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. 
Highlights
Samarinda sarong is one of the cultural treasures in the form of cloth from Samarinda, East Kalimantan
Berdasarkan hasil pengujian menunjukkan bahwa penerapan metode color moments, Gray Level Co-Occurrence Matrix (GLCM), dan Naive Bayes menggunakan percentage split (70%) mampu menghasilkan akurasi terbaik yaitu mencapai 0,987 dibanding menggunakan cross-validation (K=10) dengan akurasi 0,984
Robi’in, “Analisis Dekomposisi Wavelet Pada Pengenalan Pola Informasi), vol 4, no. 5, pp. 998–1006, 2020
Summary
Evaluasi metode dilakukan dengan menggunakan tiga parameter sebagai alat ukur pengujian yang terdiri dari precision, recall, dan akurasi. Ketiga parameter tersebut dihitung untuk mengetahui kinerja dari metode deteksi sarung Samarinda yang dikembangkan. Pada ekstraksi fitur dihasilkan 22 nilai fitur dari setiap citra sarung. Fitur yang digunakan yaitu warna dengan 3. Color Moments terdiri dari mean (μ) dan standar deviasi (σ) yang dihasilkan dari setiap channel warna RGB. Fitur tekstur dihasilkan menggunakan metode GLCM yang meliputi fitur kontras (F1), korelasi (F2), energi (F3), dan homogenitas (F4) dengan masingmasing sudut 00, 450, 900, dan 1350. Contoh dari hasil ekstraksi fitur warna maupun fitur tekstur dari beberapa 4. Sarung diperlihatkan pada Tabel 3 dan Tabel 4 Contoh dari hasil ekstraksi fitur warna maupun fitur tekstur dari beberapa 4. sarung diperlihatkan pada Tabel 3 dan Tabel 4
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