MUSIC GENRE CLASSIFICATION USING RANDOM FOREST ALGORITHM COMPARED WITH NAÏVE BAYES ALGORITHM

KURNIAWAN, TIMOTHY KALEB (2023) MUSIC GENRE CLASSIFICATION USING RANDOM FOREST ALGORITHM COMPARED WITH NAÏVE BAYES ALGORITHM. Other thesis, UNIVERSITAS KHATOLIK SOEGIJAPRANATA.

Preview	Text 18.K1.0040-TIMOTHY KALEB KURNIAWAN-COVER_a.pdf Download (742kB) | Preview
	Text 18.K1.0040-TIMOTHY KALEB KURNIAWAN-BAB I_a.pdf Restricted to Registered users only Download (86kB)
	Text 18.K1.0040-TIMOTHY KALEB KURNIAWAN-BAB II_a.pdf Restricted to Registered users only Download (155kB)
	Text 18.K1.0040-TIMOTHY KALEB KURNIAWAN-BAB III_a.pdf Restricted to Registered users only Download (318kB)
	Text 18.K1.0040-TIMOTHY KALEB KURNIAWAN-BAB IV_a.pdf Restricted to Registered users only Download (932kB)
	Text 18.K1.0040-TIMOTHY KALEB KURNIAWAN-BAB V_a.pdf Restricted to Registered users only Download (82kB)
Preview	Text 18.K1.0040-TIMOTHY KALEB KURNIAWAN-DAPUS_a.pdf Download (198kB) | Preview
	Text 18.K1.0040-TIMOTHY KALEB KURNIAWAN-LAMP_a.pdf Restricted to Registered users only Download (108kB)

Abstract

This study aims to compare the Random Forest algorithm with Naive Bayes and find out which algorithm has better accuracy. After finding which algorithm is better, the author looks for causes that make one algorithm better than the other To get the results, the authors conducted an experiment by conducting an accuracy test using the GTZAN Dataset. Testing is done by changing the parameters of the Test Set and Train Set. In the end, Random Forest provides better accuracy than Naive Bayes, although the difference in accuracy is not that great.

Item Type:	Thesis (Other)
Subjects:	000 Computer Science, Information and General Works > 004 Data processing & computer science
Divisions:	Faculty of Computer Science > Department of Informatics Engineering
Depositing User:	Mr Yosua Norman Rumondor
Date Deposited:	04 Oct 2023 06:38
Last Modified:	04 Oct 2023 06:38
URI:	http://repository.unika.ac.id/id/eprint/32954

Actions (login required)

View Item