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All Journal JURIKOM (Jurnal Riset Komputer) Bulletin of Computer Science Research
Sopiyan Apandi
Universitas Pamulang, Tangerang Selatan
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Perbandingan Random Forest dan Gradient Boosting pada Prediksi Hasil Belajar Siswa Sopiyan Apandi; Muhammad Bahrein
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.v13i3.9776

Abstract

Student learning outcomes are important indicators for evaluating the success of the learning process and can be used to support data-driven academic decision-making. This study aims to compare the performance of the Random Forest and Gradient Boosting algorithms in predicting student learning outcomes. The study employed a quantitative approach with a comparative experimental design using the student dataset containing 500 student records and 11 attributes. The target variable was passed, while several attributes such as gender, age, study_hours_per_week, attendance_rate, parent_education, internet_access, extracurricular, and previous_score were used as predictors. The data preprocessing stage included data cleaning, missing value handling, categorical data transformation, feature selection, and train-test splitting with an 80:20 ratio. Model evaluation was conducted using confusion matrix, accuracy, precision, recall, F1-score, and ROC-AUC, supported by 10-fold cross validation. The results showed that Random Forest slightly outperformed Gradient Boosting on most evaluation metrics. On the test data, Random Forest achieved an accuracy of 87.00%, precision of 90.63%, recall of 89.23%, F1-score of 89.92%, and ROC-AUC of 93.27%, while Gradient Boosting obtained an accuracy of 86.00%, precision of 90.48%, recall of 87.69%, F1-score of 89.06%, and ROC-AUC of 93.10%. These findings indicate that both algorithms performed well in predicting student learning outcomes, with Random Forest showing more stable performance on the dataset used.
Evaluasi Komparatif Algoritma Machine Learning untuk Prediksi Dini Diabetes Aniq Astofa; Perani Rosyani; Rahmawati Rahmawati; Sopiyan Apandi
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.859

Abstract

Diabetes is one of the non-communicable diseases that is often detected at an advanced stage, thereby increasing the risk of serious complications. The application of machine learning has the potential to support early diabetes detection; however, most previous studies have focused on large-scale datasets and high predictive accuracy, while methodological evaluations on small-sized clinical data remain limited. This study aims to evaluate and compare the performance of several machine learning algorithms for early diabetes prediction using a limited clinical dataset, with particular emphasis on analyzing the impact of data characteristics on model performance. The dataset used in this study consists of 22 samples with eight clinical features and one target variable, which were divided into 17 training samples and 5 testing samples. The research stages include data preprocessing, training–testing data splitting, model training, and performance evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The algorithms evaluated include Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. The experimental results indicate that none of the evaluated models were able to effectively detect the diabetes class, as reflected by precision, recall, and F1-score values of zero across all models. Although Random Forest and XGBoost achieved an accuracy of 0.6, this value was largely influenced by the dominance of the non-diabetes class in the very limited test set. Correlation analysis further reveals that Glucose, BMI, and Diabetes Pedigree Function are the most influential features associated with diabetes status. The main contribution of this study lies in providing a realistic methodological evaluation of machine learning models applied to small-sized clinical datasets, highlighting that limited sample size and training–testing data partitioning have a substantial impact on model performance and the interpretation of evaluation metrics. These findings provide an important methodological reference for future studies aiming to develop more reliable early diabetes prediction models under constrained clinical data conditions.
Co-Authors Astofa, Aniq Muhammad Bahrein Perani Rosyani Rahmawati Rahmawati