Klasifikasi Status Kemiskinan Rumah Tangga Berdasarkan Karakteristik Demografi dan Hunian Menggunakan Algoritma Clasification and Regression Tree

Aji Pandu Winata; Dinda Aprilia; Idhia Sriliana; Fitri Aryati; Reny Puspasari

doi:10.37905/euler.v13i3.34952

Klasifikasi Status Kemiskinan Rumah Tangga Berdasarkan Karakteristik Demografi dan Hunian Menggunakan Algoritma Clasification and Regression Tree

Aji Pandu Winata, Dinda Aprilia, Idhia Sriliana, Fitri Aryati, Reny Puspasari

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.

Keywords

Poverty; Household; CART

Full Text:

PDF

References

Badan Pusat Statistik Provinsi Bengkulu, “Profil Kemiskinan Provinsi Bengkulu: Maret 2024 (Berita Resmi Statistik No. 41/07/17/Th. XVIII),” BPS Provinsi Bengkulu, Jul. 1, 2024.

L. Priseptian and W. P. Primandhana, “Analisis Faktor-Faktor yang Mempengaruhi Kemiskinan,” FORUM EKONOMI: Jurnal Ekonomi, Manajemen dan Akuntansi, vol. 24, no. 1, Jan. 2022, doi: 10.30872/jfor.v24i1.10362.

M. Adon and G. T. Jeraman, “Masyarakat Madani: Kontribusi Teori Kemiskinan sebagai Deprivasi Kapabilitas dari Amartya Sen dalam Upaya Pemberdayaan Masyarakat Miskin,” Jurnal Masyarakat Madani, vol. 8, no. 1, 2023.

Z. A. Pradana and E. Pasaribu, “Pengaruh Pendidikan, PDRB, dan Pendapatan Asli Daerah Terhadap Tingkat Kemiskinan di Provinsi Bengkulu,” Oikos-Nomos Jurnal Kajian Ekonomi dan Bisnis, vol. 16, no. 2, pp. 55–62, Dec. 2023. doi: 10.37479/jkeb.v16i2.23072.

D. Fitriza, Atus Amadi Putra, Dodi Vionanda, and Zilrahmi, “Classification of Poor Households in West Sumatra Province using Decision Tree Algorithm C4.5,” UNP Journal of Statistics and Data Science, vol. 2, no. 2, pp. 145–150, May 2024, doi: 10.24036/ujsds/vol2-iss2/157.

D. Ispriyanti, A. Prahutama, M. Mustafid, and T. Tarno, “Klasifikasi Penerimaan Beras Miskin Di Kota Semarang Menggunakan Algoritma Chisquare Automatic Interaction Detection (Chaid) Dan Classification And Regression Tree (Cart),” MEDIA STATISTIKA, vol. 12, no. 1, p. 63, Jul. 2019, doi: 10.14710/medstat.12.1.63-72.

N. Indah Prabawati, Widodo, and H. Ajie, “Kinerja Algoritma Classification And Regression Tree (Cart) dalam Mengklasifikasikan Lama Masa Studi Mahasiswa yang Mengikuti Organisasi di Universitas Negeri Jakarta,” PINTER : Jurnal Pendidikan Teknik Informatika dan Komputer, vol. 3, no. 2, pp. 139–145, Dec. 2019, doi: 10.21009/pinter.3.2.9.

L. Nuzula, A. Prahutama, and A. R. Hakim, “Klasifikasi Status Kemiskinan Rumah Tangga dengan Metode Support Vector Machines (SVM) dan Classification and Regression Trees (CART) Menggunakan GUI R (Studi Kasus di Kabupaten Wonosobo Tahun 2018),” Jurnal Gaussian, vol. 9, no. 4, pp. 525–534, Dec. 2020, doi: 10.14710/j.gauss.v9i4.29449.

D. A. Wulandari, D. Kusnandar, and Y. Intisari, “Bagging Classification Trees Untuk Klasifikasi Tingkat Kesejahteraan Rumah Tangga Di Kalimantan Barat,”, Buletin Ilmiah Math. Stat. dan terapannya (Bimaster), vol.8, no.4 2019. doi: 10.26418/bbimst.v8i4.36035

H. D. Darmawan, D. Yuniarti, and Y. N. Nasution, “Klasifikasi Lama Masa Studi Mahasiswa Menggunakan Perbandingan Metode Algoritma C.45 dan Algoritma Classification and Regression Tree Comparison of C4.5 Algorithm and Classification and Regression Tree Algorithm In The Classification of Study Period of Undergraduate Students,” Jurnal EKSPONENSIAL, vol. 8, no. 2, 2017.

Z. Cheng et al., “Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy,” Adv Radiat Oncol, vol. 3, no. 3, pp. 346–355, Jul. 2018, doi: 10.1016/j.adro.2017.11.006.

T. H. Wu, P. Y. Chen, C. C. Chen, M. J. Chung, Z. K. Ye, and M. H. Li, “Classification and Regression Tree (CART)-based estimation of soil water content based on meteorological inputs and explorations of hydrodynamics behind,” Agric Water Manag, vol. 299, Jun. 2024, doi: 10.1016/j.agwat.2024.108869.

P. Subarkah, “Penerapan Algoritme Klasifikasi Classification And Regression Trees (Cart) Untuk Diagnosis Penyakit Diabetes Retinopathy,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 19, no. 2, pp. 294–301, May 2020, doi: 10.30812/matrik.v19i2.676.

F. S. Aguiar, L. L. Almeida, A. Ruffino-Netto, A. L. Kritski, F. C. Q. Mello, and G. L. Werneck, “Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients,” BMC Pulm Med, vol. 12, Aug. 2012, doi: 10.1186/1471-2466-12-40.

U. Hasanah and A. Nurhopipah, “Prediksi Persentase Body Fat Menggunakan Algoritma CART dan M5’,” JTIM : Jurnal Teknologi Informasi dan Multimedia, vol. 4, no. 4, pp. 351–363, Feb. 2023, doi: 10.35746/jtim.v4i4.316.

R. R. Adhitya, Wina Witanti, and Rezki Yuniarti, “Perbandingan Metode Cart Dan Naïve Bayes Untuk Klasifikasi Customer Churn,” INFOTECH journal, vol. 9, no. 2, pp. 307–318, Jul. 2023, doi: 10.31949/infotech.v9i2.5641.

A. Mahmud, A. Pangestika, A. P. Ramadhanty, G. M. Putra, G. S. N. D. S. Putri, and R. Nooraeni, “Klasifikasi Status Desa/Kelurahan DIY (Yogyakarta) Menggunakan Model Decision Tree (Studi Kasus Data Praktik Kerja Lapangan Politeknik Statistika STIS Tahun 2020),” Engineering, MAthematics and Computer Science (EMACS) Journal, vol. 3, no. 1, pp. 33–41, Feb. 2021, doi: 10.21512/emacsjournal.v3i1.6787.

DOI: https://doi.org/10.37905/euler.v13i3.34952