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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Bambang Widjanarko Otok
Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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COMPARISON OF BINARY PROBIT REGRESSION AND FOURIER SERIES NONPARAMETRIC LOGISTIC REGRESSION IN MODELING DIABETES STATUS AT HAJJ GENERAL HOSPITAL SURABAYA Bambang Widjanarko Otok; Muhammad Zulfadhli; Riwi Dyah Pangesti; Muhammad Idham Kurniawan; Albertus Eka Putra Haryanto; Darwis Darwis; Iwan Kurniawan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0255-0270

Abstract

Diabetes mellitus is a chronic disease with a rising global prevalence, including in Indonesia. Early detection and accurate modeling are crucial for effective prevention and management. Binary Logistic Regression (BLR) is commonly used for binary outcome modeling; however, in practice, the relationship between binary outcomes and continuous predictors is often nonlinear, making BLR less suitable. To address these limitations, alternative methods such as Binary Probit Regression (BPR) and Flexible Semiparametric Nonlinear Binary Logistic Regression (FSNBLR) have been developed. This study aims to compare the performance of BLR, BPR, and FSNBLR models in classifying diabetes mellitus cases at Hajj General Hospital Surabaya. All three models were estimated using the Maximum Likelihood Estimation (MLE) method. Since the resulting estimators do not have closed-form solutions, numerical iteration using the Newton-Raphson method was applied. Model performance was assessed using Area Under the Curve (AUC), accuracy, sensitivity, and specificity. The FSNBLR model outperformed both the BLR and BPR models. It achieved the highest AUC value of 81.86%, while BLR (66.30%) and BPR (66.30%). That is indicated FSNBLR superior discriminative ability. In addition, the FSNBLR model recorded higher accuracy, sensitivity, and specificity compared to the other two models. The FSNBLR model demonstrated better predictive performance in identifying diabetes mellitus cases, especially in scenarios involving nonlinear relationships between predictors and the outcome variable. These findings suggest that flexible semiparametric approaches offer greater effectiveness in medical classification tasks, particularly for chronic conditions like diabetes mellitus.
QUANTILE BASED PLS-SEM WITH WILD BOOTSTRAP Abdul Malik Balami; Bambang Widjanarko Otok; Santi Wulan Purnami
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1775-1790

Abstract

Partial Least Squares SEM (PLS-SEM) is the recommended technique for structural equation modeling (SEM), which assesses correlations between latent components concurrently, particularly for small samples and non-normal data. But because traditional PLS-SEM only calculates average correlations between constructs, it runs the risk of overlooking variances in the quantile distribution. Consequently, the creation of the Quantile PLS-SEM approach, which incorporates quantile regression, provides a means to examine correlations across the entire data distribution. To improve estimation, wild bootstrap is used to address heteroscedasticity issues and produce more reliable inferences. The purpose of this study is to develop and apply Quantile based PLS-SEM with Wild Bootstrap to analyze the gizi data status of the Indonesian population based on the Survey Status Gizi Indonesia 2024. The analysis's findings indicate that specific and sensitive interventions have a significant impact on the gizi status of different quantities.
Co-Authors Abdul Malik Balami Albertus Eka Putra Haryanto Darwis Darwis Iwan Kurniawan Muhammad Idham Kurniawan Muhammad Zulfadhli Riwi Dyah Pangesti Santi Wulan Purnami