Pengenalan Huruf Isyarat Tangan Menggunakan Ekstraksi Ciri Local Binary Pattern

  • M. Adhi Satria Universitas Telkom
  • Kurniawan Nur Ramadhani Universitas Telkom
  • Anditya Arifianto Universitas Telkom
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Abstract

Pada penelitian ini dibangun sistem pengenalan huruf isyarat tangan menggunakan metode ekstraksi ciri Local Binary Patterns (LBP). Metode LBP memiliki kehandalan dalam melakukan analisis tekstur, mengatasi penskalaan dan citra yang kabur. Untuk algoritma klasifikasi, digunakan metode k-Nearest Neighbour (KNN) dan Support Vector Machine (SVM). Parameter LBP terbaik didapatkan untuk nilai R=10 dan P=16 menggunakan SVM dengan kernel Gaussian. Performansi terbaik dalam penelitian ini didapatkan untuk nilai F1-Score 99,84%.

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References

Pietik̈ainen, T. M. (2004). TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS. Oulu.

Youdong Ding, H. P. (2011). Recognition of Hand-Gestures Using Improved Local Binary Pattern. Multimedia Technology (ICMT), 2011 International Conference on, 3171-3174.

Bolandraftar, S. B. (2013). Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background. Journal of Engineering Research and Applications, 605--610.

Jerry J. Tula, S. (2015, September 16). Pelayanan Penyandang Disabilitas Dalam Menggunakan Berbagai Sarana Aksebilitas. Diambil kembali dari Direktorat Jenderal Rehabilitasi Sosial: https://rehsos.kemsos.go.id/modules.php?name=News&file=article&sid=1890

D.K. Vishwakarma, P. K. (2016). A Framework for Recognition of Hand Gesture in Static Postures. International Conference on Computing, Communication and Automation (ICCCA2016).

Mahmood Jasim, M. H. (2015). Sign Language Interpretation using Linear Discriminant Analysis and Local Binary Patterns. Informatics, Electronics & Vision (ICIEV)}, year={2015}.

Nitesh S. Soni, P. D. (2015). Online Hand Gesture Recognition & Classification for Deaf Dumb. Inventive Computation Technologies (ICICT).

Xu-hui ZHANG, J.-j. W.-l. (2016). Improvement of Dynamic Hand Gesture Recognition Based on HMM Algorithm. Information System and Artificial Intelligence (ISAI).

Timo Ojala, M. P. (2002). Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence .

Olivier Chapelle, P. H. (1999). Support Vector Machines for Histogram-Based Image Classification. IEEE Transactions on Neural Networks.

Ratna Astuti Nugrahaeni, K. M. (2016). Comparative Analysis of Machine Learning KNN, SVM, and Random Forests Algorithm for Facial Expression Classification. International Seminar on Application for Technology of Information and Communication.

Chih-Wei Hsu, C.-C. C.-J. (2013). A Practical Guide to Support Vector Classification.

Idicula, D. M. (2014). Recognition of Hand Gestures of English Alphabets using HOG Method. International Conference on Data Science & Engineering (ICDSE).

Published
2018-05-23
How to Cite
Satria, M. A., Ramadhani, K. N., & Arifianto, A. (2018). Pengenalan Huruf Isyarat Tangan Menggunakan Ekstraksi Ciri Local Binary Pattern. Indonesia Journal on Computing (Indo-JC), 3(1), 75-84. https://doi.org/10.21108/INDOJC.2018.3.1.215
Section
Computer Science