Identifikasi Jenis Ras pada Kucing Menggunakan Algoritma Support Vector Machine (SVM)
DOI:
https://doi.org/10.26905/jasiek.v6i1.10989Keywords:
Ras Kucing, Klasifikasi, Machine Learning, Suppor Vector MachineAbstract
Kucing adalah mamalia karnivora kecil yang dikenal sebagai satu-satunya spesies jinak dalam keluarga Felidae . Di Indonesia, beberapa jenis ras kucing populer sebagai hewan peliharaan, antara lain Bengal, Ragdoll, Russian Blue, Siamese, dan Persia. Namun, keberagaman ras kucing ini seringkali membuat pemiliknya kesulitan mengidentifikasi jenis ras yang dimiliki oleh kucing mereka. Penelitian ini bertujuan untuk melakukan klasifikasi pada lima jenis ras kucing tersebut menggunakan metode Support Vector Machine (SVM) dengan kernel Radial Basis Function(RBF). Data yang digunakan terdiri dari tiga jenis dataset, yaitu data latih sebanyak 1400 sampel, data validasi sebanyak 600 sampel dan data uji sebanyak 250 sampel. Hasil penelitian menunjukkan bahwa model yang dibangun menggunakan metode SVM dengan kernel RBF berhasil mencapai tingkat akurasi sebesar 86%, presisi sebesar 87%, recall sebesar 86%, dan f1 score sebesar 86%. Hasil tersebut menandakan bahwa model klasifikasi ini mampu melakukan prediksi dengan tingkat keakuratan yang baik.
References
A. A. Bardekar dan V. M. Deshmukh, “An Empirical Study : Musical Influence on Face Using the Local Binary Pattern ( LBP ) Approach,†2012.
Y. Li, H. Tang, W. Xie, dan W. Luo, “Multidimensional Local Binary Pattern for Hyperspectral Image Classification,†IEEE Trans. Geosci. Remote Sens., vol. 60, hal. 1–13, 2022.
D. Ayon, “Machine Learning Algorithms : A Review,†Int. J. Comput. Sci. Inf. Technol., vol. 7, no. 3, hal. 1174–1179, 2016, doi: 10.21275/ART20203995.
R. R. Jaka Kusuma1, Abwabul Jinan2, Muhammad Zulkarnain Lubis3, Rubianto4, “Komparasi Algoritma Support Vector Machine Dan Naive Bayes Pada Klasifikasi Ras Kucing,†J. Generic, vol. 14, no. 1, hal. 8–12, 2022, [Daring]. Tersedia pada: http://generic.ilkom.unsri.ac.id/index.php/generic/article/view/122
D. A. Pisner dan D. M. Schnyer, Support vector machine. Elsevier Inc., 2019. doi: 10.1016/B978-0-12-815739-8.00006-7.
J. Mase, M. T. Furqon, dan B. Rahayudi, “Penerapan Algoritme Support Vector Machine ( SVM ) Pada Pengklasifikasian Penyakit Kucing,†J. Pegembangan Teknol. Inf. dan Ilmu Komput., vol. 2, no. 10, hal. 3648–3654, 2018.
I. Muslihah dan M. Muqorobin, “Texture Characteristic of Local Binary Pattern on Face Recognition with Probabilistic Linear Discriminant Analysis,†Int. J. Coop. Inf. Syst., vol. 1, hal. 22–26, 2020.
A. Suryadibrata dan S. D. Salim, “Klasifikasi Anjing dan Kucing menggunakan Algoritma Linear Discriminant Analysis dan Support Vector Machine,†Ultim. J. Tek. Inform., vol. 11, no. 1, hal. 46–51, 2019, doi: 10.31937/ti.v11i1.1076.
A. P. Bradley, “The use of the area under the ROC curve in the evaluation of machine learning algorithms,†Pattern Recognit., vol. 30, no. 7, hal. 1145–1159, 1997, doi: 10.1016/S0031-3203(96)00142-2.
Fernanda Januar Pratama, Wikky Fawwaz Al Maki, dan Febryanti Sthevanie, “Big Cats Classification Based on Body Covering,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 5, hal. 984–991, 2021, doi: 10.29207/resti.v5i5.3328.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
The journal allow the authors to hold the copyright without restrictions and allow the authors to retain publishing rights without restrictions.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement 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 acknowledgement 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 (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
