DETEKSI KEPITING MOLTING MENGGUNAKAN TEKNIK KLASIFIKASI MACHINE LEARNING
Indonesia
DOI:
https://doi.org/10.31949/jensitec.v8i01.1909Abstract
Soft crab is an export product where foreign demand is much higher than production. In the production of soft crabs, it is done by keeping the crabs individually in a crab box which is placed in the pond until they molt. Molting is a natural process of molting, i.e. removing the old tough skin for growth purposes. Shortly after molting, the new crab shells are still very soft and will harden again after water absorption occurs. Therefore it is important to monitor molting crabs to help farmers in the cultivation of soft shell crabs. The number of crab datasets is 1060 which consists of 1000 training data and 60 testing data. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest Classifier (RFC). KNN, SVM, and RFC are classification algorithms from Machine Learning. This study aims to compare the performance of the three algorithms so that the performance of the three algorithms is known. Several parameters are used to configure the KNN, SVM, and RFC algorithms. From the results of the trials conducted, KNN has the best performance with 98.33% accuracy, 98.33% precision, 98.38% recall, and 98.38% F1 Score.
Keywords:
Molting crab classification, Image Processing, Machine Learning, KNN, RFC, and SVM.Downloads
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[2] H. Kudsiah, S. W. Rahim, M. A. Rifa’i, and Arwan, “Demplot Pengembangan Budidaya Kepiting Cangkang Lunak Di Desa Salemba, Kecamatan Ujung Loi, Kabupaten Bulukumba Sulawesi Selatan,” J. Panrita Abdi Univ. Hasanuddin, vol. 2, no. 2, pp. 151–164, 2018, [Online]. Available: https://journal.unhas.ac.id/index.php/panritaabdi/issue/view/518.
[3] Y. Fujaya, “Pertumbuhan dan molting kepiting bakau yang diberi dosis vitomolot berbeda Growth and molting of mud crab administered by different doses of vitomolt,” J. Indones. Aquac., vol. 10, no. 1, pp. 24–28, 2011.
[4] R. Ario, A. Djunaedi, I. Pratikto, P. Subardjo, and F. Farida, “Perbedaan Metode Mutilasi Terhadap Lama Waktu Molting Scylla serrata,” Bul. Oseanografi Mar., vol. 8, no. 2, p. 103, 2019, doi: 10.14710/bu- loma.v8i2.24886.
[5] N. A. Yushinta Fujaya, Siti Aslamyah, Letty Fudjaja, Budidaya dan Bisnis Kepiting Lunak: Stimulasi Molting Dengan Ekstrak Bayam. Surabaya: Firstbox Media, 2019.
[6] F. F. Ferdiansyah, B. Rahmat, and I. Yuniar, “Klasifikasi Dan Pengenalan Objek Ikan Menggunakan Algoritma Support Vector Machine (Svm),” J. Inform. dan Sist. Inf. ( JIFoSI ), vol. 1, no. 2, pp. 522–528, 2020.
[7] M. Ramadhani and D. H. Murti, “Klasifikasi Ikan Menggunakan Oriented Fast and Rotated Brief (Orb) Dan K-Nearest Neighbor (Knn),” JUTI J. Ilm. Teknol. Inf., vol. 16, no. 2, p. 115, 2018, doi: 10.12962/j24068535.v16i2.a711.
[8] M. M. M. Fouad, H. M. Zawbaa, N. El-Bendary, and A. E. Hassanien, “Automatic Nile Tilapia fish classification approach using machine learning techniques,” 13th Int. Conf. Hybrid Intell. Syst. HIS 2013, pp. 173–178, 2014, doi: 10.1109/HIS.2013.6920477.
[9] L.-D. Quach, L. Q. Hoang, N. D. Trung, and C. N. Nguyen, “Towards Machine Learning Approaches To Identify Shrimp Diseases Based on Description,” 2020, doi: 10.15625/vap.2019.00063.
[10] R. Ali, M. M. Yusro, M. S. Hitam, and M. Ikhwanuddin, “Machine Learning With Multistage Classifiers For Identification Of Of Ectopara- site Infected Mud Crab Genus Scylla,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 2, pp. 406–413, 2021, doi: 10.12928/TELKOMNIKA.v19i2.16724.
