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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Statistics and Its Applications
Anang Kurnia
School of Data Science, Mathematics, and Informatics, IPB University, Indonesia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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Bayesian Vector Autoregressive Modeling on Macroeconomic Variables in Indonesia Indra Mahib Zuhair Riyanto; Muhammad Firlan Maulana; Nur Anggraini Fadhilah; Laras Suprapti; Salsabila Fayiza; Eliza Rahmadania; Bulan Cahyani Suhaeri; Anang Kurnia; Laily Nissa Atul Mualifah; Aulia Akhrian Syahidi
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.v10i1p105-118

Abstract

This research studies a Bayesian Vector Autoregressive (BVAR) model to analyze the dynamic interactions among the rupiah exchange rate, exports, imports, gold futures prices, and inflation in Indonesia during the 2015-2024 period. The BVAR method was chosen to overcome the limitations of conventional VAR models on overparameterization problem by utilizing hierarchical Minnesota priors and Markov-Chain Monte Carlo (MCMC) estimation. Data were stationary through first order differencing and normalized using z-score. Lag selection based on the Akaike Information Criterion (AIC) showed that lag 6 is optimal. Model evaluation using Mean Absolute Percentage Error (MAPE) shows good overall model performance on training data, especially on the gold price variable (MAPE 10,09%) and inflation (MAPE 3,74%). On test data, the model struggles to perform well on prediction due to the high uncertainty of the test data period. Impulse Response Function (IRF) analysis is used to reveal short-term responses between variables, such as the effect of exchange rate depreciation on inflation and the impact of export value on a temporary decline in import value. The result highlights the BVAR model’s ability to capture general macroeconomic relationships, especially when many parameters need to be estimated and the available data is limited.
POISSON MIXED MODELS WITH A BOOSTING APPROACH FOR THE ANALYSIS OF COUNT DATA Ita Wulandari; Khairil Anwar Notodiputro; Bagus Sartono; Anwar Fitrianto; Anang Kurnia
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/barekengvol20iss1pp0815-0828

Abstract

Boosting is a powerful technique for enhancing predictive accuracy by iteratively reweighting observations, and is particularly effective in high-dimensional settings and for variable selection. While previous studies have demonstrated the advantages of integrating boosting with generalized linear mixed models (GLMMs) for binary outcomes, its application to count data within hierarchical frameworks remains limited. This study addresses that gap by extending boosting methods to count data through the development of a boosted Poisson mixed model (bPMM), a novel approach for small area estimation and variable selection in complex survey designs. The proposed model is applied to fertility data in the Indonesian provinces of Bali and East Nusa Tenggara, where the response variable is the number of live births and the predictors include twenty-eight socio-demographic covariates. Using the Akaike Information Criterion (AIC) for model selection, three significant variables were identified in Bali (Model 1), and one in East Nusa Tenggara (Model 2). The results demonstrate that bPMM not only improves variable selection in high-dimensional settings but also accommodates hierarchical structure in count data.
SURVIVAL ANALYSIS OF CHRONIC KIDNEY FAILURE PATIENTS USING THE COX STRATIFIED MODEL AND RANDOM SURVIVAL FOREST Assyifa Lala Pratiwi Hamid; Budi Susetyo; Anang Kurnia
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/barekengvol20iss2pp1527-1540

Abstract

This study aims to analyze the factors influencing the survival of chronic kidney failure patients undergoing hemodialysis and to compare the performance of the Cox Stratified Model with the Random Survival Forest (RSF) using retrospective data from 741 patients at Asy-Syifa General Hospital, Indonesia. Data were analyzed using the Cox Stratified Model to address violations of the proportional hazards assumption and RSF to capture non-linear patterns and complex interactions among variables. The results showed that age, hypertension, diabetes, anemia, and hemodialysis frequency significantly affected survival, with a C-Index of 0.66 for the Cox Stratified Model and 0.6558 for RSF. The limitations of this study include its single-center retrospective design, which may limit generalizability, potential residual confounding from unmeasured variables, as well as the interpretability limitations and higher computational demands of RSF. The originality of this research lies in the direct comparison between advanced statistical models and machine learning methods in a cohort of chronic kidney failure patients in Indonesia, providing new insights for improving risk stratification and clinical prediction.
Co-Authors Anwar Fitrianto Assyifa Lala Pratiwi Hamid Aulia Akhrian Syahidi Bagus Sartono Budi Susetyo Bulan Cahyani Suhaeri Eliza Rahmadania Indra Mahib Zuhair Riyanto Ita Wulandari Khairil Anwar Notodiputro Laily Nissa Atul Mualifah Laras Suprapti Muhammad Firlan Maulana Nur Anggraini Fadhilah Salsabila Fayiza