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All Journal Pedagogi : Jurnal Penelitian Pendidikan Union: Jurnal Ilmiah Pendidikan Matematika PRISMA CogITo Smart Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JE (Journal of Empowerment) MATHEMA: JURNAL PENDIDIKAN MATEMATIKA Mosharafa: Jurnal Pendidikan Matematika Jurnal Pengabdian Sains dan Humaniora Polyhedron International Journal in Mathematics Education Hexagon: Jurnal Ilmu dan Pendidikan Matematika
Erwan Setiawan, Erwan
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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SMOTE and Weighted Random Forest for Classification of Areas Based on Health Problems in Java Setiawan, Erwan; Sartono, Bagus; Notodiputro, Khairil Anwar
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9933

Abstract

Random Forest (RF) is a popular Machine Learning (ML) approach extensively employed for addressing classification issues. Nevertheless, the RF method for classification problems demonstrates suboptimal performance in cases of data imbalance. There are several approaches to enhance RF performance when coping with data imbalance issues, such as using weighting and oversampling. This research explores the intervention of RF in addressing data imbalances, focusing on case studies of health problem classification in Java This study aims to develop models to analyze the health status of regions using RF, WRF, SMOTE-RF, and SMOTE-WRF methods. The objective is to compare the performance of these models and identify the best model for classifying DBK and Non-DBK categories in Java. The research results show that SMOTE-WRF is the most effective model in classifying DBK, achieving an accuracy level of 93.62%, sensitivity of 85.71%, precision of 75%, F-score of 80%, and AUC of 93.57%. The three key variables in the SMOTE-WRF model entail access to adequate sanitation, egg and milk consumption, and the number of doctors
Co-Authors Adetia, Elsa Anang Kurnia Apriliani, Sundari Asikin, Rinawati Bagus Sartono Darwati, Jenudin Elsa Komala, Elsa Erma Monariska Feronika, Jelyani Halimah Halimah Haryadi, Dinda Ramadhia Hendri Murfi Inayah, Sarah Jusniani, Nia Loly, Joao Ferreira Rendes Bean Muhamad Soeleman Muhammad, Guntur Maulana Muharomah, Nita Nurul Ningsih, Dinni Nurfajrin Purwanto, Arie Putri, Risma Andrea Rahman, Gusti Arviana Ramadhia H, Dinda Rani Sugiarni Septian, Ari Septiani, Dea Tri Setiawan, Ayang SITI MARYAM Soeleman Soeleman Suprayogi, Muhammad Azis Suprayogi, Muhammad Aziz Sutandi, Andi Yudi Satria