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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Mattawang: Jurnal Pengabdian Masyarakat Panrannuangku Jurnal Pengabdian Masyarakat Jurnal Algoritma Malcom: Indonesian Journal of Machine Learning and Computer Science
Budaya, I Gede Bintang Arya
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Health Professions Library & Information Science Public Health Social Sciences

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Journal : Jurnal Algoritma

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Komparasi Ekstraksi Fitur Nada Gamelan Gangsa Terhadap Performa Klasifikasi Dengan LSTM Budaya, I Gede Bintang Arya; Yusadara, I Gede Putra Mas; Harsemadi, I Gede; Martha, Gede Indra Raditya; Agustino, Dedy Panji
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2382

Abstract

Indonesia has a rich and diverse culture, one aspect of which is traditional musical instruments. Bali, as one of the provinces in Indonesia, has unique instruments such as the Gangsa, which is an important part of the Gamelan ensemble. The main challenge in learning traditional musical instruments is the accurate recognition and classification of notes. This study aims to classify Gangsa notes using a Long Short-Term Memory (LSTM) model based on audio feature extraction. Three feature extraction methods were used: Mel-Frequency Cepstral Coefficients (MFCC), Chroma Features, and Mel-Spectrogram. The dataset consisted of 10 tone classes recorded manually from Gangsa bars. The research stages included audio pre-processing, feature extraction, model training, and performance evaluation using accuracy, precision, recall, and confusion matrix metrics. The results show that the MFCC-based model achieved the highest accuracy of 100%, followed by Chroma Features with 98%, and Mel-Spectrogram with 88%. This study shows that the selection of appropriate audio features has a significant effect on tone classification performance. These findings contribute to the application of Artificial Intelligence (AI) in cultural preservation through digital music education.
Co-Authors Dedy Panji Agustino, Dedy Panji Dewi, Ni Made Rai Masita Evi Triandini I Gede Harsemadi I Komang Dharmendra Kusuma, Tubagus Mahendra Mahendra, I Nyoman Dwi Arysna Martha, Gede Indra Raditya Pande, I Made Suandana Astika Suniantara, I Ketut Putu Yusadara, I Gede Putra Mas