Klasifikasi Pemberian Bantuan UMKM Cirebon dengan Menggunakan Algoritma K-Nearest Neighbor

Abstract

The Indonesian government in obtaining Real Time data on MSMEs who are entitled to assistance, accuracy in distributing MSME assistance, and accelerating Indonesia's economic growth through MSMEs, especially the Cirebon Regency area. There are several ways so that cash transfer assistance for micro-scale SMEs from the government is right on target, in this study the authors will use data mining techniques with the k-nearest neighbors method in classifying receiving assistance from SMEs. The data used in this study uses secondary data with attributes of Regency, District, Business Name, Product Name, Business License, Assets and Turnover. The application of the KNN algorithm uses the retrieval operator, cross validation, and in developing the model using the KNN algorithm operator, apply model and performance. The results of the accuracy are 98.46 % with details, namely the Prediction Results are Eligible and it turns out to be true as many as 339 Data. The Prediction Result is Eligible and it turns out to be true Not Eligible as much as 2 Data. Prediction results are not eligible and it turns out to be true as much as 4 data. Prediction results are not eligible and it turns out to be true, 42 data are not eligible. Recommendations for the pattern of knowledge obtained using the K-NN algorithm. Researchers provide recommendations that are feasible to be given assistance for MSMEs as many as 339 MSME participant data spread across the Cirebon district and included in the affected category. Then there are several MSME participants who cannot receive MSME assistance according to the application of the KNN algorithm, which is 42 data, and there are 2 data from participants who are proposed to receive MSME assistance. The hope of the research for participants who receive assistance from the government can survive in conditions like this covid 19