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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Unes Law Review YUME : Journal of Management Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Engineering, Mathematics and Computer Science Journal (EMACS) Algor COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Riset dan Aplikasi: Akuntansi dan Manajemen Jurnal Pengabdian West Science
Andrew Andrew, Andrew
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences

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Journal : Engineering, Mathematics and Computer Science Journal (EMACS)

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Comparative Study of CNN-based Deep Learning Models for Animal, Digit, and Flower Image Classification Suri, Puti Andam; Setiono, Michael Alvin; Andrew, Andrew; Fajar, Muhammad
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.14317

Abstract

This study explores how four convolutional neural network (CNN) models MobileNetV2, DenseNet121, EfficientNetB0, and InceptionV3 perform in classifying images from three different datasets: animals, handwritten digits (MNIST), and flowers. The main goal is to understand which model offers the best balance between accuracy and efficiency when applied to datasets with varying complexity. Each model was trained and tested using identical preprocessing steps, and its performance was evaluated based on accuracy, precision, recall, and F1-score through a confusion matrix. Training and testing times were also measured to assess computational efficiency. The results show that DenseNet121 consistently achieved the highest accuracy: 98% on animal images and 88% on flower images while MobileNetV2 provided a close performance (97% and 82%) but with much faster processing times, between 11 and 55 minutes. EfficientNetB0, on the other hand, performed poorly on the more complex flower dataset, achieving only 5% accuracy. These findings suggest that DenseNet121 is ideal for projects where accuracy is the main concern, whereas MobileNetV2 is more suitable for real-time applications that require quick responses without a major drop in accuracy. Overall, this research highlights the importance of aligning model selection with both dataset characteristics and computational limitations in practical image classification tasks.
Co-Authors Abdurrohman, Abdurrohman Akbar, Junaidi Aminnatus Shalihah, Aliffa Anggelisa, Anggelisa Anggy, Anggy Aprikasari, Manda Arisna, Arisna Augustpaosa Nariman Augustpaosa Nariman Bernita, Bernita Chintya, Chintya Clarinda , Marcellina Aurelia Devin, Devin Dika Ardiana, Muhammad Elizabeth Hiu , Jesslyn Juana Figo, Figo Firizqi, Januponsa Dio Frentyo, Frentyo Gunawan, Daniel Elfresh Haris Haris, Haris Hariyanto, Susanto Haryono Haryono Hesniati, Hesniati Ipan, Ipan Jayanti, Annida Rida Jumiati, Nurul Kamisriati, Kamisriati Kartika, I Kadek Laulita, Nasar Buntu Madona, Putri Megawati, Vita Muhammad Afif Muhammad Fajar Mukhlis Mukhlis Muliyasari Kusno, Erlina Novia, Eka Putri Handayani, Nadya Rahaditya, R. Runtulalo, Rio Maichel Santoso, Handri Sembiring , Rachel Sepfanya Setiono, Michael Alvin Sunata , Yuliana Susanto Suri, Puti Andam Timotius, Frenly Tyana, Tyana Winson, Winson Wiryaatmaja , Christopher Louis Yuhefizar Yuhefizar Zai, Immanuel