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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Pattimura Proceeding : Conference of Science and Technology
Sihombing, Esther Damayanti
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Binary Logistic Regression Modeling on Household Poverty Status in Bengkulu Province Sihombing, Esther Damayanti; Novianti, Pepi; Wahyuliani, Indah
Pattimura Proceeding Vol 5 No 1 (2024): Prosiding Konferensi Nasional matematika (KNM) XXII Tahun 2024
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ppcst.knmxxiiv5i1p89-100

Abstract

Binary logistic regression is a statistical method used to analyze the relationship between one or more predictor variables and a binary or dichotomous response variable. Poverty is an issue in every province in Indonesia. One of the provinces with a relatively high poverty rate is Bengkulu Province, ranking seventh in Indonesia with a poverty rate of 14.62%. The Central Bureau of Statistics of Bengkulu Province (2023) explains that efforts to reduce poverty must involve all levels of society. Various government programs and policies in various fields such as health, social, and other areas are continuously being implemented to reduce the number of households classified as poor. Identifying the characteristics of households in Bengkulu Province by poverty status is important to study, as it serves as a reference to ensure that government programs are implemented according to the target. One method that can be used to identify household characteristics is binary logistic regression. This study aims to model the poverty status of households in Bengkulu Province using binary logistic regression and to identify the factors that influence it. The data used are social and economic household data from March 2022. The response variable used is household poverty status (poor and not poor), while the predictor variables include the ownership of toilet facilities, the source of lighting, floor area, family size, and per capita calorie consumption. Modeling is done using binary logistic regression with simultaneous and partial parameter significance tests, as well as model fit tests. The analysis results show that the factors significantly influencing household poverty status in Bengkulu Province are the ownership of toilet facilities, the source of household lighting, floor area, family size, and per capita calorie consumption. The formed binary logistic regression model has a classification accuracy of 89.98% with a sensitivity of 18.34% and a specificity of 98.61%.
EVALUATION OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES ON IMBALANCED DATASET FOR POVERTY CLASSIFICATION IN BENGKULU PROVINCE Sriliana, Idhia; Nugroho, Sigit; Agwil, Winalia; Sihombing, Esther Damayanti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1143-1156

Abstract

Classification is a statistical method that aims to predict the class of an object whose class label is unknown. The Multivariate Adaptive Regression Splines (MARS) classification method is a classification model that involves several basis functions with influential predictor variables. The MARS classification model is generally effective in classifying imbalanced data, including poverty data classification. The response variable used is the poverty status of households classified into poor and non-poor households, and the predictor variables consist of several poverty indicators. The problem that often arises in classification methods is a class imbalance in the response variable. Due to the poverty status included in the class imbalance data, the Bootstrap Aggregating (Bagging) and Synthetic Minority Over-sampling Technique (SMOTE) approaches will be used to improve classification accuracy on the MARS model. Bagging works by replicating data to strengthen the stability of classification accuracy, while SMOTE works by synthesizing data from minority data classes. The evaluation results showed that the classification model of poverty in Bengkulu Province using the SMOTE-MARS method provides the best classification accuracy compared to the MARS (25.81%) and Bagging-MARS (32.26%) methods based on the sensitivity value obtained, which is 85.36%.
Co-Authors Agwil, Winalia Idhia Sriliana Pepi Novianti Sigit Nugroho Wahyuliani, Indah