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All Journal International Journal of Artificial Intelligence Research JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Chemurgy ILKOM Jurnal Ilmiah EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Industrial Galuh
Aji Ery Burhandenny
Informatika, Program Studi Teknik Elektro, Universitas Mulawarman, Indonesia
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering

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Journal : International Journal of Artificial Intelligence Research

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An extraction of shapes and support vector machine methods for identification of decorative wall “Lamin” motifs of the Dayak Kenyah Pampang tribe Haviluddin Haviluddin; Masna Wati; Rayner Alfred; Aji Ery Burhandenny; Arief Ardi Pratama
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.475

Abstract

One of the Dayak cultures of Kalimantan Island, Indonesia is a traditional house called Lamin where each wall is decorated according to tribal characteristics. This study aims to identify the image on the Lamin wall using the Support Vector Machine (SVM) method based on the eccentricity and metric parameter values. The data of this study consisted of 50 types of images of the Lamin wall motifs of the Dayak Kenyah tribe consisting of tebengaang, dragon, crocodile, tiger, and arch which were taken from the tourist village, Pampang, Samarinda, East Kalimantan. Based on the experiment, the shape feature extraction method has produced the highest value of the eccentricity parameter which is 0.6979 and the metric parameter is 0.9953 on the image of the arch. Motif identification using the SVM method using linear, Gaussian/RBF, and polynomial kernel parameters has resulted in the highest accuracy with 80% image composition of kernel polynomial at 85%, Gaussian/RBF at 80%, and linear at 78%.
Co-Authors 'Aisyah 'Aisyah Ade Nur Ifan Ahmad Dwi Martanto Priyatmoko Anggriani Profita Arief Ardi Pratama Arif Harjanto Budi Nining Widarti Budi Yannur Dhea Yolanda Didit Suprihanto, Didit Edhi Sarwono Hairul Huda Happy Nugroho Haviluddin Haviluddin Muhammad Akbar Rayner Alfred Restu Mukti Utomo Wati, Masna