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

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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
Indonesian Journal of Statistics and Applications Vol 10 No 1 (2026): Vol 10 Issue 1 June 2026
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.v10i1p119-128

Abstract

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
Missing Value Estimation Using Fuzzy C-Means in Classification of Chronic Kidney Disease: Pendugaan Missing Values Menggunakan Fuzzy C - Means Pada Pengklasifikasian Penyakit Ginjal Kronik Raisa Nida Eria; Aam Alamudi; Itasia Dina Sulvianti; Pika Silvianti; Septian Rahardiantoro
Indonesian Journal of Statistics and Applications Vol 9 No 1 (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.v9i1p21-32

Abstract

Based on World Health Organization (WHO) the cases of death due to Chronic Kidney Disease (CKD) ranked the 10th worldwide in 2020. CKD need to be done prevent early. History data to identify individuals predisposed to CKD in this research. In this research data contains missing values, therefore using Fuzzy C - Means (FCM) method to address it. The percentage of error in clustering CKD using FCM method is 20,25% and balanced accuracy of 84,80%. The result from classification using Classification and Regression Trees (CART) shows that accuracy value of 97,50%; sensitivity of 100,00%; and specificity of 92,86%. Individual suffer from CKD if having (1) hemoglobin more than or equal 13; spesific gravity 1,020 or 1,025; serum creatinine less than 1,3; albumin 1 or 2 or 3 or 4 or 5; and sugar 0 or 2 or 3 or 4 or 5, (2) hemoglobin more than or equal 13; spesific gravity 1,020 or 1,025; and serum creatinine more than or equal 1,3, (3) hemoglobin more than or equal 13 and spesific gravity 1,005 or 1,010 or 1,015, (4) hemoglobin less than 13 and red blood cell count less than 5,5.
Co-Authors Bagus Sartono Defri Ramadhan Ismana Erfiani Itasia Dina Sulvianti Mohammad Masjkur Pika Silvianti Rahma Anisa Raisa Nida Eria Septian Rahardiantoro Yuri Nurdiantami