DOI: https://doi.org/10.14710/medstat.16.2.170-181

PER CAPITA CONSUMPTION ESTIMATION IN SURABAYA USING ENSEMBLE MODEL APPROACH

*Sutikno Sutikno  -  Department of Statistics, Institut Teknologi Sepuluh Nopember, Jl. Teknik Mesin No. 175, Keputih, Kec. Sukolilo, Kota Surabaya, Jawa Timur 60115, Indonesia
Jerry Dwi Trijoyo Purnomo  -  Department of Statistics, Institut Teknologi Sepuluh Nopember, Jl. Teknik Mesin No. 175, Keputih, Kec. Sukolilo, Kota Surabaya, Jawa Timur 60115, Indonesia
Unggul Harfianto  -  Department of Statistics, Institut Teknologi Sepuluh Nopember, Jl. Teknik Mesin No. 175, Keputih, Kec. Sukolilo, Kota Surabaya, Jawa Timur 60115, Indonesia
Yoga Prastya Irfandi  -  Department of Statistics, Institut Teknologi Sepuluh Nopember, Jl. Teknik Mesin No. 175, Keputih, Kec. Sukolilo, Kota Surabaya, Jawa Timur 60115, Indonesia
Kartika Nur Anisa  -  Department of Statistics, Institut Teknologi Sepuluh Nopember, Jl. Teknik Mesin No. 175, Keputih, Kec. Sukolilo, Kota Surabaya, Jawa Timur 60115, Indonesia
Fajar Dwi Cahyoko  -  Department of Statistics, Institut Teknologi Sepuluh Nopember, Jl. Teknik Mesin No. 175, Keputih, Kec. Sukolilo, Kota Surabaya, Jawa Timur 60115, Indonesia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
The categorization of the Low-Income Community category is based on the poverty indicators in the Multidimensional Poverty Index, including the dimensions of health, education, and living standards. The Proxy Means Test (PMT) can estimate household income or consumption by taking into account household conditions that are readily observable and cannot be manipulated. This method offers the advantage of being capable of determining both the poverty level of a household and the household's characteristics based on asset ownership and socio-demographic conditions. This study aims to estimate per capita consumption using OLS, Robust, Quantile, LASSO, and Ensemble methods. The application of these methods is intended to address various issues, including the presence of outlier data, multicollinearity, and uncertainties. The results indicate that none of the four methods used achieved the highest accuracy based on the MSE, MAE, and sMAPE criteria. Consequently, employing an ensemble model becomes essential to accommodate the element of uncertainty present in these four models. The application of the ensemble method is not only as a comparison between the models, but also as a means to capture the uncertainty contained in each model

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Keywords: Proxy Mean Test; percapita consumption; ensemble model

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