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Rizqi Saputra
Universitas Mulawarman
Computer Science & IT Engineering

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Deteksi Sarung Samarinda Menggunakan Metode Naive Bayes Berbasis Pengolahan Citra Anindita Septiarini; Rizqi Saputra; Andi Tejawati; Masna Wati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (802.952 KB) | DOI: 10.29207/resti.v5i5.3435

Abstract

Samarinda sarong is one of the cultural treasures in the form of cloth from Samarinda, East Kalimantan. It has a characteristic in the form of a square motif with a unique color combination. However, several people do not know the difference between a Samarinda sarong and a non-Samarinda sarong because the Samarinda sarongs may have a similar motif or color to a non-Samarinda sarong. This study aims to develop a Samarinda sarong detection method to distinguish between the sarong of Samarinda and non-Samarinda. The detection of the Samarinda sarong was carried out based on two features: color and texture. The feature extraction of color was applied using color moments and Gray Level Co-Occurrence Matrix (GLCM) for texture. The classification was implemented using the Naive Bayes method. The dataset used consists of 250 sarong images (150 Samarinda sarong images and 100 Non-Samarinda sarong images) divided into training and test data. It was divided using percentage split and cross-validation. The test results show the implementation of the color moments, GLCM, and Naive Bayes methods using a percentage split (70%) produce the best accuracy of 0.987 compared to using cross-validation (K=10) with an accuracy of 0.984. The difference may occur because the number of training and testing data used on percentage split and cross-validation is different. Moreover, the sarong images used on training and test data were chosen randomly.
Co-Authors Anindita Septiarini, Anindita Tejawati, Andi Wati, Masna