Comparison of Ensemble Classifier Methods, using the K-NN, and Naive Bayes algorithms for the Eligibility of PKH Recipients Case Study (Entalsewu Village, Buduran District, Sidoarjo Regency)
Perbandingan Metode Ensemble Classifier, dengan menggunakan algoritma K-NN, dan Naive Bayes Kelayakan Penerima PKH Studi Kasus (Desa Entalsewu, Kecamatan Buduran, Kebupaten Sidoarjo)
DOI:
https://doi.org/10.21070/ups.12076Keywords:
Poverty, PKH, ensemble learning, K-NN, Naïve Bayes, Soft VotingAbstract
Poverty is a socioeconomic challenge in which individuals cannot meet basic living needs. To address this issue, the Indonesian government implemented the Family Hope Program (PKH), a conditional social assistance program for vulnerable and Extremely Poor Households (RTSM). However, mistargeting remains a challenge in Entalsewu Village, Buduran, Sidoarjo. This study applies an Ensemble Classifier combining K-Nearest Neighbor (K-NN) and Naive Bayes using Soft Voting to improve classification accuracy. The results show that the proposed Ensemble Classifier achieved 93% accuracy with a 50:50 train-test split and an average execution time of 6.425 seconds. Precision reached 96% for eligible and 91% for ineligible groups, while recall reached 93% and 95%, respectively. The resulting F1-scores were 94% for the eligible group and 93% for the ineligible group. These results indicate that the proposed method provides effective and efficient classification performance for improving PKH targeting accuracy.
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