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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana
Giri, I Nyoman Yusha Tresnatama
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Computer Science & IT

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The Effect of Feature Selection on Music Genre Classification Giri, I Nyoman Yusha Tresnatama; Putri, Luh Arida Ayu Rahning
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 9 No 4 (2021): JELIKU Volume 9 No 4, Mei 2021
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2021.v09.i04.p13

Abstract

One of the things that affects classification results is the correlation of features to the class of a data. This research was conducted to determine the effect of the reduction of features (independent variable) that have the weakest correlation or have a distant relationship with the class (dependent variable). Bivariate Pearson Correlation is used as a feature selection method and K-Nearest Neighbor is used as a classification method. Results of the test showing that, 75.1% average accuracy was obtained for classification without feature selection, while using feature selection, average accuracy was obtained in the range of 75% - 79.3%. The average accuracy obtained by the selection of features tends to be higher compared to the accuracy obtained without selection of features.
Music Genre Classification Using Modified K-Nearest Neighbor (MK-NN) Giri, I Nyoman Yusha Tresnatama; Rahning Putri, Luh Arida Ayu; Mastrika Giri, Gst Ayu Vida; Anom Cahyadi Putra, I Gusti Ngurah; Widiartha, I Made; Supriana, I Wayan
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 3 (2022): JELIKU Volume 10 No 3, February 2022
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2022.v10.i03.p02

Abstract

The genre of music is a grouping of music according to their resemblance to one another and commonly used to organize digital music. To classify music into certain genres, one can do it by listening to the music one by one manually, which will take a long time so that automatic genre assignment is needed which can be done by a number of methods, one of which is the Modified K-Nearest Neighbor. Modified K-Nearest Neighbor method is a further development of its former method called KNearest Neighbor method which adds several additional processes such as validity calculations and weight calculations to provide more information in the selection class for the testing data. Research to find the best H value shows that the H = 70% of the training data is able to produce an accuracy of 54.100% with K = 5 and the proportion ratio of test data and training data is 20:80 (fold 5). The best H value is then used for further testing, which is to compare the K-Nearest Neighbor method with the Modified K-Nearest Neighbor method using two different proportions of test data and training data and each proportion of data also tests a different K value. The results of the classification comparison of the two methods show that the Modified K-Nearest Neighbor method, with the highest accuracy of 55.300% is superior to the K-Nearest Neighbor method with the highest accuracy of 53.300%. The two highest accuracies produced in each method were obtained using K = 5 and the proportion ratio of test data and training data is 10:90 (fold 10).
Co-Authors Gst. Ayu Vida Mastrika Giri I Gusti Ngurah Anom Cahyadi Putra I Made Widiartha I Wayan Supriana Luh Arida Ayu Rahning Putri