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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Perfecting a Video Game with Game Metrics Journal of Development Research Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate : Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Indonesia Sosial Teknologi
Moch Arief Soeleman
Dian Nuswantoro University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

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The evaluation of convolutional neural network and genetic algorithm performance based on the number of hyperparameters for English handwritten recognition Muhammad Munsarif; Edi Noersasongko; Pulung Nurtantio Andono; Moch Arief Soeleman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1250-1259

Abstract

Convolutional neural network (CNN) has been widely applied to image recognition, especially handwritten English recognition. CNN's performance is good if the hyperparameter values are correct. However, the determination of precise hyperparameters is not a trivial task. This task is made more difficult when combined with a larger number of hyperparameters resulting in a high dimensionality of the search space. Usually, hyperparameter optimization uses a finite number. Previous studies have shown that a large number of hyperparameters can result in optimal CNN performance. However, the studies only apply to text mining datasets. This study offers two novelties. First, it applied 20 hyperparameters and their ranges to handwritten English. Second, this paper conducted seven experiments based on different hyperparameters and the number of hyperparameters. This paper also compares the existing methods, namely random and grid search. The experiment resulted in the proposed model being superior to the existing methods. EX3 is better than other experiments and a larger number of hyperparameters and layer-specific hyperparameter values are unimportant.
Co-Authors Abdul Syukur Abdul Syukur Adhe Irham Thoriq Affandy Affandy Ahmad Zainul Fanani Ajib Susanto Ali Muqoddas Alzami, Farrikh Anas Syaifudin Ari Yahya A Arry Maulana Syarif Barata, Mula Agung Dwi Puji Prabowo, Dwi Puji Edi Noersasongko Edi Noersasongko Edi Sugiarto Elkaf Rahmawan P Elkaf Rahmawan Pramudya Esa Wahyu Andriansyah Farikh Al Zami Fikri Budiman Fikri Budiman Heribertus Himawan Imran, Bahtiar Karis W. Khafiidz Hastuti Kristhina Evandari Lu'luul Maknun Magus Sarasnomo Mohammad Ahmad Maidanul Abrori Mohammad Faishol Zuhri Much Chafid Muh Nasirudin Karim Muhammad Haris Zuhri Muhammad Munsarif Muljono Muljono Muljono Muljono Mursalim Mursalim Muslih Muslih Muslih Muslih Nanang Lestio Wibowo Noor Ageng Setiyanto Nurhindarto, Aris Pujiono Pujiono Pulung Nurtantio Andono Pulung Nurtantio Andono Purwanto Purwanto R. Anggi Pramunendar Ricardus Anggi Pramunendar S. Kholidah Rahayu Maharani Saiful Ulya Tutik Khotimah Widyatmoko Karis