ANALISIS KLASIFIKASI ARTIST MUSIC MENGGUNAKAN MODEL REGRESI LOGISTIK BINER DAN ANALISIS DISKRIMINAN

ANDREA TRI RIAN DANI, VITA RATNASARI, LUDIA NI'MATUZZAHROH, IGAR CALVERIA AVIANTHOLIB, RADITYA NOVIDIANTO, NARITA YURI ADRIANINGSIH

Abstract


Characteristics of a song are an important aspect that must be kept authentic by a singer. Using the Spotify API feature, we can extract the characteristics or elements of a song sung by a singer.  There are eight (8) elements that we can get from the extraction of a song, namely: Danceability, Energy, Loudness, Speechiness, Acousticness, Liveness, Valence, and Tempo. Based on the extraction results, we can label the music artist using the classification analysis method. In this study, the labels are music artists, namely Ariana Grande and Taylor Swift. This study aims to obtain the classification of music artist labels using binary logistic regression methods and discriminant analysis. The response variable used in this study is Artist Music (Y) which is categorized into two categories, namely Ariana Grande (Y=0) and Taylor Swift (Y=1). The data will be divided into training and testing data with the proportion of data 90:10 and 80:20. Based on the results of the analysis, the binary regression model that was built, with the proportion of training testing data that is 90:10 has a classification accuracy for data testing of 90.00%.

Keywords


Discriminant Analysis, Classification, Binary Logistic Regression

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DOI: https://doi.org/10.34312/jjps.v3i1.13708

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