Bastian, Joni (2023) Implementasi SuperTML untuk Klasifikasi Genre Musik Indonesia dengan Streamlit. Undergraduate thesis, UPN Veteran Jawa Timur.
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Abstract
Music genres are increasingly diverse, and music is listened to a lot because it has benefits such as refreshing, motivation, or therapy. But there are more and more genres Some music listeners have a tendency towards the type of genre they like. In Indonesia itself there are several popular music genres such as pop, folk, rock, indie, and dangdut. Seeing this behavior, classification of music genres becomes a topic interesting to research. Several approaches to classifying musical genres general through audio and tabular data approaches. In this research, classification Music genres will be classified through an image approach with implements SuperTML to convert tabular data into image form which is then trained using several CNN models and pre-trained CNN. After testing the implementation of the SuperTML method, it was possible used to form an image that will be used as classification data. On In this research, pre-trained CNN with the MobileNet model gained performance best compared to other CNN models and pre-trained CNN models with accuracy reached 61%.
| Item Type: | Thesis (Undergraduate) | ||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76.6 Computer Programming Q Science > QA Mathematics > QA76.87 Neural computers T Technology > T Technology (General) |
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| Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
| Depositing User: | Joni Bastian | ||||||||||||
| Date Deposited: | 22 Nov 2023 02:03 | ||||||||||||
| Last Modified: | 22 Nov 2023 02:03 | ||||||||||||
| URI: | http://repository.upnjatim.ac.id/id/eprint/18756 |
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