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All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) UNP Journal of Statistics and Data Science
Ervi Dayana Putri
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Medicine & Pharmacology Social Sciences

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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%.
Clustering of Regencies/Cities in NTT Province Based on Poverty Using the Ward and Fuzzy C-Means Methods Ervi Dayana Putri; Tessy Octavia Mukhti
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 14 No 1 (2026): VOLUME 14 No 1, 2026
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v14i1.62887

Abstract

Poverty remains a major problem in Indonesia's economic development. In Indonesia, East Nusa Tenggara Province has the fifth highest percentage of poor people in Indonesia as of September 2024. However, East Nusa Tenggara Province has also shown a decline in its poverty rate. According to a report from the Statistics Indonesia of Nusa Tenggara Timur Province, 18.60 percent of the province's population will be impoverished in 2024. This number is still significantly higher than Indonesia's 8.57 percent poverty rate. Additionally, East Nusa Tenggara Province's districts and cities have not benefited equally from the reduction in poverty because of notable regional differences. This study uses poverty data for East Nusa Tenggara Province in 2024 obtained from the Statistics Indonesia of Nusa Tenggara Timur Province and applies cluster analysis by comparing the Ward hierarchical cluster method and the non-hierarchical Fuzzy C-Means method to group regions based on poverty characteristics. The results of the study concluded that the Ward method with 3 clusters provided better results than the Fuzzy C-Means method. It is anticipated that the government will use these results as a foundation to concentrate more on creating more focused development plans for regions with the highest rates of poverty.
Co-Authors Adinda Putri Rahmatul Annisa Sepniza Nasywa Tessy Octavia Mukhti