Klasifikasi Aksara Jawa Cetak Menggunakan Jaringan Syaraf Tiruan Backpropagation

Authors

  • Resky Novaliandy Universitas Sanata Dharma
  • Anastasia Rita Widiarti Universitas Sanata Dharma

Keywords:

Javanese Script, Moment Invariant, Artificial Neural Network Backpropagation

Abstract

Script is a visual symbol that is usually printed on paper, stone, wood, cloth and others. One example of a script is the Javanese Nglegena script. Nglegena Javanese script can be found in various forms, one of which is in the form of images. Image is a digital representation of an object. By using the image of the character, it is done as an effort to fight against the character. Identification can be done digitally by using one of the classifications, namely artificial-backpropagation network. Backpropagation neural network is a good classification that can update its weight value. This research uses image data of Javanese Nglegena script with a total data of 635 images. Each image is preprocessed in the form of image resizing and thinning. Then the feature extraction used is in the form of 7 moment invariant values, the value of image length and image width so that the number of inputs for each data is 9 features. Then the 9 feature values ​​become input for classifying the backpropagation-artificial network. The best results for testing 16 types of Javanese Nglegena script get a training accuracy of 92.1% and a test accuracy of 76.1%. Where the best architecture uses a hidden 2-layer network with 9 input neurons, 55 neurons in layer 1, 65 neurons in layer 2 and 4 layers, logsig activation function and the trainlm training method.

References

Abdul, K. (2010). Momen Invariant Untuk Mendapatkan Ciri Daun Tanaman Hias. TEKNOMATIKA, vol 2, no 2, pp.1-8.

Berrar D. (2018). Cross-validation. Encyclopedia of Bioinformatics and Computational Biology, vol 1, Elsevier, pp.542–545.

Cheisaryanto, P.F. (2019). Pengenalan Aksara Jawa Nglegena menggunakan Jaringan Syaraf Tiruan. Skripsi. Progam Studi Teknik Informatika. Universitas STMIK AKAKOM.

Destyningtias B., Heranurweni S., T. Nurhayati. (2010). Segmentasi Citra Dengan Metode Pengambangan. Jurnal Elektrika. vol.2, no.1, pp 39 – 49.

Devi, H.K.A., (2006). Thinning: A Preprocessing Technique for an OCR System for the Brahmi Script. Ancient Asia, vol.1, pp.167–172.

Indyaputra, O.G. (2019). Metode Backpropagation Untuk Alih Aksara Jawa Cetak Menggunakan Ciri ICZ-ZCZ. Skripsi. Progam Studi Teknik Informatika. Universitas Sanata Dharma.

Kurniawan H.C., Soemarto K.S., Yahya B.N. (2020). Evaluasi Metode Ekstraksi Fitur Hu Moment Invariant Untuk Pengenalan Aktivitas Manusia. Jurnal Telematika, vol.15, no.2.

Nugroho, K.S. (2019). Confusion Matrix Untuk Evaluasi Model Pada Supervised Learning. Retrieved from: https://ksnugroho.medium.com/confusion-matrix-untuk-evaluasi-model-pada-unsupervised-machine-learning-bc4b1ae9ae

Sholahuddin, A. (2012). Metode Moment Invariant Dan Backpropagation Neural Network Pada Pengenalan Wajah. Lokakarya Komputasi dalam Sains dan Teknologi Nuklir, pp.283-295.

Siang, JJ. (2005). Jaringan Syaraf Tiruan dan Pemrogramannya menggunakan Matlab. Yogyakarta: Penerbit Andi.

Sunyoto, A. (2013). Analisa Metode Moment Invariant Untuk Mendeteksi Obyek Yang Telah Mengalami Transformasi. Data Manajemen dan Teknologi Informasi, vol.14, no. 1.

Wibowo, A.T. (2018). Pengenalan Pola Tulisan Tangan Aksara Jawa Dengan Algoritma Backpropagation. Skripsi. Progam Studi Teknik Informatika. Universitas Sanata Dharma.

Wikipedia. (2021, 4 16) Aksara Jawa. Retrieved from Wikipedia: https://id.wikipedia.org/wiki/Aksara_Jawa

Woods, Richard E., Gonzales, Rafael E. (2008). Digital Image Processing Third Edition. Pearson Education. New Jersey.

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Published

2022-01-22

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