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All Journal Jambura Journal of Mathematics Limits: Journal of Mathematics and Its Applications
Qalbi, Asyifah
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Computer Science & IT Mathematics

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Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West Java Setyowati, Silfiana Lis; Qalbi, Asyifah; Aristawidya, Rafika; Sartono, Bagus; Firdawanti, Aulia Rizki
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.28736

Abstract

Random Forest is an ensemble learning algorithm that combines multiple decision trees to generate a more stable and accurate classification model. This study aims to optimize Random Forest parameters for classifying school-age students' eligibility for the Kartu Indonesia Pintar (KIP) in West Java, based on economic factors. The research uses secondary data from the 2023 National Socio-Economic Survey (SUSENAS) of West Java, with a sample size of 13,044 individuals. To address class imbalance, Synthetic Minority Oversampling Technique (SMOTE) is applied. Hyperparameter tuning through grid search identifies the optimal combination of parameters, including the number of trees (ntree), random variables per split (mtry), and terminal node size (node_size). Model performance is evaluated using balanced accuracy, sensitivity, and specificity. Results indicate that the optimal parameters (mtry = 5, ntree = 674, node_size = 26) yield a balanced accuracy of 65.47%. Significant variables include PKH status, floor area of the house, source of drinking water, and building material type. The model accurately identifies students in need of educational assistance. In conclusion, optimizing Random Forest parameters improves the accuracy of KIP eligibility classification, supporting educational equity policies in West Java. These findings provide a foundation for developing more effective beneficiary selection systems for educational aid.
Perbandingan Kinerja Hybrid Classification SVM-RF dan SVM-NN Terhadap Faktor Risiko Anemia Ibu Hamil di Indonesia dengan Pendekatan Clustering K-Means Qalbi, Asyifah; Erfiani, Erfiani; Susetyo, Budi
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 3 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 3 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Classification is one of the most researched topics by researchers from the field of machine learning and data mining. Machine learning methods that are often used include Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN). However, SVM does not always provide good accuracy. For example, when applied to highly imbalanced data, SVM will experience challenges. In addition, there is no single best method that can be applied to all classification problems. Currently, hybrid method approaches for data mining applications are becoming increasingly popular such as hybrid SVM-RF, SVM-NN and KMeans-SVM methods. In this study, a hybrid method of SVM-RF and SVM-NN was used to classify risk factors for anemia in pregnant women in Indonesia with a K-Means approach to cluster data misclassified by SVM. The results showed that the hybrid method can improve the performance of the SVM model. Hybrid SVM-RF provides a higher evaluation metric value compared to SVM-NN. The four evaluation metrics used, namely accuracy, balanced accuracy, sensitivity and specificity in SVM-RF are worth 0,989; 0,989; 0,988; and 0,989, respectively. The variables that contribute generally based on SHAP Global to the classification of risk factors for anemia in pregnant women in order are Age, Fe Tablet, Working Status, Education, Nutritional Status and ANC.
Co-Authors Aristawidya, Rafika Aulia Rizki Firdawanti Bagus Sartono Budi Susetyo Erfiani Erfiani Setyowati, Silfiana Lis