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All Journal Parameter: Journal of Statistics MATHunesa: Jurnal Ilmiah Matematika
Andi Isna Yunita
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODELING ON LIFE EXPECTANCY RATE IN SOUTH SULAWESI Nabila Miftakhurriza; Jelita Zalzabila; Siswanto; Kalondeng, Anisa; Andi Isna Yunita; Ania, Samsir Aditya
Parameter: Journal of Statistics Vol. 4 No. 2 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i2.17267

Abstract

Geographically Weighted Panel Regression (GWPR) is one of the panel data regression approaches used in spatial data analysis. This study uses the global Fixed Effect Model (FEM) panel regression model and the local GWPR model to examine Life Expectancy Rate (LER) at the district/city level in South Sulawesi Province in 2019-2021. LER is an important indicator that reflects the health and welfare of the community. This research aims to develop a GWPR model that can explain variations in LER and identify factors that affect that variable, so that it can help stakeholders in allocating resources and designing effective intervention programs. Parameter estimation in the GWPR model is carried out in each observation area using the Weighted Least Square (WLS) method. The calculation of spatial weights in the GWPR model used weighting functions such as fixed bi-square, fixed gaussian, fixed exponential, adaptive bi-square, adaptive gaussian, and adaptive exponential. The results showed that the use of a fixed exponential weighting function gave optimal results with the lowest cross-validation (CV) value of 44,614. Parameter analysis of the GWPR model shows that the factors that affect LER are local and not the same in each district/city in South Sulawesi Province. Factors that have a significant influence include the number of health facilities and households that have access to proper sanitation. This GWPR model has a coefficient of determination of 97,7%. The FEM model has a coefficient of determination of 58,4%. Therefore, GWPR performs LER modelling more effectively than FEM.
COMPARISON OF THE PERFORMANCE OF NAÏVE BAYES AND CORRELATED NAÏVE BAYES METHODS WITH THE APPLICATION OF SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE Radia Sultan; Siswanto; Andi Isna Yunita
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

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Abstract

Classification is the process of creating a model to recognize patterns with the aim of mapping them into specific classes and predicting classes. Naive Bayes is a popular, simple and effective classification method with a probabilistic approach based on Bayes' Theorem. The assumption of independence in this method sometimes makes the classification performance decrease. Correlated naïve bayes corrects this assumption by considering attribute correlations, while SMOTE is used to overcome data imbalances. This approach is important in medical data analysis, one of which is predicting ischemic heart disease. This study aims to compare the performance of Naïve Bayes and Correlated Naïve Bayes methods in the classification of ischemic heart disease, with the application of SMOTE to overcome data imbalance. The analysis was carried out using ischemic heart disease data at the Integrated Heart Center of Dr. Wahidin Sudirohusodo Hospital, Makassar City, for the period of July 2021 to July 2022. Naïve Bayes managed to classify 66 data with 75% accuracy, 94% precision, and 62% sensitivity. Meanwhile, Correlated Naïve Bayes showed better performance by correctly classifying 77 data, resulting in 87.5% accuracy, 86% precision, and 94% sensitivity. These results show that Correlated Naïve Bayes has a superior performance in classifying ischemic heart disease.
Co-Authors Ania, Samsir Aditya Jelita Zalzabila Kalondeng, Anisa Nabila Miftakhurriza Radia Sultan Siswanto