[11] D. Abdullah and E. D. Putra, “Komparasi Perbaikan Kualitas Segmentasi Pada Citra Digital Metode Fuzzy C-Means Dan Otsu,” Pseudocode, vol. 4, no. 1, pp. 71–80, 2017.
[12] D. S. Tobias and A. R. Widiarti, “Deteksi Glaukoma pada Citra Fundus Retina dengan Metode K-Nearest Neighbor,” in Seminar Nasional Ilmu Komputer(SNIK 2016), 2016, pp. 92–99.
[13] F. Muwardi and A. Fadlil, “Sistem Pengenalan Bunga Berbasis Pengo- lahan Citra dan Pengklasifikasi Jarak,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 3, no. 2, pp. 124–131, 2017.
[14] N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005, vol. 1, pp. 886–893.
[15] E. F. Saraswita, “Akurasi Klasifikasi Citra Digital Scenes RGB Menggu- nakan Model K-Nearest Neighbor dan Naive Bayes,” Pros. Annu. Res. Semin., vol. 5, no. 1, pp. 978–979, 2019.
[16] Mustakim and G. Oktaviani F, “Algoritma K-Nearest Neighbor Classi- fication Sebagai Sistem Prediksi Predikat Prestasi Mahasiswa,” vol. 13, no. 2, pp. 195–202, 2016.
[17] A. Pandey and A. Jain, “Comparative Analysis of KNN Algorithm using Various Normalization Techniques,” Int. J. Comput. Netw. Inf. Secur., vol. 9, no. 11, pp. 36–42, 2017.
[18] E. Ahishakiye, E. O. Omulo, D. Taremwa, and I. Niyonzima, “Crime prediction using Decision Tree (J48) classification algorithm,” Int. J. Comput. Inf. Technol., vol. 06, no. 03, pp. 188–195, 2017.
[19] T. B. Sasongko, “Komparasi dan Analisis Kinerja Model Algoritma SVM dan PSO-SVM (Studi Kasus Klasifikasi Jalur Minat SMA),” J. Tek. Inform. dan Sist. Inf., vol. 2, no. 2, pp. 244–253, 2016.
[20] Y. Xu, X. Zhao, Y. Chen, and Z. Yang, “Research on a mixed gas classification algorithm based on extreme random tree,” Appl. Sci., vol. 9, no. 9, 2019.
[21] DOKMANIC, I., PARHIZKAR, R., RANIERI, J. AND VETTERLI, M., 2015. Euclidean Distance Matrices: Essential Theory, Algorithms and Applications. IEEE Signal Processing Magazine, [online] 32(6), pp.12–30. Available at:¡http://arxiv.org/abs/1502.07541¿ [Accessed 29 Dec. 2020].
[22] ¸ OLAKOG˘ LU, H.B., 2019. A generalization of the Minkowski distance and a new definition of the ellipse. [online] Available at:¡http://arxiv.org/abs/1903.09657¿ [Accessed 29 Dec. 2020].
[23] Sari, V., Firdausi, F., & Azhar, Y. (2020). Perbandinga Prediksi Kualitas Kopi Arabika dengan Menggunakan Algoritma SGD, Random Forest dan Naive Bayes. Edumatic:Jurnal Pendidikan Informatika, 4(2), 1–9. https://doi.org/10.29408/edumatic.v4i2.2202.
[24] Patil, N. M., & Nemade, M. U. (2017). Music Genre Classification Using MFCC , K-NN and SVM Classifier. International Journal of Computer Applications, 4(2), 43–47
Published
2022-01-21
How to Cite
rezkiawan , runal ., Niswar, M. ., & Ahmad Ilham, A. . (2022). DETEKSI KEPITING MOLTING MENGGUNAKAN TEKNIK KLASIFIKASI MACHINE LEARNING: Indonesia. J-ENSITEC (Journal of Engineering and Sustainable Technology), 8(01), 599–610. https://doi.org/10.31949/jensitec.v8i01.1909
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