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All Journal Journal of Informatics Management and Information Technology Journal of Informatics, Electrical and Electronics Engineering
Solikhun Solikhun
Sekolah Tinggi Ilmu Komputer Tunas Bangsa, Pematangsiantar
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Optimasi Kinerja Algoritma Random Forest dengan SMOTE untuk Prediksi Kinerja Akademik Siswa Muhammad Rizky Ramadhan; Solikhun Solikhun
Journal of Informatics Management and Information Technology Vol. 6 No. 2 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v6i2.950

Abstract

Student academic performance prediction is an essential component of Educational Data Mining (EDM) for the early identification of at-risk students. This approach aims to improve prediction accuracy and support decision-making for the early identification of at-risk students. This study proposes an optimized prediction pipeline that integrates feature engineering, Pearson Correlation-based Feature Filtering (PCFF), the Synthetic Minority Over-sampling Technique (SMOTE), and Random Forest (RF) to predict student academic performance. The Portuguese Student Performance Dataset (1,043 clean records; Pass = 814, Fail = 230) was used for evaluation. Four engineered features were constructed, reducing the feature space to 15 features through PCFF (threshold |r| ? 0.1). SMOTE was applied exclusively within each training fold to prevent data leakage. Two primary models were evaluated: a baseline Naïve Bayes model (Accuracy = 90.43%) and the proposed RF Default + SMOTE model (Accuracy = 93.78%, Recall = 95.09%, F1-score = 95.98%). Ten-fold stratified cross-validation achieved an accuracy of 90.89% ± 2.08%. The engineered feature G_avg obtained the highest feature importance score (0.285), outperforming the original grade features. The results demonstrate that integrating SMOTE and feature engineering significantly improves minority class detection, reducing False Negatives from 15 (baseline) to 8 (RF + SMOTE), representing a 46.7% improvement in identifying at-risk students.
Penerapan SMOTE pada Algoritma LightGBM dan XGBoost untuk Klasifikasi Penyakit Diabetes Regita Audyna Siregar; Solikhun Solikhun; Timbo Faritcan P. Siallagan
Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v5i4.2593

Abstract

Diabetes mellitus is a chronic metabolic disease whose prevalence continues to increase worldwide and is often diagnosed late owing to insufficient recognition of its early symptoms. One of the major challenges in diabetes classification using machine learning is class imbalance in the dataset, which may cause the model to disproportionately favor the majority class and consequently compromise its capacity to accurately identify diabetic patients. Accordingly, this research seeks to compare the classification effectiveness of Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) algorithms combined with the Artificial Minority Oversampling Approach (SMOTE) applied to diabetes classification task. The study utilized the Pima Indians Diabetes Dataset consisting of 768 instances and 8 attributes and followed the Machine Learning Life Cycle (MLLC), including exploratory data analysis, MinMax Scaler normalization, feature selection using Mutual Information, class imbalance handling with SMOTE, model training, and evaluation using accuracy, precision, recall, F1-score, and ROC-AUC as quantitative measures. Analysis of the results demonstrates that the application of SMOTE significantly improved the performance of both models. LightGBM with SMOTE achieved the best performance, with an accuracy of 85.33%, precision of 81.55%, recall of 91.33%, F1-score of 86.16%, and ROC-AUC of 90.49%, while XGBoost with SMOTE achieved an accuracy of 83.00%, precision of 79.64%, recall of 88.67%, F1-score of 83.91%, and ROC-AUC of 90.04%. These findings demonstrate that the combination of LightGBM and SMOTE is more effective than XGBoost in detecting diabetes cases and holds considerable promise for deployment as a clinical decision-making tool in early diabetes screening.
Co-Authors Muhammad Rizky Ramadhan Regita Audyna Siregar Timbo Faritcan Parlaungan Siallagan