Penerapan Algoritma K-Nearest Neighbor (KNN) Untuk Mengklasifikan Jenis Penerimaan Bantuan

Abstract

One of the villages in North Lampung which has many socio-economic problems is Madukoro. Some of the problems that often occur in This village includes high poverty rates, low levels of education and low levels of public health. The K-Nearst Neighbor (KNN) algorithm method was chosen by the author because it can be used as a solution to determine the classification of aid recipients. The K-Nearst Neighbor (KNN) algorithm will determine beneficiaries based on work level, age and income. Calculation results using Microsoft Excel show that there are 110 PKH assistance classes, 57 elderly assistance classes, 9 BLT classes with a total data of 176 beneficiary data. And from the results of the calculation of government assistance in the village of North Lampung Madukoro using rapid miner, it is known that the PKH assistance class is 110 people with an accuracy rate of 89.57%, the elderly assistance class is 58 people with an accuracy rate of 87.50% and then the BLT class is 9 people. with an accuracy rate of 100.00% with a total data of 176 beneficiary data.