360 DEGREES AND K-NEAREST NEIGHBOUR METHODS FOR LECTURER PERFORMANCE APPRAISAL SYSTEMS: 360 DEGREES AND K-NEAREST NEIGHBOUR METHODS FOR LECTURER PERFORMANCE APPRAISAL SYSTEMS

Novita Br Ginting (Universitas Ibn Khaldun Bogor)
Yuggo Afrianto (Universitas Ibn Khaldun Bogor)

Jurnal Mantik
Vol. 3 No. 1 (2019): May: Manajemen dan Informatika

The lecturer performance appraisal system functions to measure and evaluate the performance of the lecturers in a certain period of time. The purpose of this study is to apply the 360-degree and machine learning algorithm using the K-NN (k-nearest neighbour) method to model the lecturer performance appraisal system to be more objective and accountable. DP3 is List of Appraisal of Employee Work Implementation. DP3 data is used as knowledge datasets to be used as training data and test data for the classification process and prediction of lecturer performance values. The results obtained show the 360-degree method and K-NN is able to predict with 90% accuracy right on knowledge datasets that have not been normalized and K = 5 values. Hence, this model can then be used to become a lecturer performance appraisal system application.