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All Journal UNP Journal of Statistics and Data Science
Rosa Salsabila Azarine
Universitas Negeri Padang
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Comparison of Kernel and Spline Nonparametric Regression (Case Study: Food Security Index of Jambi Province 2023) Rosa Salsabila Azarine; Septrina Kiki Arisandi; Fadhilah Fitri; 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-iss3/397

Abstract

Food security is one of the issues that plays an important role in national development, especially in regions with varying levels of economic welfare such as Jambi Province. One of the main factors affecting food security is food expenditure, which reflects the economic capacity of households to access food. The complex and non-linear relationship between Food Security Index (FSI) and Food Expenditure requires a flexible modeling approach in the analysis. This study aims to compare the performance of nonparametric regression Kernel ans Spline regression methods, namely the Nadaraya-Watson Estimator (NWE) and Local Polynomial Estimator (LPE) for Kernel Regression as well as Smoothing Spline and B-Spline for Spline Regression. The analysis was conducted using secondary data obtained from the Food Security and Vulnerability Map (FSVA) of 2023, with a total of 141 subdistricts in Jambi Province. The response variable is the Food Security Index (FSI), while the predictor variable is Food Expenditure. Model evaluation was conducted using the Mean Squared Error (MSE) and the coefficient of determination (R²). The results showed that the NWE method had the best performance with the smallest MSE value of 24.47690 and the highest R² value of 0.3332, meaning that approximately 33.32% of the variation in FSI could be explained by Food Expenditure. The LPE method showed nearly comparable performance, while Smoothing Spline and B-Spline exhibited higher prediction error rates. Therefore, the NWE method can be recommended as an effective nonparametric regression approach for modeling the relationship between food expenditure and food security.
Evaluating Determinants Of Survival Time In Heart Failure Patients Using Cox Proportional Hazards Regression Fajri Juli Rahman Nur Zendrato; Tessy Octavia Mukhti; Sarmilah; Razita Nur Amalina; Rosa Salsabila Azarine
UNP Journal of Statistics and Data Science Vol. 4 No. 3 (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-iss3/477

Abstract

Heart failure represents a severe chronic cardiovascular disorder and remains a major contributor to global mortality rates. Identifying specific risk factors that impact patient survivability is crucial for enhancing clinical interventions. This research investigates the determinants influencing the survival duration of heart failure patients by applying the Cox Proportional Hazards (Cox PH) regression approach. The primary objective of this study is to provide information on the main causes of heart failure and the factors that can influence the condition. Utilizing a secondary dataset of 299 patient records sourced from Kaggle, the study analyzed several variables, including age, gender, anemia, diabetes, smoking habits, and hypertension. The analytical findings reveal that age serves as the most critical determinant; individuals older than 65 face a 2.04 times greater mortality risk than younger cohorts. Furthermore, comorbidities such as anemia and hypertension significantly elevate this risk, presenting hazard ratios of 1.44 and 1.46, respectively. These outcomes emphasize the critical role of age and pre-existing medical conditions in the prognosis of heart failure, offering valuable perspectives for medical practitioners to design targeted therapeutic strategies that prolong patient survival.
Co-Authors Fadhilah Fitri Fajri Juli Rahman Nur Zendrato Razita Nur Amalina Sarmilah Septrina Kiki Arisandi Tessy Octavia Mukhti