{"id":3104,"date":"2026-04-09T23:45:54","date_gmt":"2026-04-09T15:45:54","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=3104"},"modified":"2026-06-10T11:54:32","modified_gmt":"2026-06-10T03:54:32","slug":"tempo-recognition-of-kendhang-instruments-using-hybrid-feature-extraction","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=tempo-recognition-of-kendhang-instruments-using-hybrid-feature-extraction","title":{"rendered":"Tempo Recognition of Kendhang Instruments Using Hybrid Feature Extraction"},"content":{"rendered":"\n
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<\/i> 2024<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 27, Issue 3<\/a><\/p>\n<\/div>\n\n\n\n

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Muljono1<\/sup><\/i><\/a>, Pulung Nurtantio Andono1<\/sup>, Sari Ayu Wulandari2<\/sup>, Harun Al Azies1<\/sup>, and Muhammad Naufal1<\/sup><\/p>\n\n\n\n

1<\/sup>Department of Informatics Engineering, Universitas Dian Nuswantoro, Semarang 50131, Indonesia<\/p>\n\n\n\n

2<\/sup>Department of Electrical Engineering, Universitas Dian Nuswantoro, Semarang 50131, Indonesia<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0January 16, 2023
Accepted:\u00a0June 14, 2023
Publication Date:\u00a0April 9, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Representation of the K-NN algorithm.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202403_27(3).0004<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This article is the result of research on Gamelan instruments that examines from a technological perspective what is rarely done nowadays, through kendhang tempo recognition by proposing three classification modeling schemes. The proposed scheme is a new approach to kendhang tempo classification, using kendhang sound converted to image-based features via Mel spectrogram, then features are extracted from the image with Visual Geometry Group (VGG)-19 before incorporating the method K-Nearest Neighbour (K-NN) as a classification method. Based on the experimental results that have been obtained, modeling using the 3rd scheme, namely two-phase feature extraction from the Mel spectrogram image as the first phase and the second phase of VGG-19 with classification using K-NN has an advantage in accuracy (99.6%) of implementing Kendhang tempo recognition correctly and the average achievement of the fastest training processing time was 3.37 seconds compared to the 1st scheme with an accuracy of 94% and an average model training process time of 16.4 seconds and the 2nd scheme with a model accuracy of 98% and the average time to complete the model training process the longest is 6228.6 seconds.<\/p>\n\n\n\n

Keywords:\u00a0Features Extraction, K-Nearest Neighbour, Mel spectrogram, Sound Recognition, VGG-19<\/em><\/p>\n\n\n\n

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