KOMPARASI ALGORITMA KNN DAN ANN PADA PREDIKSI HASIL STUDI MAHASISWA (STUDI KASUS: MAHASISWA UNIVERSITAS AHMAD DAHLAN YOGYAKARTA)

Sugriyono, NIM.: 18206050005 (2021) KOMPARASI ALGORITMA KNN DAN ANN PADA PREDIKSI HASIL STUDI MAHASISWA (STUDI KASUS: MAHASISWA UNIVERSITAS AHMAD DAHLAN YOGYAKARTA). Masters thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Classification techniques in data mining can be used to make predictions. ANN and kNN algorithms are classification methods that are widely implemented in prediction of student study outcomes. Many implementations of this classification method have low accuracy results. One of the causes that influence low accuracy of classification results is the quality of dataset. This study aims to compare ANN and kNN classification algorithms to determine the best classification method of two algorithms in predicting student study outcomes. Preprocessing is carried out to improve data quality by removing outliers from datasets that already have label classes. The student study result dataset is 6.847 instances, with 18 attributes and three classes. Kmeans algorithm and distance matrix are used for preprocessing. Kmeans to get cluster center in each class, the distance matrix is used to evaluate distance of instance from cluster center. Euclidean and Manhattan distance matrices are used as a comparison of the performance of distance matrix. Based on the research results, ANN algorithm has greater accuracy than kNN algotithm, with K-means preprocessing method and Euclidean distance matrix. Using 10-fold cross validation and confusion matrix test method, ANN algorithm has 98,99% accuracy, while kNN algorithm has 98,45% accuracy.

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