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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Building of Informatics, Technology and Science
Rossi, Farli
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Computer Science & IT Education

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Penerapan Convolutional Neural Network (CNN) pada Pengenalan Aksara Lampung Berbasis Optical Character Recognition (OCR) Mulyanto, Agus; Susanti, Erlina; Rossi, Farli; Wajiran, Wajiran; Borman, Rohmat Indra
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 7, No 1 (2021): Volume 7 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v7i1.44133

Abstract

Provinsi Lampung memiliki bahasa dan aksara daerah yang disebut juga dengan Had Lampung atau KaGaNga yang merupakan aksara asli lampung. Melihat bagaimana pentingnya nilai akan eksistensi sebuah budaya dan pentingnya pelestarian aksara lampung maka dibutuhkan teknologi yang membantu dalam mengenalkan aksara lampung, salah satunya dengan teknologi optical character recognition (OCR) yang digunakan untuk merubah citra kedalam teks. Untuk mengenali pola citra Aksara Lampung dan klasifikasi model maka digunakan Convolutional Neural Network (CNN). CNN memiliki lapisan convolution yang terbentuk dari beberapa gabungan lapisan konvolusi, lapisan pooling dan lapisan fully connected. Pada peneilitian yang dilakukan dataset dikembangkan dengan pengumpulan hasil tulis tangan dari sampel responden yang telah ditentukan, kemudian dilakukan scanning gambar. Selanjutnya, dilakukan proses pelabelan dan disimpan dengan format YOLO yaitu TXT. Dari asitektur CNN yang dibangun berdasarkan hasil evaluasi menunjukan loss, accuracy menghasilkan nilai training accuracy mendapatkan nilai sebesar 0.57 dan precision mendapatkan nilai sebesar 0.87. Dari hasil nilai accuracy dan precision menunjukkan bahwa model training sudah baik karena mendekati angka 1.
Classification of Character Types of Wayang Kulit Using Extreme Learning Machine Algorithm Fatmayati, Fryda; Nugraheni, Murien; Nuraini, Rini; Rossi, Farli
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3568

Abstract

Wayang Kulit, which is an original Indonesian culture, is conditioned by the meaning of life in every performance. However, Wayang Kulit is currently less popular among young people due to a lack of understanding of the art of Wayang Kulit performance. To be able to provide knowledge to the younger generation about Wayang Kulit, one of which is by introducing the characters that exist in Wayang Kulit performances. This study aims to build an image classification system for Wayang Kulit characters by applying the neural network method using Extreme Learning Machine (ELM) and morphological feature extraction. Morphological feature extraction provides information about the shape characteristics of objects present in the image which are then used for input in the classification process. The Extreme Learning Machine (ELM) method may arbitrarily establish the weight value between the input neurons and the hidden layer during the classification step, resulting in a quicker learning pattern. Based on the test results using the confusion matrix, the accuracy value is calculated to get a value of 81%.
Co-Authors Agus Mulyanto Erlina Susanti Fryda Fatmayati Heni Pujiastuti Murien Nugraheni Rini Nuraini, Rini Rohmat Indra Borman