Identifikasi Kadar Ikan Pada Pempek Menggunakan Teknik Blok Citra Dengan Fitur GLCM Dan Metode JST

  • Nurdiana Dewi Saputri Universitas Multi Data Palembang
  • Gasim Gasim Universitas Multi Data Palembang
Keywords: Pempek, Image Blocks Model, GLCM, Artificial Neural Network

Abstract

As we know today, Indonesia has many unique foods, each in each region. For example, pempek is a typical food from Palembang, South Sumatra. The ingredients for making pempek do not only use fish, but there are many different dough formulas that create different flavor compositions. Differences that occur in the dough formula when making pempek will affect the texture and taste, because there is a mixture of fish and the amount of flour. The research uses image block techniques with GLCM (Gray Level Co-Occurrence Matrix) features and artificial neural network methods. The GLCM (Gray Level Co-Occurrence Matrix) feature extraction used consists of Entropy, Standard Deviation, Contrast, Angular Second Moment (ASM)/ Homogeneity, Correlation, and Inverse Different Moment (IDM)/ Energy. The dataset used in this study is to use the best results at a portrait distance of 13 cm from previous studies. There are 4 types of comparisons used, namely 1 fish 1 flour, 1.5 fish 1 flour, 2 fish 1 flour, and 1 fish 2 flour. The recognition results obtained in this study were 360 recognized training data and 89 recognized test data and obtained an accuracy rate of 37.08%.

Published
2022-10-05
How to Cite
Saputri, N., & Gasim, G. (2022, October 5). Identifikasi Kadar Ikan Pada Pempek Menggunakan Teknik Blok Citra Dengan Fitur GLCM Dan Metode JST. Jurnal Algoritme, 3(1), 99-113. Retrieved from https://jurnal.mdp.ac.id/index.php/algoritme/article/view/3362