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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi GRADIEN BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Teknoinfo Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Berdaya Mandiri TRIBUTE: JOURNAL OF COMMUNITY SERVICES Journal Of Human And Education (JAHE) PENA ABDIMAS : Jurnal Pengabdian Masyarakat Journal of Scientech Research and Development JAPI: Jurnal Akses Pengabdian Indonesia Prosiding Seminar Nasional Official Statistics Jurnal Penelitian Ilmu Pendidikan Indonesia MAP (Jurnal Manajemen dan Administrasi Publik) Jurnal Ilmu Kesehatan dan Gizi Abdi Reksa Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M) Indonesian Journal of Statistics and Its Applications Journal of Statistics and Data Science
Dyah Setyo Rini
Jurusan Matematika, Fakultas MIPA, Universitas Bengkulu
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Comparison Of Methods For Handling Imbalanced Datasets In Improving Classification Algorithm Performance Dyah Setyo Rini; Winalia Agwil; Dian Agustina; Ahmad Famuji
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.35780

Abstract

Data availability in large observations and dimensions is known as big data. There are several problems in processing big data, such as imbalanced datasets. In classification modeling, an imbalanced dataset is a common challenge. Data class predictions are more likely to be accurate in the majority class data and inaccurate in the minority class, resulting from the problem of imbalanced data. The data-level, the algorithm-level, and the ensemble method approach are the solutions that have been extensively researched. Some methods with a data-level approach are SMOTE, Undersampling, and Oversampling.  The algorithm-level method is NWKNN. And then, the ensemble approach is UnderBagging, RUSBoosting, SMOTEBoost, and SMOTEBagging. The goal of this study is to determine the best method for handling each case of the imbalanced dataset. There are three cases of imbalance, namely mild, moderate, and extreme. A simulation study was conducted for each imbalanced case to evaluate the accuracy of each method. Based on the AUC value, the SMOTEBagging method is the best for mild imbalance cases with an AUC value of 0.9581. For moderate imbalance cases, the SMOTEBagging method is the best method, with an AUC value of 0.9033. Meanwhile, for extreme imbalance cases, the UnderBagging method provides the best performance.
Co-Authors Agwil, Winalia Ahmad Famuji Alwansyah, Muhammad Arib Alya Saputri Andang Wijanarko Angel Syarifatunnisa Arono Arono Avrillia Permata Hati4 Bagus Sartono Baki Swita Bayubuana, Muhammad Gabdika Bayubuana Cinta Rizki Oktarina Crisdianto, Riki Crisdianto, Riki Dian Agustina Dian Agustina Dian Agustina Dian Agustina Dian Agustina Dwi Putri, Malia Eka Chandra Wardana, Dian Eko Heru Purwanto Eko Heru Purwanto Elfi Yuliza Elfi Yuliza Etis Sunandi Fachri Faisal Fairuzindah, Athaya Famuji, Ahmad Herlin Fransiska Hermalia Hiasa, Fina Idhia Sriliana Indah Wahyuliani Jomecho, Tommy Karuna, Elisabeth Evelin M. Taufiqurrahman Syah Meiwidian, Muhamad Iqbal Meylisah, Eni Mochammad Yusa Muhammad Taufiqurrahman Syah Nafri Yanti Nafri Yanti Natsir, Mohammad Nur Afandi Nutia Rosdalena Nutia Rosdalena Oktarina, Cinta Rizky Pepi Novianti Praditia, Rahmat Kevin Pratiwi, Stevy Cahya Purwanto, Eko Heru Qhiky Lioni Tasyah Rahmat Kevin Praditia Renaldi Reyvo Valeriyan Reyvo Valeriyan Reza Pahlepi Riwi Dyah Pangesti Rosdalena, Nutia Safroni Aziz Suprianto Saputri, Alya Saputri, Alya Setiawan, Yudi Sigit Nugroho Siska Yosmar Sri Syuhada Putri Supianti, Filo Syah, M. Taufiqurrahman Syah, Muhammad Taufiqurrahman Ulfasari Rafflesia Valeriyan, Reyvo Winalia Agwil Winalia Agwil Winalia Agwil Wiranda, Risca Putri Wiranda, Risca Putri Yanti, Rizki Dwi Yudi Setiawan Yudi Setiawan Zulfia Memi Mayasari