Klasifikasi kelayakan Calon Pegawai Honorer Dinas Pendidikan Kabupaten Labuhanbatu Menggunakan Metode Knn-Confunsion

Abstract

Eligibility Classification of Candidates for Honorary Personnel at the Education Service in Labuhanbatu Regency, policies will be made that can direct Honorary Personnel to be able to carry out their duties optimally. One of the policies of the Head of the Labuhanbatu Regency Education Service is to assess the suitability of Candidates for Honorary Staff. This policy aims to encourage honorary staff to carry out their duties well and with discipline, and this policy is sufficient to improve the performance of the honorary staff at the Labuhanbatu Regency Education Office. The biggest problem with the performance of Honorary Staff within the Labuhanbatu Regency Education Service is service orientation, integrity and discipline. In selecting new Honorary Staff Candidates, it is necessary to have a Classification of the Eligibility of the new Honorary Staff Candidates. The way to determine the eligibility of new Honorary Staff Candidates to become Honorary Staff is by calculating the similarity value. Next, to ensure errors in assessing the similarity of criteria, measurements of the level of accuracy, precision, recall are carried out, and comparison measurements are made of the level of accuracy, precision and recall. Based on the problems above, there is a need to develop a Decision Support System that is capable of selecting the Eligibility Classification of Candidates for Honorary Staff at the Labuhanbatu Regency Education Service. K-nearest Neighbors or KNN is a classification algorithm that works by taking a number of K nearest data (neighbors) as a reference to determine the class of new data. This algorithm classifies data based on similarity or closeness to other data. Confusion Matrix is a performance measurement for machine learning classification problems where the output can be two or more classes.