A Image Matching Comparison Using the K-Nearest Neighbor (KNN) Method and Support Vector Machine (SVM)
Abstract
Image matching is the process of finding digital images that have a degree of similarity. matching images using the classification method. In measuring image matching, the images used are original logo images and manipulated logo images. Comparison of classification algorithms from the two methods namely K-Nearest Neighbor (KNN) and Support Vector Machine with Sequential Minimal Optimization (SMO) optimization used to calculate matches based on accuracy values. The K-Nearest Neighbor (KNN) classification method is based on proximity or K calculations while the Support Vector Machine (SVM) classification method measures the distance between the hyperplane and the nearest data. Image match values are measured by Precision, Recall, F1-Score, and Accuracy. The image matching steps start from the preparation of data processing, extraction of HSV color features and shapes, then the classification stage. Digital images are used as many as 10 images consisting of one original logo and 9 manipulated logos. In the classification testing stage, using the WEKA application by applying the 10-fold cross-validation method. From the results of tests conducted that the closest k-neighbor (KNN) classification method is 80% and has a k = 0.889 which is quite good in measuring proximity, while the SVM classification method is 70%. The results of this image matching comparison can be concluded that the K-Nearest Neighbor classification method works better than SVM for image matching.
Downloads
References
T. T. Li, B. Jiang, Z. Z. Tu, B. Luo, and J. Tang, “Image matching using mutual k-nearest neighbor graph,” Commun. Comput. Inf. Sci., vol. 503, pp. 276–283, 2015.
J. I. Pengetahuan and D. A. N. T. Komputer, “Analisis Performa Algoritma Naive Bayes Pada,” vol. 5, no. 1, pp. 37–42, 2019.
A. Ciputra, “Dengan Algoritma Naive Bayes Dan Ekstraksi Fitur Citra Digital,” vol. 9, no. 1, pp. 465–472, 2018.
K. Adi and R. R. Isnanto, “Support Vector Machine Untuk Klasifikasi Citra Jenis Daging Berdasarkan Tekstur Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrices ( GLCM ),” vol. 01, pp. 1–10, 2016, doi: 10.21456/vol6iss1pp1-10.
M. F. Grace and H. S. Scott, “An optional federal charter for insurance: Rationale and design,” Futur. Insur. Regul. United States, vol. 6, no. 2, pp. 55–96, 2009.
& P. Rusydi Umar, Imam Riadi, “Perbandingan Metode SVM, RF dan SGD untuk Penentuan Model Klasifikasi Kinerja Programmer pada Aktivitas Media Sosial,” J. Resti, vol. 4, no. 2, pp. 329–335, 2020, [Online]. Available: https://doi.org/10.29207/resti.v4i2.1770.
K. Ayuningsih, Y. A. Sari, and P. P. Adikara, “Klasifikasi Citra Makanan Menggunakan HSV Color Moment dan Local Binary Pattern dengan Naïve Bayes Classifier,” vol. 3, no. 4, pp. 3166–3173, 2019.
R. Munir, “Pengantar Pengolahan Citra,” Pengolah. Citra Digit., no. Bagian 1, pp. 1–10, 2013.
P. N. Andono, Pengolahan Citra Digital. Yogyakarta: ANDI, 2015.
S. Gustina et al., “Identifikasi Tanaman Kamboja menggunakan Ekstraksi Ciri Citra Daun dan Jaringan Syaraf Tiruan,” vol. 2, no. 1, pp. 128–132, 2016, [Online]. Available: http://ars.ilkom.unsri.ac.id.
E. Ciri, M. Gray, L. C. Matrix, I. Sel, and D. Putih, “JOINTECS Ekstraksi Ciri Metode Gray Level Co-Occurrence Matrix Untuk,” vol. 3, no. 28, pp. 71–80, 2020.
G. A. Pradipta and P. D. Wulaning Ayu, “Perbandingan Segmentasi Citra Telur Ayam Menggunakan Metode Otsu Berdasarkan Perbedaan Ruang Warna Rgb Dan Hsv,” JST (Jurnal Sains dan Teknol., vol. 6, no. 1, pp. 136–147, 2017, doi: 10.23887/jst-undiksha.v6i1.9329.
N. Arifin and I. S. Areni, “Klasifikasi Kematangan Stroberi Berbasis Segmentasi Warna dengan Metode HSV,” vol. 23, no. 2, pp. 113–116, 2019, doi: 10.25042/jpe.112019.03.
E. F. Saraswita, “Akurasi Klasifikasi Citra Digital Scenes RGB Menggunakan Model K-Nearest Neighbor dan Naive Bayes,” Pros. Annu. Res. Semin., vol. 5, no. 1, pp. 978–979, 2019.
I. Riadi, R. Umar, and F. D. Aini, “Analisis Perbandingan Detection Traffic Anomaly Dengan Metode Naive Bayes Dan Support Vector Machine (Svm),” Ilk. J. Ilm., vol. 11, no. 1, pp. 17–24, 2019, doi: 10.33096/ilkom.v11i1.361.17-24.
R. A. Rizal, I. S. Girsang, and S. A. Prasetiyo, “Klasifikasi Wajah Menggunakan Support Vector Machine (SVM),” REMIK (Riset dan E-Jurnal Manaj. Inform. Komputer), vol. 3, no. 2, p. 1, 2019, doi: 10.33395/remik.v3i2.10080.
A. Wibowo, “Aplikasi Diagnosis Penyakit Kanker Payudara Menggunakan Algoritma Sequential Minimal Optimization,” J. Teknol. dan Sist. Komput., vol. 5, no. 4, p. 153, 2017, doi: 10.14710/jtsiskom.5.4.2017.153-158.
A. F. Hastawan, R. Septiana, and Y. E. Windarto, “Perbaikan Hasil Segmentasi Hsv Pada Citra Digital Menggunakan Metode Segmentasi Rgb Grayscale,” Edu Komputika J., vol. 6, no. 1, pp. 32–37, 2019, doi: 10.15294/EDUKOMPUTIKA.V6I1.23025.
Copyright (c) 2020 Dewi Astria Faroek
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal 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 (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) 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).
