KLASIFIKASI ALGORITMA K-NEAREST NEIGHBOR, NAIVE BAYES, DECISION TREE UNTUK PREDIKSI STATUS KELULUSAN MAHASISWA S1
COMPARATION OF K-NEAREST NEIGHBOR, NAIVE BAYES, DECISION TREE TO PREDICT UNDERGRADUATE STUDENTS TO GRADUATE ON TIME
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Abstract
Students are a crucial factor that must be considered in seriously evaluating study programs. The indicator of the success of the study program is the length of time it takes to complete the study. The study period is the time when students complete their studies. In addition, student study time reflects the level of student learning performance. In a broader perspective, the average student study time affects the quality of study programs and therefore student study time is used as one of the criteria in determining the assessment by the National Accreditation Board for Higher Education (BAN PT). The purpose of this study was to understand how well the K-Nearest Neighbor, Naive Bayes, Decision Tree performed to predict undergraduate students of the Law Study Program, Faculty of Law, Sebelas Maret University, graduating on time using the RapidMiner application. From the results of the testing and prediction process with the RapidMiner application using the three methods that have been carried out. The K-Nerest Neighbor (KNN) method obtained an accuracy of 96.67%, in the prediction test using the Naïve Bayes method it obtained an accuracy of 77.33%, while the Decision Tree method obtained an accuracy of 94.00%. So that the K-NN method is the best method in comparative classification in predicting student graduation on time with a predicted accuracy value of 96.67%.
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