Evaluation Of Lecturer Education And Teaching Performance Through E-Monevin Using K-Nearest Neighbor (K-NN) Algorithm

Abstract

Abstract In implementing the performance evaluation of lecturers, a monitoring and evaluation Information System based system has been developed, better known as e-monevin. Assessment is conducted by students on lecturers who take courses taken by students in the current semester. The results of the student assessment are taken into consideration in the assembly of lecturers. Performance is a way, behavior, and ability of a person. The performance of lecturers is assessed by how to carry out their duties and responsibilities as educators in delivering material to students. Measurement and evaluation of lecturers’ performance seen from several aspects one of which is the field of education and teaching. The purpose of this study is to determine the performance of lecturers in the field of education and teaching through e-mononevin using Rapid Miner software in data processing. The data processing technique used is the K-Nearest Neighbor (K-NN) algorithm. From the test results that have been done using the K-NN algorithm in K-Fold Cross Validation with different number of validations, the best accuracy value obtained shows that the K-NN algorithm using number of validation 6 and 9, with an accuracy value of 96.30 %.