Klasifikasi Tingkat Kemiskinan di Indonesia Menggunakan Naive Bayes dengan RapidMiner
DOI:
https://doi.org/10.55606/juisik.v5i3.1770Keywords:
Data Classification, Machine Learning, Naive Bayes, Poverty, RapidMinerAbstract
Poverty is a multidimensional issue that has a significant impact on social and economic development in Indonesia. Accurate analysis of poverty levels is essential to support government policies in distributing aid and planning targeted development programs. This study aims to classify poverty levels in Indonesia using the Naive Bayes algorithm based on machine learning, assisted by the RapidMiner Studio software. The dataset consists of 155 entries with 12 key attributes reflecting social and economic indicators, such as household expenditure, education level, unemployment rate, and the Human Development Index (HDI). The research follows the CRISP-DM methodology, which includes the stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The modeling results show that the Naive Bayes algorithm achieves an accuracy of 94.19%, with high precision and recall values, indicating consistent model performance in classifying poor and non-poor categories. These findings suggest that the Naive Bayes-based machine learning approach can serve as an effective analytical tool to understand poverty patterns in Indonesia. The implementation of this model is expected to assist the government in making data-driven decisions to improve the effectiveness of poverty alleviation programs.
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