Development of an Intelligent Imaging System for Determining Maturity of Copra Flesh in Coconuts Using Shape and Texture Extraction

Authors

  • Yogi Wiyandra Fakultas Ilmu Komputer, Program Studi Sistem Komputer, UPI YPTK Padang
  • Firna Yenila Fakultas Ilmu Komputer, Program Studi Sistem Komputer, UPI YPTK Padang
  • Suci Wahyuni Fakultas Ilmu Komputer, Program Studi Teknik Informatika, UPI YPTK Padang

DOI:

10.33395/sinkron.v8i2.13369

Keywords:

Shape extraction, texture extraction, KNN

Abstract

Copra is dried coconut meat that is used to produce coconut oil. According to the Central Statistics Agency (BPS), Indonesia's copra production in 2020 reached 2.3 million tonnes. This is one form of the process of improving the economy of people living on the coast. This research was conducted to educate farmers in determining the level of maturity of the copra meat produced. This research was conducted using an extraction method that involves colour extraction and texture extraction. the method is used to provide convenience in seeing the level of maturity of the two characteristics of copra obtained in the field, namely texture and colour. The process obtained in the training with one of the images used as a test image in colour extraction produces area, perimeter, metric and eccentricity values in label 3 with values of 651.00, 184.69, 0.24 and 0.89. while in the feature extraction method the results are obtained with an average intensity value of 243.31, standard deviation of intensity 39.76 and entropy value of the tested image 4.57. The method is able to perform a detection process so that it can determine the level of maturity of copra seen from the existing types of copra such as asalan copra, regular copra, black copra and wet copra, each of which provides different functions in the copra processing stage. The process will be carried out using KNN which is seen from all test data and training data stored after the detection process. The results of the process carried out using digital images involving the extraction method for detection and KNN for classification are able to provide the right value. This is evidenced by the better accuracy value of 98%.

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How to Cite

Wiyandra, Y., Yenila, F., & Wahyuni, S. (2024). Development of an Intelligent Imaging System for Determining Maturity of Copra Flesh in Coconuts Using Shape and Texture Extraction. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 789-796. https://doi.org/10.33395/sinkron.v8i2.13369