K-Means Clustering for Profiling BPJS Health Traders in Semarang by Ability To Pay (ATP) and Willingness To Pay (WTP)

Abstract

This study applies the K-Means clustering algorithm to profile market vendors in Semarang City based on their Ability To Pay (ATP) and Willingness To Pay (WTP) BPJS Health insurance contributions, addressing a notable decline in participation among Non-Salaried Workers (PBPU) in Indonesia's National Health Insurance (JKN-KIS). Utilizing quantitative data from 95 Bulu Market vendors, ATP and WTP were estimated via household income and expenditure analysis through linear regression models performed in R-Commander (p < 0.05). Clustering with k=4 yielded optimal segmentation (Davies-Bouldin Index = 0.079), identifying four distinct groups: Opportunistic (low ATP/WTP), Rational (high ATP/moderate-low WTP), Price-Sensitive (financially vulnerable with low ATP/WTP), and Prospective (high ATP and WTP). This novel integration of ATP and WTP in Indonesia's informal sector provides data-driven insights allowing BPJS policy makers to tailor intervention strategies, improving premium compliance within the JKN system. The methodology offers a replicable framework for health insurance segmentation in comparable middle-income Southeast Asian settings, supporting Universal Health Coverage goals.