APLIKASI JARINGAN SYARAF TIRUAN UNTUK MENGENALI TULISAN TANGAN HURUF A, B, C, DAN D PADA JAWABAN SOAL PILIHAN GANDA (Studi Eksplorasi Pengembangan Pengolahan Lembar Jawaban Ujian Soal Pilihan Ganda di Universitas Terbuka)

Dwi Astuti Aprijani, Unggul Utan Sufandi

Abstract


Artificial Neural Network (ANN) can be applied to recognice pattern, particularly at the stage of data classification. This study used a multilayer perceptron backpropagation ANN, an unsupervised learning algorithm, to recognize the pattern of uppercase handwriting on the answer sheet of multiple-choice exams. The application of this network involves mapping a set of input against a reference set of outputs. In this research, ANN was trained using 8000 handwritten uppercase characters (A, B, C, and D) consisting of 6000 training data characters (1500 characters for each letter) and 2000 testing data characters (500 characters for each letter). The result showed that for the most optimal performance, the architecture and network parameters were 10 neurons in hidden layer, learning rate of 0.1 and 3000 iteration times. The accuracies of the result using the optimal network architecture and parameters were 90.28% for training data and 87.35% for testing data.


Keywords


artificial neural network, backpropagation, handwriting recognition, learning rate

Full Text:

PDF

References


Basu, J.K., Bhattacharyya, D. & Kim, T. (2010). Use of artificial neural network in pattern recognition. International Journal of Software and Its Applications, 4 (2), April 2010.

Han, J. & Kamber, M. (2001). Data mining: Concept, model, methods, and algorithm. New Jersey: Wiley-Interscience.

Kantardzic, M. (2003). Data mining: Concept and techniques. San Fransisco: Morgan Kaufmann Publisher.

Kusumadewi, S. (2004). Membangun jaringan syaraf tiruan (menggunakan MATLAB & Excel Link). Edisi Pertama. Yogyakarta: Penerbit Graha Ilmu.

Suhardi, I., Imardjoko, Y.U. & Samiadji, H. (2003). Evaluasi jaringan syaraf tiruan untuk pengenalan karakter tulisan tangan jenis cetak. Seminar Nasional Teknik Elektro, G-1.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2015 Jurnal Matematika, Sains, Dan Teknologi

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.