ANALYSIS OF FACTORS AFFECTING HOUSEHOLD POVERTY IN KOTAYASA VILLAGE THROUGH A BINARY LOGISTIC REGRESSION APPROACH
Main Article Content
Abstract
Kotayasa Village is a village that has the highest number of poor households in Banyumas Regency with 1,662 poor households (78%). This study aims to determine which factors have a significant effect and interpret the binary logistic regression model for poor households in Kotayasa Village. The data used in this research is secondary data from the Office of Social and Community and Village Empowerment in Banyumas Regency. The results showed that the factors that had a significant effect on poor households in Kotayasa Village were electricity, gold savings and livestock. Meanwhile, factors that have no significant effect are sources of drinking water and private vehicles. The results of the analysis obtained the odds ratio values, namely electric power of 3,999, gold deposits of 7,963, and livestock of 1,497. The electric power variable shows that households with electric power less than equal to 900 have a greater chance of being included in poor households of 3,999. The gold savings variable is that households that do not have gold savings have a greater chance of being included in poor households of 7,963. The livestock variable shows that households that do not have livestock have a greater chance of being included in poor households of 1,497.
Keywords: Odds ratio, binary logistic regression, poor household.
Keywords: Odds ratio, binary logistic regression, poor household.
Downloads
Download data is not yet available.
Article Details
How to Cite
Tripena, A., Lianawati, Y., & Setyawan, A. (2023). ANALYSIS OF FACTORS AFFECTING HOUSEHOLD POVERTY IN KOTAYASA VILLAGE THROUGH A BINARY LOGISTIC REGRESSION APPROACH. Electro Luceat, 9(2), 43-58. https://doi.org/10.32531/jelekn.v9i2.708
Section
Articles
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
DAFTAR PUSTAKA
[1]. Agresti, A. (2002). Categorical Data Analysis, Second Edition. New York: John
Willey & Sons.
[2]. Badan Pusat Statistik. (2008). Analisis dan Perhitungan Tingkat Kemiskinan Tahun 2008.
[3]. Badan Pusat Statistik. (2022). Data dan Informasi Kemiskinan Kabupaten/Kota di Indonesia.
[4]. Badan Pusat Statistik (2021). Statistik Indonesia 2021
[5]. Bain, L., dan Engelhardt. (1992). Introduction to Probability and Mathematical Statistics. Belmonf California: Duxbury Presss An Imprint of Wadsworth Publishing Company
[6]. Bhinadi, A. (2017). Penanggulangan Kemiskinan dan Pemberdayaan Masyarakat. Yogyakarta: Deepublish.
[7]. Hosmer, D. W., Lemeshow, S., dan Sturdivant, R. X. (2013). Applied Logistic Regression Third Edition. New York: John Willey and Sons Inc.
[8]. Miftahuddin. (2011). Analisa Karakteristik Rumah Tangga Miskin dengan Metode
Regresi Logistik Terbaik. Jurnal Matematika, Statistika, dan Komputasi, 7, 79-81.
[9]. Nisva, T.M.T, dan Ratnasari, V. (2020). Analisis Regresi Logistik Biner pada Faktor-Faktor yang Mempengaruhi Jenis Perceraian di Kabupaten Lumajang. Jurnal Inferensi, 3(1).
[10]. Permatasari, V. S., dan Yuliana, L. (2020). Penerapan Regresi Logistik Biner pada Status Kesejahteraan Rumah Tangga di Provinsi Bali Tahun 2020. Jurnal Politeknik Statistika STIS.
[11]. Santi, N. D., Mumtaz, T., Fatmawati, A. D., dan Retnosari, L. (2022). Perhitungan dan Analisis Kemiskinan Makro Indonesia Tahun 2022. Badan Pusat Statistik..
[12]. Setyawan, D. A. (2021). Modul Hipotesis dan Variabel Penelitian. Tahta Media.
[13]. Ludeman, L. C.. 1987. Fundamental of Digital Signal Processing. Singapore : John Wiley & Sons, Inc.
[1]. Agresti, A. (2002). Categorical Data Analysis, Second Edition. New York: John
Willey & Sons.
[2]. Badan Pusat Statistik. (2008). Analisis dan Perhitungan Tingkat Kemiskinan Tahun 2008.
[3]. Badan Pusat Statistik. (2022). Data dan Informasi Kemiskinan Kabupaten/Kota di Indonesia.
[4]. Badan Pusat Statistik (2021). Statistik Indonesia 2021
[5]. Bain, L., dan Engelhardt. (1992). Introduction to Probability and Mathematical Statistics. Belmonf California: Duxbury Presss An Imprint of Wadsworth Publishing Company
[6]. Bhinadi, A. (2017). Penanggulangan Kemiskinan dan Pemberdayaan Masyarakat. Yogyakarta: Deepublish.
[7]. Hosmer, D. W., Lemeshow, S., dan Sturdivant, R. X. (2013). Applied Logistic Regression Third Edition. New York: John Willey and Sons Inc.
[8]. Miftahuddin. (2011). Analisa Karakteristik Rumah Tangga Miskin dengan Metode
Regresi Logistik Terbaik. Jurnal Matematika, Statistika, dan Komputasi, 7, 79-81.
[9]. Nisva, T.M.T, dan Ratnasari, V. (2020). Analisis Regresi Logistik Biner pada Faktor-Faktor yang Mempengaruhi Jenis Perceraian di Kabupaten Lumajang. Jurnal Inferensi, 3(1).
[10]. Permatasari, V. S., dan Yuliana, L. (2020). Penerapan Regresi Logistik Biner pada Status Kesejahteraan Rumah Tangga di Provinsi Bali Tahun 2020. Jurnal Politeknik Statistika STIS.
[11]. Santi, N. D., Mumtaz, T., Fatmawati, A. D., dan Retnosari, L. (2022). Perhitungan dan Analisis Kemiskinan Makro Indonesia Tahun 2022. Badan Pusat Statistik..
[12]. Setyawan, D. A. (2021). Modul Hipotesis dan Variabel Penelitian. Tahta Media.
[13]. Ludeman, L. C.. 1987. Fundamental of Digital Signal Processing. Singapore : John Wiley & Sons, Inc.