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All Journal Prosiding Seminar Nasional Official Statistics Indonesian Journal of Statistics and Its Applications
Mustika, Diva Arum
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Humanities Computer Science & IT Economics, Econometrics & Finance Mathematics Social Sciences Other

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Perbandingan Performa Metode Klasifikasi Regresi Logistik, Classification Tree, dan Random Forest (Studi kasus: Perkawinan Anak pada Perempuan Usia Muda di Nusa Tenggara Barat Tahun 2022) Pangestika, Dhita Elsha; Mustika, Diva Arum; Rahman, Ayub Abdul
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2440

Abstract

Child marriage remains a persistent issue in Indonesia, particularly in West Nusa Tenggara Province. This study compares the performance of three classification methods—logistic regression, classification tree, and random forest—in predicting child marriage among young women. The analysis uses 2022 National Socio-Economic Survey (Susenas) data, which comprises 69 women aged 20–24 who had married and were still living with their parents. Model performance was evaluated using the Area Under the Curve (AUC) metric with 50 validation repetitions. Logistic regression yielded the highest AUC (77.86%), followed by random forest (76.07%) and classification tree (75.49%). These results indicate that logistic regression is more stable and suitable for linear, low-dimensional, and limited observational data. Additionally, education level and the household head’s type of employment were identified as key predictors of child marriage.
KAJIAN EFEK SPASIAL KASUS DIFTERI DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) Mustika, Diva Arum; Nooraeni, Rani; IJSA, Indonesian Journal of Statistics and Its Applications
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
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.v3i1.185

Abstract

Diphtheria is an infectious disease caused by the Corynebacterium diphtheriae bacteria. Indonesia is the country with the most cases of diphtheria in Southeast Asia and ranks third in the world. In 2016, cases of diphtheria increased by 65 percent and became Extraordinary Events (KLB) in Indonesia, even though during 2013 to 2015 the number of cases of diphtheria has decreased. The province that has the highest number of diphtheria cases in Indonesia in 2016 is East Java. Diphtheria is centered and spread in certain districts / cities in East Java Province so that there are indications of spatial effects in the spread of diphtheria. Because data on the number of diphtheria cases overdispersed and indicated spatial effects in its spread, the main method used in this study was Geographically Weighted Negative Binomial Regression (GWNBR). This method will be compared with other alternative methods namely Poisson regression method and Negative Binomial Regression to get the best modeling. Based on the AIC value of each model it can be concluded that the best method for modeling the number of diphtheria cases is GWNBR. The modeling results with GWNBR show that there is indeed a spatial influence on the number of diphtheria cases and risk factors in East Java Province in 2016. The percentage of DPT-HB3 / DPT-HB-Hib3 immunization coverage is not significant in all observation areas, while the percentage of drug and vaccine availability is significant at entire observation area.
Co-Authors IJSA, Indonesian Journal of Statistics and Its Applications Nooraeni, Rani Pangestika, Dhita Elsha Rahman, Ayub Abdul