Classification of beneficiaries for the rehabilitation of uninhabitable houses using the K-Nearest Neighbor algorithm

Abstract

The registrars for rehabilitation programs for uninhabitable settlements are increasing every year. The large data processing of registrants may result in inaccuracies and need a long time to determine livable houses (RTLH) and unfit for habitation (non RTLH). This study aims to apply the K-Nearest Neighbor algorithm in classifying the eligibility of recipients of uninhabitable house rehabilitation assistance. The data used in this study were 1289 data with 13 attributes from the Jepara Regency Public Housing and Settlement Service. Data processing begins with attribute selection, categorization, outlier data cleaning, and data normalization and method application. The proposed system has the best classification at k of 5 with an accuracy of 97.93%, 96.88% precision, 99.53% recall, and an AUC value of 0.964.