Pengenalan Simbol Jarimatika Menggunakan Orientasi Histogram dan Multi-layer Perceptron

Andi Sunyoto, Agus Harjoko

Abstract


Makalah ini membahas tentang pengenalan simbol-simbol Jarimatika menggunakan Jaringan Syaraf Tiruan (JST). Hasil penelitian ini dapat digunakan untuk pengembangan aplikasi perhitungan Jarimatika dan interaksi antara manusia dan komputer yang lebih natural. Segmentasi yang digunakan adalah orientasi histogram, algoritma JST yang digunakan adalah back propagation multi-layer perceptron. Layer-layer JST tersebut adalah satu layer input, satu hidden layer dan satu output layer. Penelitian ini betujuan untuk implementasi pengenalan pola simbol Jarimatika menggunakan JST multi-layer perceptron, implementasi harus mampu menghasilkan klasifikasi dengan benar, sistem harus mampu melakukan klasifikasi dari gambar statis, sehingga dapat menganalisa pengenalan gestur tangan dari simbol-simbol Jarimatika.Penelitian ini menggunakan 18 simbol dasar Jarimatika. Total citra yang digunakan adalah 360 yang terbagi atas 270 citra untuk training dan 90 citra untuk testing. Hasil penelitian ini menunjukkan bahwa JST multi-perceptron dapat digunakan untuk pengenalan simbol Jarimatika dengan akurasi 93.33%. Jumlah neuron yang optimal pada hidden layer adalah 725. Implementasi penelitian ini menggunakan Matlab versi 7 (R2010a).

This paper focuses on the recognition of Jarimatika symbols using Artificial Neural Network (ANN). The results of this research can be used to develop applications for the Jarimatika and to make interaction between humans and computers more natural. The Segmentation used is orientation histograms, the ANN algorithm used is back propagation multi-layer perceptron. Th layers of the ANN are one input layer with 19 data, one hidden layer and one output layer. This research aims to implement Jarimatika symbols with pattern recognition and multi-layer perceptron algoritm, the implementation must be able to produce the correct classification, the system must be able to perform the classification of static images, so can analyze the recognition of hand gestures from Jarimatika symbols. This research uses 18 basic Jarimatika symbols. Total image used were 360, consisting of 270 images for training and 90 images for testing. The results of this study indicate that the multi-layer perceptron ANN can be used for recognition of Jarimatika symbols with accuracy 93.33%. The optimal number of neurons in the hidden layer is 725. Implementation of this research using Matlab version 7 (R2010a).


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References


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DOI: https://doi.org/10.24076/citec.2014v1i4.32

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