, achieving accuracy, precision, recall, and F1-score of 92.8%. In comparison, the regex-based method obtains an accuracy of 90.7%, precision of 91.3%, recall of 90.7%, and F1-score of 90.6%. These findings indicate that the semantic-based approach is more effective in handling linguistic variability and reducing potential bias caused by explicit clarification patterns. Overall, this study highlights the importance of appropriate preprocessing strategies in improving classification performance and provides insights into the impact of clarification statements in fact-checking datasets on transformer-based hoax detection models
Predicting Sleep Quality Using Hybrid Stacking Ensemble Of Random Forest And Catboost With Shap Interpretability Baitullah Hakim; Muhammad Faisal; Mochamad Imamudin
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.17445

Abstract

Sleep quality is an important indicator of physical and mental well-being, yet its prediction from lifestyle variables remains challenging because behavioral and physiological factors interact nonlinearly. This study develops a hybrid stacking ensemble that combines Random Forest and CatBoost as base learners and Logistic Regression as the meta-classifier for three-class sleep-quality prediction (Poor, Moderate, Good). The Sleep Health and Lifestyle Dataset contains 374 records, of which only 109 are unique, and is preprocessed through blood-pressure decomposition, derived-feature construction, categorical encoding, and numerical standardization. Models are evaluated with Accuracy, Precision, Recall, F1-Score, and multiclass ROC-AUC under Stratified 10-Fold Cross Validation, both in its standard form and in a duplicate-aware form in which identical records are kept in the same fold. Under the duplicate-aware protocol, Random Forest achieved 97.86% Accuracy, 91.06% macro F1-Score, and 0.9970 ROC-AUC; CatBoost achieved 97.33%, 90.70%, and 0.9930; and Hybrid Stacking achieved 97.06%, 90.51%, and 0.9784. Recall for the minority Poor class (n = 12) was 66.7% for all three models, and stacking did not improve on its base learners. SHAP analysis identifies Stress Level and Sleep Duration as the two dominant features. The results are specific to this small, largely duplicated dataset and do not support clinical use.
Co-Authors Baitullah Hakim Minggar P. D. Ramadhan Muhammad Faisal Zainal Abidin