Implementasi Keras Library dan Convolutional Neural Network Pada Konversi Formulir Pendaftaran Siswa

• Main Authors: Wahyu Andi Saputra -, Muhammad Zidny Naf’an, Asyhar Nurrochman
• Format: Article
• Language: Indonesian
• Published: Ikatan Ahli Indormatika Indonesia 2019-12-01
• Series: Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
• Subjects: optical character recognition; form-sheet conversion; keras library; convolutional neural network
• Online Access: http://jurnal.iaii.or.id/index.php/RESTI/article/view/1338

Form sheet is an instrument to collect someone’s information and in most cases it is used in a registration or submission process. The challenge being faced by physical form sheet (e.g. paper) is how to convert its content into digital form. As a part of study of computer vision, Optical Character Recognition (OCR) recently utilized to identify hand-written character by learning pattern characteristics of an object. In this research, OCR is implemented to facilitate the conversion of paper-based form sheet's content to be stored properly into digital storage. In order to recognize the character's pattern, this research develops training and testing method in a Convolutional Neural Network (CNN) environment. There are 262.924 images of hand-written character sample and 29 paper-based form sheets from SDN 01 Gumilir Cilacap that implemented in this research. The form sheets also contain various sample of human-based hand-written character. From the early experiment, this research results 92% of accuracy and 23% of loss. However, as the model is implemented to the real form sheets, it obtains average accuracy value of 63%. It is caused by several factors that related to character's morphological feature. From the conducted research, it is expected that conversion of hand-written form sheets become effortless.