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All Journal Xplore: Journal of Statistics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Pika Silvianti
IPB University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics

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Klasifikasi Status Keaktifan Siswa SMA di Jawa Barat Menggunakan Random Forest dengan SMOTE M Itmamurohman; Pika Silvianti; La Ode Abdul Rahman
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (229.188 KB) | DOI: 10.29244/xplore.v11i2.929

Abstract

The dropout rate in Indonesia has a higher percentage as education levels grow. The high school dropout rate in Indonesia is at 0.67%. West Java is the province with the highest high school dropout rate in the academic year 2017/2018. In the next academic year, the high school dropout rate in West Java decreased. The student who drop out of school was caused by various factors. This study examines important variables and classification performance that are generated by random forest. The number of dropout students is very small compared to the number of active students. The imbalance data is handled using SMOTE. Random forest with SMOTE is considered able to predict data classes better because it can increase sensitivity values and reduce errors in classifying dropout students as active students. Father's income, number of siblings, class, father's education level, and father's type of work are important variables that have a major influence in determining the active status of high school students in West Java.
Regresi Logistik Biner dan Support Vector Machine dalam Klasifikasi Indeks Pembangunan Manusia Rupmana Butar Butar; Destriana Aulia Rifaldi; Anwar Fitrianto; Pika Silvianti
Jurnal Teknik Informatika dan Sistem Informasi Vol 12 No 1 (2026): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v12i1.11853

Abstract

Binary Logistic Regression and Support Vector Machine (SVM) are two widely used classification methods in data analysis, especially for problems with categorical target variables. In this study, these two methods are compared to classify the Human Development Index (HDI) status of Indonesia in 2024. The initial data consists of five predictor variables, but after conducting a correlation analysis to avoid multicollinearity, only three variables were used in the modeling. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to address class imbalance. Binary Logistic Regression was chosen due to its good interpretability, while SVM was used as a comparison due to its robustness against outliers. Evaluation results show that Binary Logistic Regression achieved an accuracy of 87.85%, slightly higher than SVM, which reached 86.92%. Therefore, Binary Logistic Regression is considered more optimal in classifying HDI status on the data that has been balanced and simplified. This study contributes to the application of statistical methods and machine learning in supporting human development analysis based on data.
Co-Authors Destriana Aulia Rifaldi Fitrianto, Anwar La Ode Abdul Rahman M Itmamurohman Rupmana Butar Butar