Classification of Poverty in Maluku Province using SMOTE-Random Forest Algorithm

Poverty is a complex issue. According to BPS publications, in 2023, the poverty line in Indonesia has reached 9.57%. Maluku is one of the provinces with a high poverty rate, reaching 16.23%. This research aims to classify poverty status in Maluku Province using the SMOTE-random forest algorithm. This research uses SUSENAS 2022 data, where the data is not balanced. SMOTE is used to overcome this problem. The best model obtained has an accuracy rate of 85.8%. The model is based on a training data proportion of 75% and testing 25%, with parameters m=4 and r=100. The critical factor that influences poverty status in Maluku Province is the number of households.

Authors

Institutions

Publication Details

Journal
Pattimura International Journal of Mathematics (PIJMath)
Published
2025-05-01
DOI
https://doi.org/10.30598/pijmathvol4iss1pp17-28
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Classification of Poverty in Maluku Province using SMOTE-Random Forest Algorithm

Lexy Janzen Sinay, Sanlly J Latupeirissa, Lusye Bakarbessy, Ferina Lestari Damamain
Pattimura International Journal of Mathematics (PIJMath)
Data Mining and Machine Learning Applications
article

Classification of Poverty in Maluku Province using SMOTE-Random Forest Algorithm

Lexy Janzen Sinay, Sanlly J Latupeirissa, Lusye Bakarbessy, Ferina Lestari Damamain
article en

Abstract

Poverty is a complex issue. According to BPS publications, in 2023, the poverty line in Indonesia has reached 9.57%. Maluku is one of the provinces with a high poverty rate, reaching 16.23%. This research aims to classify poverty status in Maluku Province using the SMOTE-random forest algorithm. This research uses SUSENAS 2022 data, where the data is not balanced. SMOTE is used to overcome this problem. The best model obtained has an accuracy rate of 85.8%. The model is based on a training data proportion of 75% and testing 25%, with parameters m=4 and r=100. The critical factor that influences poverty status in Maluku Province is the number of households.

Pattimura International Journal of Mathematics (PIJMath)Vol. 4(1)
University of Pattimura (ID)
Openalex Percentile: Top 88%
Data Mining and Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.