Perbandingan Metoda Klasifikasi K-Nearest Neighbor dan Support Vector Machine pada Pengenalan Benda Terhalang berbasis Kode Rantai

RAHMADI KURNIA, MELIA ASMITA, ROZAKY IHSAN, IKHWANA ELFITRI, DANANG KUMARA HADI

Sari


ABSTRAK

Benda yang terhalang oleh benda lain memiliki bentuk yang tidak sempurna karena sebagian sisinya tidak terlihat. Untuk mengatasi permasalahan tersebut, digunakan metoda yang dapat mengenali bentuk pada benda pada sisi yang masih nampak. Penelitian ini membandingkan metoda klasifikasi K-Nearest Neighbor (KNN) dan Support Vector Machine (SVM) berbasis kode rantai untuk mendeteksi bentuk benda terhalang. Terdapat 15 sampel untuk lima bentuk bangun datar pada 2 jenis citra benda. Hasil untuk dua jenis citra, metoda KNN memiliki rata-rata ketepatan sebesar 89,6% sedangkan metoda SVM sebesar 88.4%. Waktu komputasi citra animasi menggunakan metoda SVM lebih cepat 0,044 detik dari pada metoda KNN dan lebih cepat 0,034 detik untuk citra riil. Rata-rata memori yang digunakan dengan metoda SVM pada citra animasi lebih sedikit 0,32 Mb dari pada metoda K-NN Pada citra riil rata-rata memori yang digunakan dengan metoda SVM lebih sedikit 0,44 Mb dari metoda K-NN.

Kata kunci: transformasi Hough, kode rantai, bentuk benda, KNN, SVM

 

ABSTRACT

Object that are blocked by other objects have an imperfect shape because some of their side are not visible. To overcome this problem, we propose a comparison the K Nearest Neighbor classification (K-NN) and the Support Vector Machine (SVM) method based on chain code algorithm. We used 15 samples for each shape of the object for two kind of images. The result of KNN method classification has an average accuracy of 89,6%. The SVM method has an average accuracy of 88.4%. The average computing time for the SVM method is 0,044 seconds faster than KNN method for drawing image and 0,0034 seconds faster for real images, The average memory for drawing image using the SVM method is 0,32Mb less than K-NN. In the real images the average memory used with the SVM method is 0,44 Mb less than the K-NN.

Keywords: hough transform, chain code, shape object, KNN, SVM


Kata Kunci


transformasi hough; kode rantai; bentuk benda; KNN; SVM

Teks Lengkap:

PDF

Referensi


Azmia, A. N., Nasien, D., & Samah, A. A. (2016). Freeman chain code as representation in offline signature verification system. Jurnal Teknologi, 78(8–2), 89–94. https://doi.org/10.11113/jt.v78.9546

Bo, C., Lu, H., & Wang, D. (2016). Hyperspectral Image Classification via JCR and SVM Models with Decision Fusion. IEEE Geoscience and Remote Sensing Letters, 13(2), 177–181. https://doi.org/10.1109/LGRS.2015.2504449

Cong, X., Li, S., Chen, F., Liu, C., & Meng, Y. (2023). A Review of YOLO Object Detection Algorithms based on Deep Learning. Frontiers in Computing and Intelligent Systems, 4(2), 17–20.

Dalitz, C., Schramke, T., & Jeltsch, M. (2017). Iterative hough transform for line detection in 3D point clouds. Image Processing On Line, 7, 184–196. https://doi.org/10.5201/ipol.2017.208

Duan, D., Xie, M., Mo, Q., Han, Z., & Wan, Y. (2010). An improved Hough transform for line detection. ICCASM 2010 - 2010 International Conference on Computer Application and System Modeling, Proceedings, 2, 354–357. https://doi.org/10.1109/ICCASM.2010.5620827

Fating, K., & Ghotkar, A. (2014). Performance Analysis of Chain Code Descriptor for Hand Shape Classification. International Journal of Computer Graphics & Animation, 4(2), 9–19. https://doi.org/10.5121/ijcga.2014.4202

G, A., T, H. N., Kumari, J., & M, S. (2013). Analysis of Digital Images Using Morphlogical Operations. International Journal of Computer Science and Information Technology , 5(1), 145–159. https://doi.org/10.5121/ijcsit.2013.5112

Girshick, R. (2015). Fast R-CNN. Proceeding of IEEE International Conference of Computer Vision, 1440–1448.

Hasan, H., Haron, H., & Hashim, S. Z. M. (2011). Heuristic algorithm to generate modified freeman chain code from thinned binary image. Australian Journal of Basic and Applied Sciences, 5(11), 752–762.

Hasan, M. A. M., Nasser, M., Pal, B., & Ahmad, S. (2014). Support Vector Machine and Random Forest Modeling for Intrusion Detection System (IDS). Journal of Intelligent Learning Systems and Applications, 06(01), 45–52. https://doi.org/10.4236/jilsa.2014.61005

Junaidi, M., & Khuzaini, K. (2020). Big Data Analysis Model Profitability Ratio in Determining Prediction of Company Performance Era 4.0. 1st International Conference of Business, 793–807.

Kabir, M. M., Ohi, A. Q., Rahman, M. S., & Mridha, M. F. (2020). An Evolution of CNN object classifiers on low-resolution images. HONET 2020 - IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI, 209–213. https://doi.org/10.1109/HONET50430.2020.9322661

Kurnia, R., Asmita, M., & Elfitri, I. (2017). Object detection on hindered condition by using chain code-based angle detection. ACM International Conference Proceeding Series, 2017-October, 50–56. https://doi.org/10.1145/3145777.3145780

Muralidharan, R., & Chandrasekar, C. (2011). Object Recognition using SVM-KNN based on Geometric Moment Invariant. International Journal of Computer Trends and Technology-July to Aug Issue, 215–220. http://www.internationaljournalssrg.org

Rachmawati, E., Khodra, M. L., & Supriana, I. (2017). Shape based recognition using freeman chain code and modified Needleman-Wunsch. Proceedings of 2016 8th International Conference on Information Technology and Electrical Engineering: Empowering Technology for Better Future, ICITEE 2016, (pp. 1–6). https://doi.org/10.1109/ICITEED.2016.7863307

Ritonga, A. S., & Purwaningsih, E. S. (2018). Penerapan Metode Support Vector Machine ( SVM ) Dalam Klasifikasi Kualitas Pengelasan Smaw ( Shield Metal Arc Welding ). Ilmiah Edutic, 5(1), 17–25.

Zhang, S. (2021). Challenges in KNN Classification. IEEE Transactions on Knowledge and Data Engineering, 34(10). https://doi.org/10.1109/TKDE.2021.3049250

Zhang, S., Li, X., Zong, M., Zhu, X., & Wang, R. (2018). Efficient kNN classification with different numbers of nearest neighbors. IEEE Transactions on Neural Networks and Learning Systems, 29(5), 1774–1785. https://doi.org/10.1109/TNNLS.2017.2673241




DOI: https://doi.org/10.26760/elkomika.v12i3.823

Refbacks

  • Saat ini tidak ada refbacks.


_______________________________________________________________________________________________________________________

ISSN (cetak) : 2338-8323 | ISSN (elektronik) : 2459-9638

diterbitkan oleh :

Teknik Elektro Institut Teknologi Nasional Bandung

Alamat : Gedung 20 Jl. PHH. Mustofa 23 Bandung 40124

Kontak : Tel. 7272215 (ext. 206) Fax. 7202892

Surat Elektronik : jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________

Statistik Pengunjung

Free counters!

Web Analytics Made Easy - StatCounter

Lihat Statistik Jurnal

Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License