DOI: https://doi.org/10.14710/j.gauss.13.1.145-155

KLASIFIKASI STATUS RUMAH TANGGA DI PROVINSI BENGKULU MENGGUNAKAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS)

Esther Damayanti Sihombing  -  Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Bengkulu, Indonesia
*Idhia Sriliana  -  Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Bengkulu, Indonesia
Dyah Setyo Rini  -  Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Bengkulu, Indonesia
Open Access Copyright 2024 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
Poverty is a global issue that captures the attention of governments in any country because it is a complex population-related problem. Poverty is a high-dimensional case, involving numerous predictor variables that interact with each other. This study was conducted to obtain a model that is capable of classifying household in Bengkulu Province. Multivariate Adaptive Regression Spline (MARS) is one of the methods used for classification of high-dimensional data. The MARS model is performed with combining Maximum Base Function (BF), Minimal Observation (MO), and Maximum Interaction (MI) with a small Generalized Cross Validation (GCV) by trial and error.  The data used in this study is data from the 2022 National Socioeconomic Survey sourced from the Central Statistics Agency of Bengkulu Province. The variables used is the proverty status of households classified as poor and not poor households as a response variable as well as several predictor variables. The results of this study indicate that it produces a MARS model with a combination of Basis Function (BF) = 48, Maximum Interaction (MI) = 3, and Minimum Observation (MO) = 0 which has the minimum GCV criteria of 0.06799. The results of the accuracy evaluation of the classification obtained an accuracy of 91.65%.
Fulltext View|Download
Keywords: Poverty, Classification, MARS

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
PERAMALAN HARGA GULA PASIR MENGGUNAKAN VARIASI KALENDER REGARIMA DENGAN MOVING HOLIDAY EFFECT (PERIODE JANUARI 2018 SAMPAI DENGAN DESEMBER 2022 DI PASAR KOTA SEMARANG) PENGELOMPOKAN KABUPATEN/KOTA DI PULAU KALIMANTAN BERDASARKAN INDIKATOR PERUMAHAN DAN KESEHATAN LINGKUNGAN MENGGUNAKAN METODE FUZZY GUSTAFSON-KESSEL PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH BERDASARKAN INDIKATOR KESEHATAN BAYI DAN BALITA MENGGUNAKAN ALGORITMA FUZZY C-MEANS DAN K-MEDOIDS More recent articles
  1. Agresti, A. 2007. An Introduction to Caterogical Data Analysis. New Jersey: John Wiley & Sons
  2. Cox, D.R., & Snell, E.J. 2018. Analysis of Binary Data. Boca Raton (US) : Chapman & Hall/CRC
  3. Eubank, R.L. 1999. Spline Smoothing and Nonparametric Regression. New York: Marcel Dekker
  4. Friedman, J.H. 1991. Multivariate Adaptive Regression Splines. The Annals of Statistics, 19(1), 1-67
  5. Gujarati, D.N. 2022. Basic Econometrics. Prentice Hall
  6. Han, J., & Kamber, M. 2006. Data Mining Concepts and Techniques Second Edition. San Francisco: Morgan Kaufmann
  7. Hastie, T., Tibshirani, R., & Friedman, J.H. 2009. The Elements of Statistical Learning: Data Mining, Inference, And Prediction (Vol. 2). New York: Springer
  8. Hickel, J. 2016. The True Extent of Global Poverty and Hunger: Questioning The Good News Narrative Of The Millennium Development Goals. Third World Quarterly, 37(5), 749-767
  9. Hosmer, D.W. & Lemeshow, S. 2000. Applied Logistic Regression. USA: John Willey and Sons
  10. Johnson, R.A., & Wichern, D.W.1992. Applied Multivariate Statistical Analysis. New Jersey: Prentice Hall
  11. Kriner, M. 2007. Survival Analysis with Multivariate Adaptive Regression Splines Disertation. Germany (GR): Munchen University
  12. Morton, R., Hebel, J., & McCarter, R. 2008. A Study Guide to Epidemiology and Biostatistics. Sudbury: Jones and Bartlett Publishers, Inc
  13. Surya, D.W.T. 2016. Perbandingan Ketepatan Klasifikasi Metode Regresi Logistik Biner dan Multivariate Adaptive Regression Splines Pada Status Stroke Pasien (Studi Kasus: Rsud Dr. H. Slamet Martodirdjo Pamekasan Tahun 2015). Disertasi. Surabaya: Institut Teknologi Sepuluh Nopember
  14. Sheskin, D.J. 2011. Handbook of Parametric and Nonparametric Statistical Procedures Fifth
  15. Tamonob, O. 2020. Analisis Multivariate Adaptive Regression Splines (MARS) Untuk Mengklasifikasikan Status Desa Di Provinsi Nusa Tenggara Timur. Skripsi. Bogor: Institut Bogor Pertanian
  16. Zhang, H. 1997. Multivariate Adaptive Splines for Analysis Of Longitudinal Data. Journal of Computational and Graphical Statistics, 6(1), 74-91

Last update:

No citation recorded.

Last update:

No citation recorded.