Article Per Year (5 Year)
p-Index From 2020 - 2025
4.494
P-Index
This Author published in this journals
All Journal Jurnal Simetris JUITA : Jurnal Informatika Jurnal CoreIT SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknologi Terpadu Jurnal Teknologi Komputer dan Sistem Informasi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Jurnal Ilmiah Teknologi dan Komputer (JITTER) MDP Student Conference JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Devella, Siska
UNIVERSITAS MULTI DATA PALEMBANG
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 29 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Teknologi Terpadu

 1 
Klasifikasi Motif Songket Palembang menggunakan Support Vector Machine berdasarkan Histogram of Oriented Gradients Yohannes, Yohannes; Al Rivan, Muhammad Ezar; Devella, Siska; Meiriyama, Meiriyama
Jurnal Teknologi Terpadu Vol 9 No 2 (2023): Desember, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i2.1032

Abstract

Songket Palembang is one of the intangible cultural heritages with the domain of traditional craftsmanship and crafts. Songket Palembang has several motifs, including Chinese Flowers, Cantik Manis, and Pulir. Preservation efforts are carried out by providing an understanding of Palembang Songket patterns. This study classified Palembang Songket patterns based on shape features using the Histogram of Oriented Gradient (HOG) method. Based on the test results of 45 test data images, the HOG method can become a feature in the image classification of Palembang Songket patterns, namely Chinese Flowers, Cantik Manis, and Pulir. The Support Vector Machine (SVM) method is a classification method that can recognize Palembang Songket patterns with RBF, Linear, and Polynomial kernels. The results showed that the RBF kernel was the best kernel that produced an average accuracy value of 88.1%, a precision of 84.1%, a recall of 82.2%, and an f1-score of 82.6%, and the three Palembang Songket patterns tested, it was found that the Palembang Songket patterns that were easiest to classify well were the Cantik Manis patterns for all types of SVM kernels.
Co-Authors Ade Hendri Pandrean Cahyati, Imelia Dwinora Celvine Adi Putra Derry Alamsyah Farisi, Ahmad Firda Novia Rahmawati Gracivo Elsion Victory Hadisaputra, William Hafiz Irsyad Julian Rusli Tee Baldi Juliana Nasution Kelvin Arianto M Ezar Al Rivan Marcella, Dewi Meiriyama, Meiriyama Meiriyama, Meiriyama Ningrum Larasati Novan Wijaya Nur Rachmat Nur Rachmat Oktavius, Kevin Pandrean, Ade Hendri Prabowo, Adrianus Prasthio, Rial RR. Ella Evrita Hestiandari Rusbandi Rusbandi Sanders, Carmel Edra Sandra Mareza Adelfi Saputra, Jordi Valentino Ruben Ho Wijaya, Albert Kusuma William Hadisaputra Wiwik Handayani Yohannes Yohannes