KLASIFIKASI SUARA ALAT MUSIK PETIK MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

Miftahul Ahmadil Khair, . (2026) KLASIFIKASI SUARA ALAT MUSIK PETIK MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN). Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

	Text ABSTRAK.pdf Download (194kB)
	Text AWAL.pdf Download (625kB)
	Text BAB I.pdf Restricted to Repository UPNVJ Only Download (240kB)
	Text BAB II.pdf Restricted to Repository UPNVJ Only Download (383kB)
	Text BAB III.pdf Restricted to Repository UPNVJ Only Download (363kB)
	Text BAB IV.pdf Restricted to Repository UPNVJ Only Download (1MB)
	Text BAB V.pdf Download (227kB)
	Text DAFTAR PUSTAKA.pdf Download (199kB)
	Text DAFTAR RIWAYAT HIDUP.pdf Restricted to Repository UPNVJ Only Download (143kB)
	Text LAMPIRAN.pdf Restricted to Repository UPNVJ Only Download (880kB)
	Text HASIL PLAGIARISME.pdf Restricted to Repository staff only Download (188kB)
	Text ARTIKEL KI.pdf Restricted to Repository staff only Download (700kB)

Abstract

This study aims to develop a classification system for plucked stringed musical instrument sounds using the CNN method, comparing Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Cepstral Coefficients (LPCC) feature extraction techniques. The five types of plucked stringed musical instruments classified in this study are the guitar, ukulele, harp, kecapi, and sitar. The research process begins with the collection of audio data from each instrument, followed by data preprocessing, which includes cleaning, segmentation, and feature extraction. The processed data is then divided into training and testing data. Then a CNN model is constructed and trained using the extracted features from the training data to recognize the sound patterns of each instrument. Model evaluation is performed using the testing data to measure classification accuracy. This study is expected to contribute to advancements in sound recognition technology, particularly in the context of music, and to create opportunities for further applications in education, art, and cultutural field.

Item Type:	Thesis (Skripsi)
Additional Information:	[No.Panggil: 2110511084] [Pembimbing: Muhammad Adrezo] [Penguji 1: Widya Cholil] [Penguji 2: Nurul Afifah Arifuddin]
Uncontrolled Keywords:	CNN, Linear Prediction Cepstral Coefficients, Mel Frequency Cepstral Coefficients, Sound Classification, Stringed Musical Instruments
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User:	MIFTAHUL AHMADIL KHAIR
Date Deposited:	27 Mar 2026 05:08
Last Modified:	27 Mar 2026 08:35
URI:	http://repository.upnvj.ac.id/id/eprint/42680

Actions (login required)

View Item