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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Indonesian Journal of Statistics and Its Applications
Setiabudi, Nur Andi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Mathematics Other

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Comparing Self-Paced Ensemble and RUSBoost for Imbalanced Poverty Classification in West Java Setiabudi, Nur Andi; Sartono, Bagus; Syafitri, Utami Dyah; Aryasa, Komang Budi
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
Toward an Adaptive IPO-Based Information Systems Framework for Customer Churn Management Syibli, Mohammad; Gernowo, Rahmat; Surarso, Bayu; Setiawan, Aldi; Setiabudi, Nur Andi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2552

Abstract

The high churn rate in the telecommunications industry remains a persistent challenge affecting customer retention, revenue stability, and long-term competitiveness. Despite extensive research, most customer churn management (CCM) studies in the telecom sector focus narrowly on improving model accuracy, overlooking organizational, strategic, and adaptive dimensions essential for effective management. This paper presents a systematic literature review (SLR) of academic publications from 2020 to 2025, analyzed through the Input–Process–Output (IPO) framework, to synthesize state-of-the-art developments in CCM from an Information Systems perspective. Twenty high-impact studies were coded across industries, emphasizing telecommunications, to examine data inputs, analytical processes, outputs, and feedback or retraining mechanisms. The findings reveal a strong bias toward predictive modelling using ensemble machine learning techniques (e.g., Random Forest, XGBoost, LightGBM) and limited exploration of explainable AI tools (SHAP, LIME), adaptive retraining, and business validation. This imbalance highlights the need for a holistic, adaptive framework integrating analytical intelligence with managerial decision-making. The study contributes by proposing a synthesized reference model and future research agenda for developing adaptive, information-systems-based churn management frameworks in the telecommunications industry.
Co-Authors Aldi Setiawan, Aldi Aryasa, Komang Budi Bagus Sartono Bayu Surarso Rahmat Gernowo Syibli, Mohammad Utami Dyah Syafitri