DOI: https://doi.org/10.21456/vol9iss2pp185-191

Klasifikasi Citra Alat Musik Tradisional dengan Metode k-Nearest Neighbor, Random Forest, dan Support Vector Machine

*Herry Sujaini scopus  -  Program Studi Informatika, Universitas Tanjungpura, Indonesia
Open Access Copyright (c) 2019 JSINBIS (Jurnal Sistem Informasi Bisnis)

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Abstract
Dalam dekade terakhir, metode non-parametrik (algoritma berbasis pembelajaran mesin) semakin banyak dipergunakan dari berbagai aplikasi berbasis pengolahan citra digital. Penelitian ini bertujuan untuk membandingkan tiga metode non-parametrik yaitu Metode k-Nearest Neighbor (kNN), Random Forest (RF), dan Support Vector Machine (SVM) terhadap klasifikasi citra alat musik tradisional di Indonesia yang populer di kalangan masyarakat yaitu : angklung, djembe, gamelan, gong, gordang, kendang, kolintang, rebana, sasando, dan serunai. Dari hasil eksperimen pengklasifikasian dengan metode kNN, RF dan SVM, metode kNN memiliki akurasi yang paling baik. Rata-rata nilai precision ketiga metode tersebut berturut-turut adalah 92,1% untuk kNN, 85,4% untuk SVM, dan 69,4% untuk RF

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Keywords: Klasifikasi; alat musik; k-Nearest Neighbor; Random Forest; Support Vector Machine

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  1. Adam, E., Mutanga. O., Odindi, J., Abdel-Rahman, E.M., 2014. Land-use/cover classification in a heterogeneous coastal landscape using Rapid Eye imagery: Evaluating the performance of random forest and support vector machines classifiers. Int. J. Remote Sens. 35, 3440–3458
  2. Akbulut, Y., Sengur, A., Guo, Y., Smarandache, F., 2017. NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors classifier. Symmetry 9, 179
  3. Beaula, A.R., Marikkannu, P., Sungheetha, A., Sahana, C., 2016. Comparative study of distinctive image classification techniques. 2016 10th International Conference on Intelligent Systems and Control (ISCO)
  4. Bi, H., Sun, J., Xu, Z., 2017. Unsupervised PolSAR Image Classification Using Discriminative Clustering”, IEEE Transactions on Geoscience and Remote Sensing, 55(5)
  5. Breiman, L., 2001. Random forests. Mach. Learn. 45, 5–32
  6. Demsar, J., Curk, T., Erjavec, A., Gorup, C., Hocevar, T., Milutinovic, M., Mozina, M., Polajnar. M., Toplak. M., Staric, A., Stajdohar, M., Umek, L., Zagar, L., Zbontar, J., Zitnik, M., Zupan, B., 2013. Orange: Data Mining Toolbox in Python. Journal of Machine Learning Research 14(Aug), 2349−2353
  7. Ghosh, A., Joshi, P.K., 2014. A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution World View 2 imagery. Int. J. Appl. Earth Obs. Geoinf. 26, 298–311
  8. Heydari, S.S., Mountrakis, G., 2018. Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites. Remote Sens. Environ. 204, 648–658
  9. Hu, Y., Ashenayi, K., Veltri, R., O'Dowd, G., Miller, G., Hurst, R. Bonner, R., 1994. A Comparison of Neural Network and Fuzzy c-Means Methods in Bladder Cancer Cell Classification, IEEE World Congress on Computational Intelligence
  10. Iandola, F.N., Moskewicz, M.W., Ashraf, K.., Han, S., Dally, W.J., Keutzer, K., 2017. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. CoRR, abs/1602.07360
  11. Kalra, K., Goswami, A.K., Gupta, R., 2013. A Comparative Studyof Supervised Image Classification Algorithms for Satellite Images. International Journal of Electrical, Electronics and Data Communication 1(10)
  12. Khatami, R., Mountrakis, G., Stehman, S.V., 2016. A meta-analysis of remote sensing research on supervised pixel-based land cover image classification processes: General guidelines for practitioners and future research. Remote Sens. Environ. 177, 89–100
  13. Kulkarni, A.D., Lowe, BC., 2016. International Journal on Recent and Innovation Trends in Computing and Communication 4(3), 58-63
  14. Kurian, J., Karunakaran, V., 2012. A Survey on Image Classification Methods, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) 1(4)
  15. Niknejad, M., Zadeh, V.M., Heydari, M., 2014. Comparing different classifications of satellite imagery in forest mapping, International Research Journal of Applied and Basic Sciences
  16. Pouteaua, R., Collinb, A., Stolla, B.A., 2011. Comparison of Machine LearningAlgorithms for Classification of Tropical Ecosystems Observed by Multiple Sensors at Multiple Scales, International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada
  17. Sonawane, M.S., Dhawale, C.A., 2016. Evaluation and Analysis of few Parametric and Nonparametric Classification Methods. 2016 Second International Conference on Computational Intelligence & Communication Technology
  18. Wei, C.,Huang,J., Mansaray, L.R., Li, Z., Liu,W., Han, J., 2017. Estimation and mapping of winter oil see drape LAI from high spatial resolution satellite data based on a hybrid method. Remote Sens. 9, 488
  19. Yang, Y., Chen, K., 2010. Unsupervised Learning via Iteratively Constructed Clustering Ensemble, The 2010 International Joint Conference on Neural Networks (IJCNN)

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