Performance Comparison of Fuzzy C-Means and Decision Tree Algorithms for Analyzing Social Assistance Recipient Eligibility
DOI:
https://doi.org/10.30871/jaic.v10i2.12431Keywords:
Fuzzy C-Means, Decision Tree, Social Assistance, PKH, ClassificationAbstract
The Family Hope Program (PKH) is one of the government's social assistance programs aimed at improving the welfare of low-income communities. In the determination process, an accurate and measurable analysis method is required so that the selection of potential beneficiaries can be carried out objectively. This study aims to compare the performance of the Fuzzy C-Means (FCM) method and the Decision Tree algorithm in analyzing the eligibility of PKH social assistance recipients. The FCM method is used to group data unsupervised into eligible and ineligible clusters, while the Decision Tree is used as a supervised classification method by utilizing socioeconomic attributes as input variables. The testing was conducted using accuracy, precision, recall, F1-Score, and computation time metrics. The dataset uses from the Targeting for the Acceleration of Extreme Poverty Eradication (P3KE) program in Binjee Village, Nisam District, North Aceh Regency, with a total sample size of 632 individual records. The results showed that FCM was able to form two clusters with an accuracy value of 59.95% and an F1-Score of 70.03%. Meanwhile, Decision Tree performed much better with an accuracy of 94.96%, an F1-Score of 95.04%, a precision of 92.33%, a recall of 97.41%, and a computation time of 0.0664 seconds. Based on these evaluation results, it can be concluded that Decision Tree is more effective than FCM because it is capable of producing higher accuracy, more consistent evaluation metric stability, clear classification rule interpretation, and comparable processing time efficiency. Thus, the Decision Tree algorithm is considered more optimal and suitable for use in supporting the process of analyzing the eligibility of PKH social assistance recipients in an objective and measurable manner.
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