Pandu Deski Prasetyo, I. P. S. Wijaya, Ario Yudo Husodo
{"title":"Klasifikasi Genre Musik Menggunakan Metode Mel-Frequency Cepstrum Coefficients dan K-Nearest Neighbors Classifier","authors":"Pandu Deski Prasetyo, I. P. S. Wijaya, Ario Yudo Husodo","doi":"10.29303/jtika.v1i2.41","DOIUrl":null,"url":null,"abstract":"In the world of music, music has several types of genre genres that can be grouped, there are music genre pop, rock, blues, slow, jazz, metal, dangdut and many more. And for each person must have a genre favorite that is different from one another, but to distinguish it does not need to play music files one by one especially if the number of music files is a lot. Therefore, computer software is needed to distinguish each of these genres in order to make it easier for users to distinguish and group the types of music according to their wishes automatically. By using the MFCC and KNN methods as a classification solution to classify several types of genre streams can be easily resolved. The results achieved from this study reached 52,4% with a K = 13 as the nearest neighboring point.","PeriodicalId":197519,"journal":{"name":"Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA )","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA )","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29303/jtika.v1i2.41","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
In the world of music, music has several types of genre genres that can be grouped, there are music genre pop, rock, blues, slow, jazz, metal, dangdut and many more. And for each person must have a genre favorite that is different from one another, but to distinguish it does not need to play music files one by one especially if the number of music files is a lot. Therefore, computer software is needed to distinguish each of these genres in order to make it easier for users to distinguish and group the types of music according to their wishes automatically. By using the MFCC and KNN methods as a classification solution to classify several types of genre streams can be easily resolved. The results achieved from this study reached 52,4% with a K = 13 as the nearest neighboring point.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
