Metode AdaBoost dan Random Forest untuk Prediksi Peserta JKN-KIS yang Menunggak

Ikhlasul Amalia Rahmi, Farit Mochamad Afendi, Anang Kurnia


The contribution of participants, employers, and/or the government is one of the most important things in the National Health Insurance Program-Healthy Indonesia Card (JKN-KIS) implementation. All Indonesian residents were required to participate in the JKN-KIS program which is divided into four types of participation, one of which is Non-Wage Recipient Participants (PBPU) whose contributions are paid independently. However, based on December 2021 data, 60% of PBPU participants were late in paying monthly until they were in arrears. Arrears in payment of contributions cause several problems, including payment of claims to deficits. This research utilized big data owned by the Healthcare and Social Security Agency (BPJS Kesehatan) and machine learning based on ensemble trees, namely AdaBoost and random forest to get the predictions of participants in arrears. The results showed that machine learning based on an ensemble tree was able to predict PBPU participants in arrears with high accuracy, as evidenced by the AUC values in both models above 80%. The random forest model has an F1-score and the AUC value is better than the AdaBoost, namely the F1-score of 85,43% and the AUC value of 87,20% in predicting JKN-KIS participants who are in arrears in payment of contributions.


AdaBoost; Big Data; Machine Learning; JKN-KIS Program; Random Forest

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