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All Journal International Journal of Advances in Intelligent Informatics JOURNAL OF APPLIED INFORMATICS AND COMPUTING JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer The Indonesian Journal of Computer Science Bomba: Jurnal Pembangunan Daerah
Laila, Rahma
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Transportation Veterinary Other

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Artificial Intelligence Untuk Identifikasi Motif Tenun Tradisional Sulawesi Tengah Pusadan, Mohammad Yazdi; Laila, Rahma; Pratama, Septiano Anggun
Bomba: Jurnal Pembangunan Daerah Vol 5 No 1 (2025)
Publisher : Badan Riset dan Inovasi Daerah Sulawesi Tengah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65123/bomba.v5i1.93

Abstract

Traditional weaving from Central Sulawesi, such as the motifs of Magau, Banua Oge/Souraja, and Tadulako, reflects deep cultural and historical values. However, the complexity of the patterns and motifs often makes manual identification challenging. This research employs an Artificial Intelligence (AI) approach using Convolutional Neural Networks (CNN) to automate the identification of these motifs. The AI model is trained using a diverse dataset of woven motif images and shows significant accuracy in classifying Magau, Banua Oge/Souraja, and Tadulako motifs. This research opens up cultural preservation and innovation opportunities in woven products with modern technology. The achieved result is the evaluation of the AI model using the following metrics: accuracy, precision, recall, and the confusion matrix. The accuracy obtained for each motif reaches 90%.
Co-Authors Alamsyah - Anita Ahmad Kasim Chairunnisa Ar. Lamasitudju Dwi Shinta Angreni Fahmi, Moh. Fajar, Moh Hajra Rasmita Ngemba Harani, Makruf Magfirah, Magfirah Maulana, Muhammad Syahputra Miftah Miftah, Miftah Mohammad Yazdi Pusadan Nouval Trezandy Lapatta Noviantika, Noviantika Pagiu, Harry T. Rinianty, Rinianty Ryfial Azhar, Ryfial Sani, Ilham Abdillah Sasuwuk, James Rio Septiano Anggun Pratama Siti Fitriana Syahrullah Syahrullah Syaiful Hendra Ulhaq, Muhammad Naufal Daffa Widiani, Ni Nengah Wirdayanti Yusuf Anshori