Deteksi Sarung Samarinda Menggunakan Metode Naive Bayes Berbasis
Pengolahan Citra

Deteksi Sarung Samarinda Menggunakan Metode Naive Bayes Berbasis
Pengolahan Citra

Samarinda Sarong, color moments, GLCM, feature extraction, Naive Bayes, classification

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 nonSamarinda 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.

Anindita Septiarini1
, Rizqi Saputra2
, Andi Tejawati3
, Masna Wati4

Universitas Mulawarman

25-10-2021

Fajar bagus W

PDF

Indonesia

Text