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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Buana Informatika Jurnal Kajian Ilmiah Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Kalbiscientia Jurnal Sains dan Teknologi ABDIMAS Jurnal Pengabdian Kepada Masyarakat KALBISIANA Jurnal Sains, Bisnis dan Teknologi Journal of Applied Research In Computer Science and Information Systems
Yulia Ery Kurniawati
Institut Teknologi Dan Bisnis Kalbis
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences

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Development of An Application Transforming Handwriting into Digital Form using CNN Josulin, Claudio; Kurniawati, Yulia Ery
Journal of Applied Research In Computer Science and Information Systems Vol. 1 No. 2 (2023): Desember 2023
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v1i2.87

Abstract

This study aims to develop an application to recognize and predict handwriting using a Convolutional Neural Network (CNN) with ResNet50 architecture. The software development life cycle (SDLC) is an incremental model with two increments. The first increment is used to build the model, and the second increment is used to build the user interface. The data used in this study is handwritten images of Latin uppercase, Latin lowercase, and Arabian numerals with 62 classes. The training data used English Handwritten Characters by Dhruvil Dave from Kaggle Dataset. Data was trained and validated using k-fold cross-validation with tenfold and ten epochs for each fold. The model has an accuracy, precision, recall, and f1-score of 66.33%, 73.4%, 66.2%, and 66%, respectively. The functional application can work as expected based on the black box testing. The developed application can predict handwriting with up to 50% accuracy.
Co-Authors Andrew Christian Thuwijaya Aris Aryanto Bima Dirgantara , Harya Bima Dirgantara Bima Dirgantara Dedy Syamsuar Denny Prabowo , Yulius Dhony Octaviano Dio Wijaya Putra Dirgantar, Harya Bima Dirgantara , Harya Bima Edwin Lesmana Tjiong Eno Maria Yohana Andriani Eric Loudwyck Erwin Halim Ester Lumba Harya Bima Dirgantar Harya Bima Dirgantara Harya Bima Dirgantara Harya Bima Dirgantara Harya Bima Dirgantara Hendri Septanto Henri Septanto, Henri Henry Dwi Septian Ignasius Widira Kristianto Ivan Surya Josulin, Claudio Kirana Maharani, Kirana Maharani Lesmana Marselino , Tedi Mailangkay, Adele BL Marselino , Tedi Lesmana Muhammad Adrinta Abdurrazzaq Muhammad Alif Iman Aulia Muhammad Hadits Alkhafidl Paramaresthi Windriyani Putra Lintangasto Ricardo Hubert Lingga Robit Hussalam Septanto, Hendri Tedi Lesmana Marselino Tedi Lesmana Marselino Wilson Yovi Friangga Yulius Denny Prabowo Yulius Denny Prabowo Yulius Denny Prabowo Yulius Denny Prabowo Yulius Denny Prabowo, Yulius Denny