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

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Survival Analysis of Heart Failure Patients Using the Cox Proportional Hazard Model and Weibull Regression Rahmika Alya; Tessy Octavia Mukhti; Sri Wahyuni; Bunga Miftahul Barokah; Azizah Apriyerni
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-iss2/351

Abstract

Cardiovascular disesase is the leading cause of death globally, claiming around 17,9 million lives each year, accounting for 31% of all deaths worldwide. Hearth failure is a common event caused by cardiovascular disease. Hearth failure is one of the principal health issues with excessive mortality and morbidity costs. Heart failure is the main reason of mortality worldwide. This take a look ambitions to analyze the factors influencing the survival of heart failure patients using the Cox proportional hazard Cox (PH) model and the Weibull regression. The main purpose of this study is to provide information on the causes of heart failure deaths and what effects occur when having heart disease. It is hoped that the results of this study can provide the general public to be more careful in order to prevent heart failure disease. The data used are secondary data from Kaggle consisting of 299 patients with the variables anemia, diabetes, hypertension, gender and smoking status. The analysis showed that only hypertension significantly increased the risk of events in both models, whereas other variables were not statistically significant. The selection of the best model is based on the assumptions of proportional hazard, flexibility, and Akaike information criterion (AIC) values. The Cox-PH model was chosen as the model of choice because it is more flexible and does not require certain fundamental assumptions regarding risk distribution. This study provides important insight into the risk factors that influence the prognosis of heart failure patients.
Analysis Analysis of The Influence of Job Resources and Leadership Quality on Job Satisfaction Using Structural Equation Modeling Azizah Apriyerni; Nisa Ulhusna; Rahmadani; Mira Meilisa
UNP Journal of Statistics and Data Science Vol. 4 No. 1 (2026): 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/vol4-iss1/469

Abstract

Job Satisfaction is a essential factor influencing employee performance, commitment, and organizational sustainability. Low levels of Job Resources and suboptimal Leadership Quality are common causes of decreased job satisfaction across various institutions. This study aims to analyze the effect of job resources and leadership quality on Jjob Satisfaction using the Structural Equation Modeling (SEM) method. The research data were obtained from a Likert-scale survey (1-8) consisting of three latent variabless and their respective indicators, and wer analyzed through Confirmatory Factor Analysis (CFA) and Structural Model assesment. The result of the CFA indicate that all indicators meet the criteria for validity and reliability, with factor loadings above 0.50, a Composite Reliability (CR) value of 0.9667, and an Average Variance Extracted (AVE) value of 0.6769. the Goodness of Fit evaluation shows that the final model is highly acceptable, as reflected by a low Chi-square/df value, RMSEA = 0.005, and CFI, TLI, GFI, and NFI value of 1.000. the Structural analysis further demonstrates that Job Resources have a positive and significant impact on Job Satisfaction. Simultaneously, both variables contribute significantly to explaining variations in Job Satisfaction. This study highlights that enhancing Job Resources and improving Leadership Quality are crucial strategies to strengthen employee Job Satisfaction. The findings provide empirical insight that can assist organizations in developing more effective and sustainable human resource management policies
Co-Authors Bunga Miftahul Barokah Mira Meilisa Nisa Ulhusna Rahmadani Rahmika Alya Sri Wahyuni Tessy Octavia Mukhti