Comparison of Basic Statistics and Machine Learning Classification Algorithms in Kalimantan Poverty Prediction with Handling Missing Data

Khikmah, Khusnia Nurul; Sofro, A'yunin Sofro

doi:10.20956/j.v22i1.44488

Open access Journal

Comparison of Basic Statistics and Machine Learning Classification Algorithms in Kalimantan Poverty Prediction with Handling Missing Data

Jurnal Matematika, Statistika dan Komputasi · Vol. 22 No. 1 (2025) · pp. 90–101

DOI: 10.20956/j.v22i1.44488
ISSN: 2614-8811, 1858-1382
Published: 2025-09-08
Publisher: Department of Mathematics, Hasanuddin University
Language: en
License: creativecommons.org/licenses/by/4.0

Abstract

Poverty is a crucial development challenge in Indonesia, including in regencies/cities in Kalimantan that require more attention. In reality, poverty is influenced by various factors. Therefore, this research proposes an analysis comparing the accuracy of basic and statistical machine learning models in predicting poverty rates and finding factors that affect poverty rates. The advance of this research is the performance comparison combined with the handling of missing data. The three models proposed in this study are binary logistic regression with backward stepwise selection, random forest, and extremely randomized trees (extra trees). The data used in this study is secondary data taken from the Indonesian Statistics (BPS) of five provinces in Kalimantan, where the pre-processing is done by handling missing data with a k-nearest neighbor (KNN). The results of the poverty prediction analysis show that the binary logistic regression model is the most accurate compared to random forest and extra trees, with a balanced accuracy of 75%. In addition, based on the best model with the highest accuracy, this study also found significant predictor variables that affect the poverty rate of regencies/cities in Kalimantan: population density, average years of schooling, and per capita expenditure on food.

Keywords

Collaboration network of the authors

17 authors · 53 co-authorship links · 11 across different organisations

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Universitas Negeri Surabaya
IPB University
Manchester Academic Health Science Centre
King Abdullah University of Science and Technology

Different organisations
Same organisation
Affiliation unknown
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Co-author	Organisation	Joint articles
Danang Ariyanto	Universitas Negeri Surabaya	7
Riska Wahyu Romadhonia	Universitas Negeri Surabaya	5
Affiati Oktaviarina	Universitas Negeri Surabaya	3
Dimas Avian Maulana	Universitas Negeri Surabaya	2
Rosalina Agista Riani	Universitas Negeri Surabaya	2
Framitha Septian Subiantoro	Universitas Negeri Surabaya	2
Asri Maharani	Manchester Academic Health Science Centre	2
Mutia Eva Mustafidah	Universitas Negeri Surabaya	2
Kusman Sadik	IPB University	1
INDAHWATI INDAHWATI	IPB University	1
A’idah Nur Hanifah	Universitas Negeri Surabaya	1
Junaidi Budi Prihanto	Universitas Negeri Surabaya	1
Mohammad Dian Purnama	Universitas Negeri Surabaya	1
Riskyana Dewi Intan Puspitasari	Universitas Negeri Surabaya	1
Hernando Ombao	King Abdullah University of Science and Technology	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en">Comparison of Basic Statistics and Machine Learning Classification Algorithms in Kalimantan Poverty Prediction with Handling Missing Data</dc:title>
	<dc:creator xml:lang="en">Khikmah, Khusnia Nurul</dc:creator>
	<dc:creator xml:lang="en">Sofro, A'yunin Sofro</dc:creator>
	<dc:subject xml:lang="en">Binary Logistic Regression</dc:subject>
	<dc:subject xml:lang="en">Extra Trees</dc:subject>
	<dc:subject xml:lang="en">Kalimantan</dc:subject>
	<dc:subject xml:lang="en">Poverty</dc:subject>
	<dc:subject xml:lang="en">Random Forest</dc:subject>
	<dc:description xml:lang="en">Poverty is a crucial development challenge in Indonesia, including in regencies/cities in Kalimantan that require more attention. In reality, poverty is influenced by various factors. Therefore, this research proposes an analysis comparing the accuracy of basic and statistical machine learning models in predicting poverty rates and finding factors that affect poverty rates. The advance of this research is the performance comparison combined with the handling of missing data. The three models proposed in this study are binary logistic regression with backward stepwise selection, random forest, and extremely randomized trees (extra trees). The data used in this study is secondary data taken from the Indonesian Statistics (BPS) of five provinces in Kalimantan, where the pre-processing is done by handling missing data with a k-nearest neighbor (KNN). The results of the poverty prediction analysis show that the binary logistic regression model is the most accurate compared to random forest and extra trees, with a balanced accuracy of 75%. In addition, based on the best model with the highest accuracy, this study also found significant predictor variables that affect the poverty rate of regencies/cities in Kalimantan: population density, average years of schooling, and per capita expenditure on food.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2025-09-08</dc:date>
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	<dc:identifier>10.20956/j.v22i1.44488</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 22 No. 1 (2025): SEPTEMBER 2025; 90-101</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
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	<dc:rights xml:lang="en">Copyright (c) 2025 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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