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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Dita Amelia
Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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MODELING AND SEGMENTATION OF FACTORS AFFECTING HUMAN DEVELOPMENT IN ISLANDS OF JAVA USING FIMIX PLS METHOD WITH MEDIATION EFFECT Muhammad Rosyid Ridho Az Zuhro; Ardi Kurniawan; Dita Amelia; Idrus Syahzaqi
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/barekengvol20iss1pp0397-0412

Abstract

Human development is a key indicator used to assess the quality of a country's human resources. Although Indonesia's HDI has experienced a significant increase of 75.02 in 2024, inequality is still a pressing issue, especially in terms of gender representation in the workforce. This study aims to identify the influence of poverty, economic, health, employment and education factors on human development in Java Island by considering gender equality as a mediating variable. The data used in the study is limited to 119 districts/cities in Java Island and sourced from BPS publications, the Health Office and the Education Office. The novelty of this study lies in the use of the Finite Mixture Partial Least Square (FIMIX-PLS) approach with mediation effects which is rarely applied in human development research in Indonesia, as well as allowing the identification of latent population heterogeneity and region-based segmentation. The results of this method reveal two distinct district/city segments in Java, with Segment 1 dominated by the variables in this study that have significant direct and indirect effects through the mediation of gender equality on human development, while Segment 2 has characteristics that emphasize the effect of gender equality. Given these differences in characteristics, it is important that contextual and regional segmentation-based development policies are designed by local and central governments. Statistical segmentation approaches such as FIMIX-PLS make a significant contribution to more targeted policy making. By changing the type of intervention according to specific problems, the government can allocate resources more effectively. This supports the achievement of SDG-10 in reducing inequality.
BAYESIAN ESTIMATION OF THE SCALE PARAMETER OF THE WEIBULL DISTRIBUTION USING THE LINEX AND ITS APPLICATION TO STROKE PATIENT DATA Tentri Ryan Rahmanita; Ardi Kurniawan; Elly Ana; Sediono Sediono; Dita Amelia
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/barekengvol20iss1pp0413-0426

Abstract

Survival analysis is used to study the timing of an event, such as recovery or death, in the context of medical data. One of the diseases that many people suffer from is stroke. Based on the survey results, the number of stroke sufferers in Indonesia reached 8.3% of 1000 people in Indonesia continues to increase every year, especially among the elderly. The research conducted aims to model the estimation of the type III censored Weibull distribution parameters with the Bayesian Linear Exponential Loss Function (LINEX) method. This study uses secondary data on stroke patients in the period January-November 2024 with a sample of 62 patients at the Haji Surabaya Regional General Hospital. Weibull distribution model with Bayesian approach using Linear Exponential Loss Function (LINEX) was applied to estimate the distribution parameters and survival function. The estimation results show that the parameter α is 6.32342 with an average hospitalization time of 5.9151646 days. MSE value is 0.000270555, which indicates that the estimation model is more accurate in predicting data for the length of hospitalization for stroke patients at the Haji Surabaya Regional General Hospital. The probability value of the survival function of stroke patients who have been hospitalized on the 5th day shows a probability of 82.4% so that no further hospitalization is needed, which indicates that the patient's health condition is improving. In addition, the hazard function analysis shows that the longer a patient is hospitalized, the greater the risk of the patient not recovering.
MODELING POVERTY SEVERITY INDEX IN EASTERN INDONESIA BASED ON NONPARARAMETRIC SPLINE TRUNCATED APPROACH FOR PANEL DATA Dita Amelia; Suliyanto Suliyanto; Najwa Khoir Aldawiyah; Kimberly Maserati Siagian; Nadinta Kasih Amalia Suryono; Nadya Lovita Hana Trisa
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp2919-2936

Abstract

Poverty severity remains a critical issue in Eastern Indonesia, where rates are consistently higher than in other regions. This study examines the Poverty Severity Index (P2) using parametric panel regression and nonparametric truncated splines for panel data across 17 provinces for the period 2020 to 2024. The predictor variables include per capita expenditure, mean years of schooling, and unmet need for health services. The secondary data were obtained from the official website of Central Bureau of Statistics (BPS). The parametric FEM produces a within R² of 36.9% and an MSE of 0.00912, which provides a baseline assessment of overall trends and global relationships among variables. In parallel, the first-order truncated spline model with two knot points which produces specific-province models, achieves an R² of 99.87% and an MSE of 0.00044. This model captures detailed province-specific patterns and nonlinearities and offers additional descriptive insight into regional variations in poverty severity. Together, these complementary approaches highlight both global and local dynamics and inform policy decisions that address economic, educational, and healthcare disparities in high-poverty regions especially in Eastern Indonesia.
VECTOR AUTOREGRESSION AND MULTIRESPONSE REGRESSION APPROACHES FOR MODELING GOLD, TIN, AND NICKEL PRICES Suliyanto Suliyanto; Dita Amelia; Gabriella Agnes Budijono; Rere Fetri Damanik
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp3241-3258

Abstract

The mining sector plays a strategic role in the global economy, especially during periods of high economic uncertainty. Between 2020 and 2024, global markets experienced severe volatility due to the COVID-19 pandemic, geopolitical tensions, energy price shocks, and monetary policy tightening. These conditions intensified price fluctuations in major mining commodities such as nickel, gold, and tin. However, limited empirical research has compared different multivariate modeling approaches for analyzing commodity price dynamics during this volatile period. This study examines the dynamic relationships between nickel, gold, and tin prices and key global factors, namely crude oil prices, the USD exchange rate, and silver prices, using monthly data from January 2020 to December 2024. The VAR model captures temporal interdependencies among variables, while the MRR model examines simultaneous relationships among multivariate response variables. The stationarity and cointegration tests show that all variables become stationary after first differencing and exhibit no long-term equilibrium relationship. The Impulse Response Function (IRF) and Variance Decomposition (VDC) analyses reveal that fluctuations in nickel, gold, and tin prices are primarily driven by their own past values, with minor cross-commodity effects. The MRR results indicate that crude oil and silver prices significantly influence metal price variations, while the USD exchange rate has the strongest overall effect. The comparison across three evaluation metrics shows that the MRR model provides better predictive performance than the VAR model. The MRR model yields higher R² than VAR, which records R² of 0.339, 0.584, and 0.529, with MAPE up to 22.42%. The results demonstrate that the MRR model consistently outperforms the VAR model, providing stronger explanatory power and higher predictive accuracy. These findings highlight the added methodological value of comparing VAR and MRR models and offer practical insights for investors, industry stakeholders, and policymakers in managing commodity price risk under volatile economic conditions.
Co-Authors Ardi Kurniawan Elly Ana Gabriella Agnes Budijono Idrus Syahzaqi Kimberly Maserati Siagian Muhammad Rosyid Ridho Az Zuhro Nadinta Kasih Amalia Suryono Nadya Lovita Hana Trisa Najwa Khoir Aldawiyah Rere Fetri Damanik Sediono, Sediono Suliyanto Tentri Ryan Rahmanita