Klasifikasi Citra Bekicot Menggunakan Algoritma Support Vector Machine
DOI:
https://doi.org/10.35960/ikomti.v6i2.1790Keywords:
Snails, SVM Classification, Confusion Matrix, Machine Learning, Image ProcessingAbstract
Snails are one of the animals that are widely found in Indonesia, but they are often considered pests and are not used optimally. In fact, there are several types of snails that have high economic value and can be exported, especially to countries such as France that use snails as restaurant cuisine. On a small scale, the process of classifying local and imported snails can be done manually however, if there are a lot of them, an automated system is needed to help the classification process become faster and more accurate. This study proposes a classification method based on Support Vector Machine (SVM) to distinguish local and imported snails. SVM is used as a classification model because of its ability to handle high-dimensional data and complex patterns. The results of the study showed an accuracy of 54%, so it can be an effective solution in the process of sorting snails on a large scale.
References
[1] N. Sciences and Y. State, “Aktivitas Harian Bekicot ( Achatina fulica ) di Kebun Biologi , Fakultas Matematika dan Ilmu Pengetahuan Alam” Sains Mat., vol. 9, no. 2, pp. 46–53, 2024.
[2] S. NOR, M. A. MUSLIM, and M. ASWIN, “Pengenalan Pola Dasar Angka berdasarkan Gerakan Tangan menggunakan Machine Learning,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 10, no. 3, p. 595, 2022, doi: 10.26760/elkomika.v10i3.595.
[3] B. C. Octariadi, Y. Brianorman, U. M. Pontianak, U. M. Pontianak, and S. V. Machine, “Pengenalan Pola Tanda Tangan Menggunakan Metode Support Vector Machine,” INFOTECH J., vol. 5, no. 2, pp. 16–22, 2019.
[4] M. Resa Arif Yudianto, P. Sukmasetya, R. Abul Hasani, and D. Sasongko, “Pengaruh Data Preprocessing terhadap Imbalanced Dataset pada Klasifikasi Citra Sampah menggunakan Algoritma Convolutional Neural Network,” Build. Informatics, Technol. Sci., vol. 4, no. 3, pp. 1367–1375, 2022, doi: 10.47065/bits.v4i3.2575.
[5] A. Peryanto, A. Yudhana, and R. Umar, “Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation,” J. Appl. Informatics Comput., vol. 4, no. 1, pp. 45–51, 2020, doi: 10.30871/jaic.v4i1.2017.
[6] G. Buatan, T. Menggunakan, and M. Deep, “Rancang bangun aplikasi uji kemiripan gambar ai generative dan gambar buatan tangan menggunakan metode deep learning,” J. TEKINKOM, vol. 7, pp. 114–123, 2024, doi: 10.37600/tekinkom.v7i1.1192.
[7] A. D. Putro and A. Hermawan, “Pengaruh Cahaya dan Kualitas Citra dalam Klasifikasi Kematangan Pisang Cavendish Berdasarkan Ciri Warna Menggunakan Artificial Neural Network,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 1, pp. 215–228, 2021, doi: 10.30812/matrik.v21i1.1396.
[8] A. Peryanto, A. Yudhana, and R. Umar, “Convolutional Neural Network and Support Vector Machine in Classification of Flower Images,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 8, no. 1, pp. 1–7, 2021, doi: 10.23917/khif.v8i1.15531.
[9] K. Ariadi et al., “PERBANDINGAN PERFORMA METODE SVM DAN KNN DALAM MENGKLASIFIKASIKAN CITRA INFEKSI TELINGA,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 6, pp. 11342–11347, 2024.
[10] M. Farid Naufal, “Perbandingan, Analisis Svm, Algoritma Untuk, dan CNN,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 2, pp. 311–318, 2021, doi: 10.25126/jtiik.202184553.
[11] A. G. Sooai, P. A. Nani, N. M. R. Mamulak, C. O. Sianturi, S. C. Sianturi, and A. H. Mondolang, “Klasifikasi Citra Daun Anggur Menggunakan SVM Kernel Linear,” JOINTECS (Journal Inf. Technol. Comput. Sci., vol. 8, no. 1, p. 19, 2023, doi: 10.31328/jointecs.v8i1.4496.
[12] E. Puerwandono and I. Maulana, “Penerapan Algoritma SVM Untuk Klasifikasi Citra Daun Sirih,” INTECOMS J. Inf. Technol. Comput. Sci., vol. 6, no. 2, pp. 859–865, 2023, doi: 10.31539/intecoms.v6i2.7761.
[13] N. L. Chusna, M. I. Shalahudin, U. Riyanto, and A. D. Alexander, “Klasifikasi Citra Jenis Tanaman Jamur Layak Konsumsi Menggunakan Algoritma Multiclass Support Vector Machine,” Build. Informatics, Technol. Sci., vol. 4, no. 1, pp. 178–183, 2022, doi: 10.47065/bits.v4i1.1624.
[14] A. Peryanto, A. Yudhana, and R. Umar, “Rancang Bangun Klasifikasi Citra Dengan Teknologi Deep Learning Berbasis Metode Convolutional Neural Network,” FORMAT J. Ilm. Tek. Inform., vol. 8, no. 2, pp. 138–147, 2019.
[15] N. 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, pp. 1–10, 2016, doi: 10.21456/vol6iss1pp1-10.
[16] Andi Saenong and R. Rahmat, “Implementasi Algoritma SVM Untuk Sistem Deteksi dan Pengawasan Keamanan Kendaraan di Area Parkir Menggunakan Kamera,” Simkom, vol. 9, no. 2, pp. 267–277, 2024, doi: 10.51717/simkom.v9i2.570.
[17] I. Muhamad Malik Matin, “Hyperparameter Tuning Menggunakan GridsearchCV pada Random Forest untuk Deteksi Malware,” Multinetics, vol. 9, no. 1, pp. 43–50, 2023, doi: 10.32722/multinetics.v9i1.5578.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ari Peryanto, Lukmanul Hakim, Achmad Nugrahantoro
This work is licensed under a Creative Commons Attribution 4.0 International License.
