Synergizing Historical Similarity and Probabilistic Attributes: A Dual-Engine Machine Learning Model for Subsidized Housing Credit Risk

Abstract

Subsidized mortgage programs require objective and efficient credit approval processes to mitigate subjective biases and manual inefficiencies inherent in traditional evaluations. This study proposes a web-based decision support system leveraging a hybrid ensemble architecture that integrates the K-Nearest Neighbor (KNN) and Naïve Bayes Classifier (NBC) algorithms to assess the creditworthiness of subsidized mortgage applicants at Raja Batu Residence. The proposed framework utilizes KNN for historical data similarity mapping and NBC for probabilistic attribute evaluation, combining their predictions through a Soft Voting Aggregation mechanism to enhance stability. The system's performance was rigorously evaluated using optimized classification metrics and the System Usability Scale (SUS) involving 20 end-users. Empirical results demonstrate outstanding classification performance, with the optimized KNN model achieving an overall accuracy of 93.33% (yielding only 2 false positives and 3 false negatives) and the Naïve Bayes model achieving 92.00% accuracy (yielding 4 false positives and 2 false negatives). This high classification accuracy ensures a well-balanced confusion matrix with a minimized risk profile, aligning effectively with the prudent risk management required for government-subsidized allocations. Furthermore, the deployed web application achieved an "Excellent" usability score of 82.75, confirming its practical viability for non-technical administrative staff. Ultimately, the hybrid integration of KNN and NBC successfully streamlines the credit evaluation workflow, minimizing subjective bias while providing a reliable, highly accurate, and user-friendly tool for housing developers.