Klasifikasi Masyarakat Penerima BPNT Program Sembako 2021 di Kelurahan Tiakar dengan Mengunakan Metode KNN Classifier - Annisa Hidayatul Nufus; Helma Helma (Journal of Mathematics UNP, 2023)
At the time of the implementation of the food program in the Tiakar Sub-District, there was no information available on the criteria that would make the head of a family eligible to be a recipient of assistance, even though this was very important so that the government's goal of fulfilling the need for nutritious food could be felt by those who really needed it. The purpose of this study was to classify the heads of families in Tiakar Subdistrict as eligible or not eligible to receive staple foods using the K-Nearest Neighbor method. This study uses interview data conducted with the heads of families in the Tiakar Village. The data analysis step is to divide the data into training data and test data by 80%:20%, determine the number of nearest neighbours, calculate the dissimilarity distance and choose a class, calculate the level of accuracy using the confusion matrix and then choose the optimal K. Based on the results of the study, it was found that the K value that was good to use in the classification of family heads in Tiakar Subdistrict was K=3 because it had an accuracy percentage of 95%.