ANALISIS FAKTOR SOSIAL EKONOMI YANG MEMPENGARUHI RENDAHNYA CAPAIAN PENDIDIKAN DI INDONESIA MENGGUNAKAN KOMBINASI METODE DATA MINING
Dublin Core
Title
ANALISIS FAKTOR SOSIAL EKONOMI YANG MEMPENGARUHI RENDAHNYA CAPAIAN PENDIDIKAN DI INDONESIA MENGGUNAKAN KOMBINASI METODE DATA MINING
Subject
Education inequality, Socio-economic factors, K-Means Clustering , Decision Tree, Datadriven policy
Description
Educational inequality remains a persistent issue in Indonesia, particularly in regions with challenging
socio-economic conditions. This study aims to analyze how various socio-economic factors influence the
average years of schooling across Indonesian provinces using a combination of K-Means Clustering and
Decision Tree algorithms. The dataset includes indicators such as poverty rate, gross regional domestic
product (GRDP), per capita expenditure, and life expectancy, obtained from official national statistics.
K-Means Clustering was employed to segment provinces into three distinct groups based on socioeconomic similarities. The clustering revealed clear disparities among regions, where the most
disadvantaged cluster showed significantly lower education levels. Subsequently, the Decision Tree
algorithm was used to classify the average years of schooling, identifying per capita expenditure, life
expectancy, and socio-economic cluster as the most influential variables.
The combined approach allows for both segmentation and interpretation, providing data-driven insights
that are accessible and actionable for policymakers. The findings highlight the importance of targeting
socio-economic improvements as a strategy to enhance educational outcomes. Ultimately, this study underscores the value of integrating unsupervised and supervised machine learning models in addressing complex social issues in education.
socio-economic conditions. This study aims to analyze how various socio-economic factors influence the
average years of schooling across Indonesian provinces using a combination of K-Means Clustering and
Decision Tree algorithms. The dataset includes indicators such as poverty rate, gross regional domestic
product (GRDP), per capita expenditure, and life expectancy, obtained from official national statistics.
K-Means Clustering was employed to segment provinces into three distinct groups based on socioeconomic similarities. The clustering revealed clear disparities among regions, where the most
disadvantaged cluster showed significantly lower education levels. Subsequently, the Decision Tree
algorithm was used to classify the average years of schooling, identifying per capita expenditure, life
expectancy, and socio-economic cluster as the most influential variables.
The combined approach allows for both segmentation and interpretation, providing data-driven insights
that are accessible and actionable for policymakers. The findings highlight the importance of targeting
socio-economic improvements as a strategy to enhance educational outcomes. Ultimately, this study underscores the value of integrating unsupervised and supervised machine learning models in addressing complex social issues in education.
Creator
Diana Yusuf, Fahrul Razi
Source
https://ojs.itb-ad.ac.id/index.php/JUSIN/article/view/3115
Publisher
Institut Teknologi dan Bisnis Ahmad Dahlan
Date
2025-06-25
Contributor
Sri Wahyuni
Rights
E-ISSN : 2797-8516
Format
PDF
Language
Indonesian
Type
Text
Files
Collection
Citation
Diana Yusuf, Fahrul Razi, “ANALISIS FAKTOR SOSIAL EKONOMI YANG MEMPENGARUHI RENDAHNYA CAPAIAN PENDIDIKAN DI INDONESIA MENGGUNAKAN KOMBINASI METODE DATA MINING,” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/10246.