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


  • 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




Shape extraction, texture extraction, KNN


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%.

GS Cited Analysis


Download data is not yet available.


Abbad Ur Rehman, H., Lin, C. Y., & Mushtaq, Z. (2021). Effective K-Nearest Neighbor Algorithms Performance Analysis of Thyroid Disease. Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A, 44(1), 77–87.

Abdullah, A., Usman, U., & Efendi, M. (2017). . Jurnal Teknologi Informasi Dan Ilmu Komputer, 4(4), 297.

Ahmad, N., Asif, H. M. S., Saleem, G., Younus, M. U., Anwar, S., & Anjum, M. R. (2021). Leaf image-based plant disease identification using color and texture features. Wireless Personal Communications, 121(2), 1139-1168.

Castiglioni, I., Rundo, L., Codari, M., Di Leo, G., Salvatore, C., Interlenghi, M., Gallivanone, F., Cozzi, A., D’Amico, N. C., & Sardanelli, F. (2021). AI applications to medical images: From machine learning to deep learning. In Physica Medica (Vol. 83, pp. 9–24). Associazione Italiana di Fisica Medica.

Director, C. P. C. R. I., Kumar, N., Rath, B., Bhat, R., Hubballi, V. N., Das, S. S., ... & John, S. S. (2021). Coconut Development Board.

Henrietta, H. M., Kalaiyarasi, K., & Raj, A. S. (2022). Coconut Tree (Cocos nucifera) Products: A Review of Global Cultivation and its Benefits. Journal of Sustainability and Environmental Management, 1(2), 257–264.

Hidayati, N., & Hermawan, A. (2021). K-Nearest Neighbor (K-NN) algorithm with Euclidean and Manhattan in classification of student graduation. Journal of Engineering and Applied Technology, 2(2).

Ibrahim, I., & Abdulazeez, A. (2021). The Role of Machine Learning Algorithms for Diagnosing Diseases. Journal of Applied Science and Technology Trends, 2(01), 10–19.

Jane Alla, M. M., & Bello, R. N. (2021). Agricultural Productivity of Selected Philippine Crops: Effect of Climate Change in Cotabato Province. In International Journal of Innovative Science and Research Technology (Vol. 6).

Khairandish, M. O., Sharma, M., Jain, V., Chatterjee, J. M., & Jhanjhi, N. Z. (2022). A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images. IRBM, 43(4), 290–299.

Lopez-Bernal, D., Balderas, D., Ponce, P., & Molina, A. (2021). Education 4.0: Teaching the basics of knn, lda and simple perceptron algorithms for binary classification problems. Future Internet, 13(8).

Mat, K., Abdul Kari, Z., Rusli, N. D., Che Harun, H., Wei, L. S., Rahman, M. M., Mohd Khalid, H. N., Mohd Ali Hanafiah, M. H., Mohamad Sukri, S. A., Raja Khalif, R. I. A., Mohd Zin, Z., Mohd Zainol, M. K., Panadi, M., Mohd Nor, M. F., & Goh, K. W. (2022). Coconut Palm: Food, Feed, and Nutraceutical Properties. In Animals (Vol. 12, Issue 16). MDPI.

Nurhasanah, N., Wulansari, A., Rasulu, H., Tjokrodiningrat, S., Fahri, J., Suwito, S., Daud, N., & Alting, H. (2021). The Depiction of Coconut Products (Food and Non-Food) In Tidore Islands, North Maluku. International Journal on Food, Agriculture and Natural Resources, 2(3), 1–4.

O’doherty, E. (n.d.). Application of Black Soldier Fly Larvae to Convert Municipal Organic Waste to Value-Added Chemicals.

Patch, C. S., Sullivan, D. R., Fenech, M., Roodenrys, S., Keogh, J. B., Clifton, P. M., Williams, P. G., & Fazio, V. A. (2021). Ilomata International Journal of Social Science (IJSS) yogi .bac htia r@g oogl ema m Algorithm Configuration K-Nearest To Clarification Medicine Tree Based On Extraction, Variation Of Color, Texture And Shape Of Leaf. Ilomata International Journal of Social Science, 2(1).

Rahayu Marlis, R., Yunita, F., Provinsi Parit, J., & Hulu Indragiri Hilir Indonesia, T. (n.d.). SISTEMASI: Jurnal Sistem Informasi Sistem Prediksi Kualitas Kopra Putih Menggunakan k-Nearest Neighbor (k-NN).

Russell, R. G., Lovett Novak, L., Patel, M., Garvey, K. V., Craig, K. J. T., Jackson, G. P., Moore, D., & Miller, B. M. (2023). Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals. Academic Medicine, 98(3), 348–356.

Sujarwo, S., Maulidah, A. I., & Setiawan, B. (2022). Factors affecting expenditure and income of small fisherman households: Evidence from Jember, Indonesia. Journal of Socioeconomics and Development, 5(2), 240.

Tsourounis, D., Theodorakopoulos, I., Zois, E. N., & Economou, G. (2022). From text to signatures: Knowledge transfer for efficient deep feature learning in offline signature verification. Expert Systems with Applications, 189.

Ulysse, A. D.-K., Gustave, D., Edmond, S. A., Kolawolé, V. S., Farid, B.-M., & Kifouli, A. (2021). Ethnobotanical study of the coconut palm in the Coastal Zone of Benin. International Journal of Biodiversity and Conservation, 13(3), 152–164.


Crossmark Updates

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.