Identification of beef and pork using gray level co-occurrence matrix and probabilistic neural network
DOI:
https://doi.org/10.31315/cip.v1i1.6126Abstract
Objective: Identify images of beef and pork using texture feature extraction Gray Level Co-Occurrence Matrix and Probabilistic Neural Network classification algorithm.
Design/method/approach: Apply texture feature extraction to Gray Level Co-Occurrence Matrix and Probabilistic Neural Network Classifier to perform classification.
Results: From the test results with k-fold cross-validation and confusion matrix, it shows that feature extraction of Gray Level Co-Occurrence Matrix and Probabilistic Neural Network Classifier get an average accuracy of 87%, precision 83%, and recall 90%.
Authenticity/state of the art: In this study, several scenarios were tested, namely the effect of using resize, brightness, and rotate values. Using a resize value of 256 x 256 pixels from the test results got the best accuracy of 87%. The brightness test of 20% affects the accuracy rate of 86% on increasing brightness and 90% on reducing brightness. In contrast, the test on the rotated image does not affect the accuracy results. The average accuracy obtained is 87%. The data in this study were obtained by collecting primary data on images of beef and pork in several markets in Denpasar.
References
Wahyuningsih, “Pusat Data dan Sistem Informasi Pertanian Sekretariat Jenderal Kementerian Pertanian,” Bul. Konsumsi Pangan, vol. 09, no. 01, pp. 32–42, 2019.
M. M. Ir. Adi Nugroho, Provinsi Bali Dalam Angka 2020 Bali Province in Figures 2020. ©BPS Provinsi Bali/BPS - Statistics of Bali Provinc, 2020.
Neneng, K. Adi, and R. Isnanto, “Support Vector Machine Untuk Klasifikasi Citra Jenis Daging Berdasarkan Tekstur Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrices (GLCM),” J. Sist. Inf. Bisnis, vol. 6, no. 1, p. 1, 2016, doi: 10.21456/vol6iss1pp1-10.
R. de S. Fernando, R. S. William, and P. Helio, “Multi-scale gray level co-occurrence matrices for texture description,” Neurocomputing, vol. 120, pp. 336–345, 2013, doi: 10.1016/j.neucom.2012.09.042.
E. Budianita, Jasril, and L. Handayani, “Implementasi Pengolahan Citra dan Klasifikasi K-Nearest Neighbour Untuk Membangun Aplikasi Pembeda Daging Sapi dan Babi Berbasis Web,” J. Sains dan Teknol. Ind., vol. 12, no. Vol 12, No 2 (2015): Juni 2015, pp. 242–247, 2015, [Online]. Available: http://ejournal.uin-suska.ac.id/index.php/sitekin/article/view/1005.
H. Wibowo and A. E. Minarno, “KLASIFIKASI CITRA MENGGUNAKAN MULTI TEXTON,” Semin. Teknol. dan Rekayasa, pp. 978–979, 2015.
R. Destiana, Y. N. Nasution, and S. Wahyuningsih, “Klasifikasi Probabilistic Neural Network (PNN) pada Data Diagnosa Penyakit Demam Berdarah,” Pros. Semin. Nas. Mat. Stat. dan Apl., pp. 15–21, 2019.
S. Kusumadewi, “Klasifikasi Pola Menggunakan Jaringan Probabilistik,” Semin. Nas. Apl. Teknol. dan Inf. 2014, pp. 65–72, 2014.
M. Astiningrum, M. Mentari, and R. R. N. Rachma, “Deteksi Kesegaran Daging Sapi Berdasarkan,” Semin. Inform. Apl., pp. 217–222, 2014.
U. Andayani, A. Wijaya, R. F. Rahmat, B. Siregar, and M. F. Syahputra, “Fish Species Classification Using Probabilistic Neural Network,” J. Phys. Conf. Ser., vol. 1235, no. 1, 2019, doi: 10.1088/1742-6596/1235/1/012094.
A. M. MH Purnomo, Konsep pengolahan citra digital dan ekstraksi fitur. Yogyakarta: Graha Ilmu, 2010.
F. Albregtsen, “Statistical Texture Measures Computed from Gray Level Coocurrence Matrices,” Arte, Individuo y Soc., vol. 22, no. 1, pp. 59–73, 2008, doi: 10.5209/ARIS.6586.
Adi Triyanto, “Ekstraksi Ciri Pada Data Suara Menggunakan Spektra Orde Tinggi Dan Kuantisasi Vektor Untuk Identifikasi Pembicara Menggunakan Jaringan Neural Buatan”,” Tesis, Progr. Pasca Sarj. Ilmu Komputer, Univ. Indones., 2000, [Online]. Available: https://lontar.cs.ui.ac.id/Lontar/opac/themes/newui/detail.jsp?id=7198&lokasi=lokal.
S. Haykin, Neural Networks : A Comprehensive Foundation. United States: Prentice Hall PTRUpper Saddle River, NJUnited States, 1994.
F. Gorunescu, Data Mining Concepts, Models and Techniques. German: Springer, Berlin, Heidelberg, 2011.
A. P. Chazhoor, “ROC curve in machine learning,” 2019. https://towardsdatascience.com/roc-curve-in-machine-learning-fea29b14d133.
Suwarno and A. A. Abdillah, “Penerapan Algoritma Bayesian Regularization Backpropagation Untuk Memprediksi Penyakit Diabetes,” J. MIPA, vol. 39, no. 2, pp. 150–158, 2016.
Downloads
Published
Issue
Section
License
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal. Please also carefully read SITech's Posting Your Article Policy.
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with Computing and Information Processing Letters agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Licensing for Data Publication
Science in Information Technology Letters use a variety of waivers and licenses, that are specifically designed for and appropriate for the treatment of data:
Open Data Commons Attribution License, http://www.opendatacommons.org/licenses/by/1.0/ (default)
Creative Commons CC-Zero Waiver, http://creativecommons.org/publicdomain/zero/1.0/
Open Data Commons Public Domain Dedication and Licence, http://www.opendatacommons.org/licenses/pddl/1-0/
Other data publishing licenses may be allowed as exceptions (subject to approval by the editor on a case-by-case basis) and should be justified with a written statement from the author, which will be published with the article.
Open Data and Software Publishing and Sharing
The journal strives to maximize the replicability of the research published in it. Authors are thus required to share all data, code, or protocols underlying the research reported in their articles. Exceptions are permitted but have to be justified in a written public statement accompanying the article.
Datasets and software should be deposited and permanently archived in appropriate, trusted, general, or domain-specific repositories (please consult http://service.re3data.org and/or software repositories such as GitHub, GitLab, Bioinformatics.org, or equivalent). The associated persistent identifiers (e.g., DOI or others) of the dataset(s) must be included in the data or software resources section of the article. Reference(s) to datasets and software should also be included in the article's reference list with DOIs (where available). Where no domain-specific data repository exists, authors should deposit their datasets in a general repository such as ZENODO, Dryad, Dataverse, or others.
Small data may also be published as data files or packages supplementary to a research article; however, the authors should prefer a deposition in data repositories in all cases.