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All Journal Jurnal Gaussian Indonesian Journal of Mathematics and Natural Sciences Teknosains: Media Informasi Sains dan Teknologi GRADIEN SEMIRATA 2015 BAREKENG: Jurnal Ilmu Matematika dan Terapan Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Bumi Raflesia Jurnal Berdaya Mandiri Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS Inferensi Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Journal Of Human And Education (JAHE) Jurnal Matematika Integratif Limits: Journal of Mathematics and Its Applications
Idhia Sriliana
Universitas Bengkulu
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Klasifikasi Status Kemiskinan Rumah Tangga Berdasarkan Karakteristik Demografi dan Hunian Menggunakan Algoritma Clasification and Regression Tree Winata, Aji Pandu; Aprilia, Dinda; Sriliana, Idhia; Aryati, Fitri; Puspasari, Reny
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.34952

Abstract

Poverty is a multidimensional issue that remains a central focus of development in Bengkulu Province. Decision-tree–based approaches, particularly the Classification and Regression Tree (CART), can be used to classify households based on demographic and housing characteristics. This study utilizes data from the 2024 National Socio-Economic Survey (SUSENAS), with household poverty status as the response variable and six predictor variables including area type, gender of household head, age of household head, number of household members, floor area of the house, and housing ownership status. The analysis consists of data preprocessing, descriptive statistics, data splitting, CART model construction, identification of influential variables, and model evaluation using a confusion matrix. The results show that the number of household members, floor area of the house, and age of the household head are the most influential variables in distinguishing poor and non-poor households. Model evaluation produced an accuracy of 0.73, sensitivity of 0.58, and specificity of 0.75. The accuracy and specificity values indicate adequate classification performance, while the low sensitivity suggests that the model is still less optimal in detecting poor households, partly due to class imbalance in the dataset. These findings indicate that the CART method can be applied to poverty analysis in Bengkulu Province, although further model improvement is needed to enhance its capability in identifying poor households.
Co-Authors Abdul Aziz, Ali Agwil, Winalia Andini, Agita Aprilia, Dinda Aryati, Fitri Baki Swita Cinta Rizki Oktarina Dian Agustina Dian Agustina Dian Agustina Dwi Lorenza, Kenny Dyah Pangesti, Riwi Dyah Setyo Rini Ermawati Ermawati Etis Sunandi Fadila, Risfa Famuji, Ahmad Fhaeza, Veronnica Noer Hayadi, Ilham Jose Rizal Pepi Novianti Rafflesia, U Reny Puspasari, Reny Reza Pahlepi, Reza Rini, Dyah Setyo Riry Sriningsih Setyo Rini, Dyah Sigit Nugroho Sihombing, Esther Damayanti Sunandi, Etis Sunandi, Etis Supianti, Filo Susi Wijuniamurti Swita, Baki Ulfasari Rafflesia Ulfasari Rafflesia Winata, Aji Pandu