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All Journal UNP Journal of Statistics and Data Science
Sepniza Nasywa
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Binary Logistic Regression to Factors Affecting Unmet Need for Limiting in East Java, Indonesia Sri Wahyuni; Yenni Kurniawati; Sepniza Nasywa; Ardiyatul Putri
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss2/353

Abstract

East Java, Indonesia's second most populated province, is anticipated to see significant annual population growth in the future, potentially resulting in a population explosion. The elevated birth rate facilitates the swift increase in population size. The unmet need for knowledge-based information among women of reproductive age has posed obstacles for the execution of family planning initiatives aimed at reducing birth rates. This study used binary logistic regression to identify the factors affecting the unmet demand for family planning among women of reproductive age in East Java Province in 2017.The investigation revealed that the woman's age, employment status, and husband's educational level significantly influenced the unmet need for constraint. Moreover, women aged 15-24 who are unemployed, lack schooling, have an illiterate partner, and reside in rural regions are more prone to experiencing an unmet need for contraception. Women aged 15-19 years compared to women aged 45-49 years were at 3,182 times higher risk of having an unmet need for family planning compared to a met need for family planning. Women aged 20-24 years compared to women aged 45-49 years were at 1,316 times higher risk of having an unmet need for family planning compared to a met need for family planning. Women who did not work compared to women who worked were 1,311 times more likely to have an unmet need for family planning compared to a met need for family planning. The binary logistic analysis model that was formed provided a good accuracy of 92,135% in predicting
Mortality Trends in Heart Failure Patients : A Study Using Cox Regression Models: Tren Mortalitas pada Pasien Gagal Jantung: Sebuah Studi Menggunakan Model Regresi Cox Ervi Dayana Putri; Tessy Octavia Mukhti; Rahmatul Annisa; Adinda Putri; Sepniza Nasywa
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss2/359

Abstract

Heart failure is classified as a cardiovascular disease, which is the leading cause of death worldwide. In Indonesia, heart failure has a high mortality rate, which in 2019 became the second leading cause of death after stroke. One method that can be used to examine the factors affecting mortality in heart failure patients is the cox proportional hazards regression. Cox proportional hazards regression is one of the most commonly used methods for analyzing survival data to date. The study data consisted of 299 observations involving 5 predictor variables, such as age, serum creatinine, serum sodium, high blood pressure, and diabetes. The conclusion of the analysis indicates that the variables of age, serum creatinine, serum sodium, and high blood pressure are significant. High blood pressure and serum creatinine are the factors that most affect the death of heart failure patients. Patients with high blood pressure have a 56,71% higher risk of death than patients without high blood pressure, and every 1 mg/dL in creatinine in the blood, the risk of death for heart failure patients will increase by 29,77%.
Applying Robust Spatial Autoregressive Model to Analyze the Determinants of Open Unemployment in West Java Berliana Nofriadi; Suci Rahmadani; Sepniza Nasywa; Tessy Octavia Mukhti; Yenni Kurniawati
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/402

Abstract

Open unemployment is a critical macroeconomic challenge in developing regions like West Java, Indonesia, where spatial disparities and data anomalies complicate traditional analysis. This study addresses these limitations by employing a Robust Spatial Autoregressive (RSAR) model with M-Estimator, integrating spatial dependence and outlier resilience to enhance estimation accuracy. Using 2024 district-level data from Indonesia’s Central Bureau of Statistics (BPS) and Open Data Jabar, the research examines determinants such as labor force participation, education, and regional GDP. The methodology begins with Ordinary Least Squares (OLS) to identify initial predictors, followed by spatial diagnostics (Moran’s I, Lagrange Multiplier tests) to confirm spatial autocorrelation. A customized Queen contiguity weight matrix captures neighborhood effects, while robust M-Estimation mitigates outlier distortions. Results reveal that the RSAR model achieves superior explanatory power (R² = 0.8626) compared to OLS and standard Spatial Autoregressive (SAR) models, with labor force participation (X₄) emerging as a significant negative predictor of unemployment. Spatial effects (ρ = 0.337) though modest, underscore the importance of inter-regional dynamics. The study concludes that RSAR offers a more reliable framework for regional labor analysis, combining spatial rigor with robustness against data irregularities. Policy-wise, the findings advocate targeted interventions to boost labor participation and address localized disparities, emphasizing the need for spatially informed, outlier-resistant methodologies in economic planning.
Co-Authors Adinda Putri Ardiyatul Putri Berliana Nofriadi Ervi Dayana Putri Rahmatul Annisa Sri Wahyuni Suci Rahmadani Tessy Octavia Mukhti Yenni Kurniawati