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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Surya Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Surya Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Aspirasi : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat Jurnal Komputer dan Teknologi Informasi
Muhammad Munsarif
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Medicine & Pharmacology Public Health Social Sciences Other

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Journal : International Journal of Advances in Intelligent Informatics

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Improving convolutional neural network based on hyperparameter optimization using variable length genetic algorithm for english digit handwritten recognition Muhammad Munsarif; Edi Noersasongko; Pulung Nurtantio Andono; Mochammad Arief Soeleman
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.881

Abstract

Convolutional Neural Networks (CNNs) perform well compared to other deep learning models in image recognition, especially in handwritten alphabetic numeral datasets. CNN's challenging task is to find an architecture with the right hyperparameters. Usually, this activity is done by trial and error. A genetic algorithm (GA) has been widely used for automatic hyperparameter optimization. However, the original GA with fixed chromosome length allows for suboptimal solution results because CNN has a variable number of hyperparameters depending on the depth of the model. Previous work proposed variable chromosome lengths to overcome the drawbacks of native GA. This paper proposes a variable length GA by adding global hyperparameters, namely optimizer and learning speed, to systematically and automatically tune CNN hyperparameters to improve performance. We optimize seven hyperparameters, such as the learning rate. Optimizer, kernel, filter, activation function, number of layers and pooling. The experimental results show that a population of 25 produces the best fitness value and average fitness. In addition, the comparison results show that the proposed model is superior to the basic model based on accuracy. The experimental results show that the proposed model is about 99.18% higher than the baseline model.
Co-Authors A. Awoseyi, Ayomikun Abdollahi, Jafar Adinullhaq, Juyus Muhammad Agatra, Denaya Ferrari Noval Ahmad Ilham Ahmad Miftakhul Akhmad Fathurrohman Akhmad Fathurrohman Alfi Widianto Alif Hardika Ardhani, Yevi Alviatul Arfido Ramadhani ARIF KURNIAWAN Ariyanto, Nova Asdani Kindarto Caesarizky, Ovien Yoga Cornella, Barisma Ami Edi Noersasongko Edi Noersasongko Eny Winaryati Fahrezi, Andrian Febrianto Febrianto Febrianto Febrianto, Febrianto Galih Ratna Hakim, Muhammad Ilham Ilham, Ahmad Irvan Nurmantoro Javier Rayhan Jian Janata Juyus Muhammad Adinullhaq Kholifatul Afwah Kindarto, Asdani Luqman Assaffat Luqman Assaffat Mardiana Mardiana Meike Amelia Hanifah Moch Arief Soeleman Mochammad Arief Soeleman Muhammad Sam'an Muhammad Saman Muhammad Sam’an Muza'in, Muhammad Nova Ariyanto Nurmantoro, Irvan Pulung Nurtantio Andono Purbandanu, Muhammad Wigig Ramadhani, Arfido Safuan Safuan Safuan Safuan Safuan Safuan Safuan Safuan Salsabila Rahma Sam'an, Muhammad SAMSUDI RAHARJO Samsudi Raharjo Sefrina Ainun Shabrina Mirza Siti Muzdalifah Suwahono Suwahono Tirta Ramadhani Wibowo, Bhanu Lintang Yanuarta, Rizky Yevi Alviatul Ardhani