DETERMINING LECTURAL EVALUATION IN FACULTY OF ENGINEERING MALIKUSSALEH UNIVERSITY USING K-NN

Abstract

K-Nearest Neighbor is a method that can classify data based on the closest distance. In addition, K-NN is one of the supervised learning algorithms with learning processes based on the value of the target variable associated with the value of the predictor variable. In the K-NN algorithm, all data must have a label, so that when a new data is given, the data will be compared with the existing data, then the most similar data is taken by looking at the label of that data. Filling and processing many questionnaires to determining the results of lectural evaluation from the performance of lecturers certainly requires a lot of time and process. Therefore, it is necessary to apply the K-NN Manhattan Distance method. In this study, the testing data is taken from one of the training data and has a classification result that is "Very Good". After going through the K-NN Manhattan Distance method with k being the closest / smallest neighbor, then the following results are obtained: Distance 5.4, the classification result is "Very Good" and 74.03% of similarity value. Based on the results obtained, the result of the classification from K-NN Manhattan Distance method show similarities with the results of the pre-existing classification.