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All Journal JUITA : Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Journal Information System Development JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) International Journal of Advances in Data and Information Systems Journal of Soft Computing Exploration Jurnal Pengabdian Kepada Masyarakat Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Brilliance: Research of Artificial Intelligence Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi International Journal of Multidisciplinary Sciences and Arts SOROT: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Indonesia Sejahtera Jurnal Pelayanan Masyarakat Jurnal Ilmu Komputer dan Informatika
Moch Yusuf Asyhari
Department Of Informatic Engineering, Universitas PGRI Madiun, Indonesia
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Journal : Journal of Soft Computing Exploration

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Sound detection of gamelan musical instruments using teachable machine Yunitasari, Yessi; Asyhari, Moch Yusuf; Kurniawati, Inung Diah; STT, Latjuba Sofyana
Journal of Soft Computing Exploration Vol. 6 No. 2 (2025): June 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i2.576

Abstract

Gamelan is an instrument of musical expression that has an aesthetic function related to social, moral, and spiritual values. Gamelan consists of a variety of musical instruments that have a unique sound. In this study, the sound detection of nine gamelan musical instruments was carried out using a teachable machine. The gamelan musical instruments detected included gong, kenong, saron, bonang, gambang, kendang, flute, siter, and rebab. The algorithm used is CNN. The CNN algorithm has a fairly good performance for the sound detection process. The test results of the built model show an "acc" value of 25 ranging from 0.99 to 1, which indicates that the model achieves an accuracy rate of 99% to 100% on the training dataset. At the same time, "test accuracy" refers to a measure of the model's effectiveness in predicting data it has not encountered during training. The "test accuracy" score varied from 0. 83, which shows that the validation data has an accuracy of 83%.
Co-Authors Abdul Rozaq Ahmad, Khairul Adila Alim Citra Aria Bima Alim Citra Aria Bima Aminuyati Anardani, Sri Aria Bima, Alim Citra Ayunda Nur Hapsari Azis, Muh Nur Luthfi Bima, Alim Citra Aria Bryan Ricky Christoffel Cahyo, Wahyu Nur Cahyono Putro, Moh Rizal Bagus Cahyono Putro, Moh. Rizal Bagus Dhomas Hatta Fudholi Eka Resty Novieta Sari Estuning Dewi Hapsari Fatim Nugrahati Fery Luvita Sari Hapsari, Estuning Dewi Ilman Winas Saputra Inung Diah Kurniawati Juwari Juwari Kelik Sussolaikah Kurniawati, Inung Diah Muh Nur Luthfi Azis Muh Nur Luthfi Azis Nita, Sekreningsih Pratiwi Susanti Pratiwi Susanti Puguh Jayadi Rifky Reza Swarga Shamsuddin, Norin Rahayu Slamet Yulianto Sri Anardani STT, Latjuba Sofyana Susanti, Pratiwi Tajuddin, Taniza Yessi Yunitasari