CLASSIFICATION OF THE SOCIOECONOMIC STATUS OF PROSPECTIVE GROOMS USING THE MODIFIED K-NEAREST NEIGHBOR (MKNN) ALGORITHM - Sari, Rosi Ernita Sari; Ali Muharom, Lutfi; Abdurrahman, Ginanjar (Computing and Information System Journal, 2025)
Marriage is an important moment in life that is influenced not only by emotional aspects, but also by socioeconomic factors. The socioeconomic status of the prospective groom can affect the harmony of the household that will be built. This study aims to classify the socioeconomic status of prospective grooms using the Modified K-Nearest Neighbor (MKNN) algorithm and evaluate its performance through accuracy, precision, and recall measurements. The dataset used consists of 200 data points on prospective grooms obtained from the BKKBN (National Family Planning Agency) of Bondowoso District, with attributes including occupation, source of income, and income value. The classification process involves data pre-processing, Euclidean distance calculation, validation of training data, weighted voting, and K-fold Cross Validation. The test results showed that MKNN was able to provide good classification performance, with the highest accuracy of 88%, precision of 91.60%, and recall of 88% in a specific K-Fold scenario. This study shows that the MKNN algorithm is effective in classifying the socioeconomic status of prospective grooms and can be used as a reference for further research.