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All Journal Indonesian Journal of Statistics and Its Applications
Nur Andi Setiabudi
Study Program on Statistics and Data Science, IPB University, Indonesia
Computer Science & IT Mathematics Other

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Comparing Self-Paced Ensemble and RUSBoost for Imbalanced Poverty Classification in West Java Nur Andi Setiabudi; Bagus Sartono; Utami Dyah Syafitri; Komang Budi Aryasa
Indonesian Journal of Statistics and Applications Vol 9 No 2 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i2p218-229

Abstract

Class imbalance remains a major challenge in classification modelling that frequently leads to biased predictive models. This study aimed to compare two ensemble techniques based on an undersampling approach, namely Self-Paced Ensemble and RUSBoost, for handling imbalanced classification in poverty identification in West Java. The results suggested that RUSBoost consistently outperformed Self-Paced Ensemble across the most critical metrics. It showed better balance in classification outcomes. When the objective is to maximize the identification of poor households, the default threshold in the RUSBoost model was prefered. On the other hand, if precision is prioritized due to limited resources, the Youden Index threshold offers a better alternative. Given the overall evaluation metrics, RUSBoost with the default threshold was suggested as the most reliable and well-balanced option among the compared models for classifying poor households in West Java under imbalanced data condition
Co-Authors Bagus Sartono Komang Budi Aryasa Utami Dyah Syafitri