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

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K-Prototypes Algorithm for School Indexing in Report Card-Based Student Admissions: Algoritma K-Prototypes untuk Indeks Sekolah pada Penerimaan Mahasiswa Baru Jalur Rapor Ervina Dwi Anggrahini; Mohammad Masjkur; Utami Dyah Syafitri
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.v9i1p117-135

Abstract

Institut Pertanian Bogor, also known as IPB University, is a state university that was ranked first as the best university in Indonesia by the Ministry of Research and Technology in 2020. It has three main channels in the new student admission selection system. The selection method is called “Seleksi Nasional Berdasarkan Prestasi”. “Seleksi Nasional Berdasarkan Prestasi” is one of the new student admission pathways at IPB University based on report cards without a test. The selection of new student admissions based on report cards requires creating a school index to assess the quality and commitment of each school by grouping schools among “Seleksi Nasional Berdasarkan Prestasi” applicants. One method that can be used is the K-Prototypes algorithm. K-Prototypes can be used to cluster large and mixed-type data (numeric and categorical) by combining distance measures from two non-hierarchical methods, namely the K-Means and K-Modes algorithms. Based on the analysis, the K-Prototypes algorithm yields three optimal clusters, each with distinct characteristics. Cluster 1 is the lowest cluster because it comprises schools with the lowest quality and commitment to new student admissions at IPB University, as indicated by the report card. Cluster 2 has a quality that is not superior to Cluster 3 but is higher than that of Cluster 1. Cluster 3 is the best cluster because it consists of schools that have high quality and commitment to new student admissions at IPB University through the report card route.
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 Ervina Dwi Anggrahini Komang Budi Aryasa Mohammad Masjkur Nur Andi Setiabudi