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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Proceeding SENDI_U JADECS (Journal of Art, Design, Art Education and Culture Studies) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Adimas : Jurnal Pengabdian Kepada Masyarakat JE (Journal of Empowerment) Abdimasku : Jurnal Pengabdian Masyarakat Media Jurnal Informatika Jurnal Algoritma Journal on Pustaka Cendekia Informatika
Sendi Novianto
Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Turtle Dove Classification Using CNN Algorithm With MobileNetV2 Transfer Learning Muhammad Minanul Lathif; Novianto, Sendi
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9173

Abstract

This study aims to optimize the performance of a Convolutional Neural Network (CNN) model based on the MobileNetV2 architecture in classifying Java sparrow images by testing four main parameters: optimizer, learning rate, number of epochs, and batch size. The dataset consists of 800 images divided evenly into four classes. The results show that using the Adam optimizer yields the best accuracy with a training accuracy of 97.50%, validation accuracy of 98.75%, and testing accuracy of 98.13%. A learning rate of 0.001 produces the same results, indicating consistent performance with this configuration. Epoch testing shows that 35 epochs yield the highest performance with a training accuracy of 98.39%, validation accuracy of 100%, and testing accuracy of 98.75%. Meanwhile, batch size testing shows that a batch size of 32 yields the highest testing accuracy of 98.85%, a batch size of 64 yields the highest training accuracy of 98.63%, and a batch size of 128 yields the highest validation accuracy of 99.58%. These findings suggest that smaller batch sizes tend to yield better performance in terms of model generalization, while larger batch sizes provide higher stability in the training process but do not always reflect actual performance on the test data. The results of this study can serve as a reference for selecting parameter configurations to improve the accuracy and generalization of image classification models using MobileNetV2. These results emphasize the importance of proper parameter settings in improving the accuracy and stability of image classification models. They can be a reference in model development in object recognition.
Co-Authors Acun Kardianawati Adiyono, Soni Agus Winarno, Agus Al zami, Farrikh Arta Moro Sundjaja, Arta Moro Asih Rohmani, Asih Budi, Setyo Hanny Haryanto Harisa, Ardiawan Bagus Herowati, Wise Indra Gamayanto Lukman, Khairil Mailangkay, Adele B L Marjuni, Aris Muhammad Minanul Lathif Panca Hutama Caniago Prasetyo, Lusi Noviani Pulung Nurtantio Andono Putra, Gerry Sentanu Ramadhan Rakhmat Sani Sasono Wibowo Sirait, Tamsir Hasudungan Sukamto, Titien Suhartini Umi Rosyidah Zami, Farrikh Al