Comparative Study of Multiclass Models for Identifying Household Drinking Water Sources in West Java - Defri Ramadhan Ismana; Bagus Sartono; Erfiani; Yuri Nurdiantami; Mohammad Masjkur; Aam Alamudi; Rahma Anisa (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), 2026)
Data imbalance is a common challenge in classification modeling, typically driven by rare events such as fraud, credit default, student dropout, or infectious diseases. Multi-class imbalance is inherently more complex than binary classification because a class can be a minority relative to one class yet a majority to another. Access to safe drinking water is one of the key factors for public health. West Java, as the province with the largest population in Indonesia, has yet to achieve the target of 100% of households having access to safe drinking water. Identifying the types of drinking water sources can be approached using multi-class classification modeling. However, this case presents an issue of data imbalance. The CatBoost method is one of the recommended approaches for multi-class classification with imbalanced data. Additionally, the TabNet method is also considered to perform well in such cases. Therefore, this study aims to apply both methods and compare their performance in classifying household drinking water sources in West Java using 2023 SUSENAS data. The results show that CatBoost yielded an average MCC of 0.225, whereas TabNet exhibited an average of 0.209. In terms of computational efficiency, CatBoost recorded an average execution time of 25 seconds, compared to 134 seconds for TabNet. Therefore, the CatBoost method provides better multi-class classification performance and faster execution compared to TabNet. Although TabNet is superior in classifying minority classes, CatBoost is more accurate in predicting majority classes and identifying the most influential variables, such as building ownership status and regional classification