Published: 2025-10-01
Analisis Sistematis Algoritma Convolutional Neural Network untuk Klasifikasi Gambar Bokeh dan Blur: Tinjauan Literatur
DOI: 10.35870/jtik.v9i4.3953
Alif Chandra Wijaya, Arditya Baskara Mahbubi, Miftah Fauzi Januarta, Tasya Syabila, Diky Zakaria
- Alif Chandra Wijaya: Universitas Pendidikan Indonesia
- Arditya Baskara Mahbubi: Universitas Pendidikan Indonesia
- Miftah Fauzi Januarta: Universitas Pendidikan Indonesia
- Tasya Syabila: Universitas Pendidikan Indonesia
- Diky Zakaria: Universitas Pendidikan Indonesia
Abstract
The classification of bokeh and blur images is a challenge in Computer Vision, often addressed using Convolutional Neural Networks (CNNs). This study conducts a Systematic Literature Review (SLR) on 23 articles from Scopus, ScienceDirect, and Google Scholar, with inclusion criteria covering the 2014–2024 publication period, CNN as the primary method, and publication in peer-reviewed journals or conferences (60.87% from scientific journals). The analysis reveals that ResNet and VGG models achieve >90% accuracy, yet still face challenges related to dataset size, computational requirements, and the lack of statistical comparisons across models. This study identifies opportunities for further development through transfer Learning, lightweight models such as MobileNet, and more comprehensive statistical analysis to enhance image classification efficiency across various applications, including digital photography, medical imaging, and security systems.
Keywords
Image Classification ; Deep Learning ; Convolutional Neural Networks ; Computer Vision ; Image Processing
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Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 9 No. 4 (2025)
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Section: Computer & Communication Science
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Published: October 1, 2025
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License: CC BY 4.0
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Copyright: © 2025 Authors
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DOI: 10.35870/jtik.v9i4.3953
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Alif Chandra Wijaya
Program Studi Mekatronika dan Kecerdasan Buatan, Kampus Purwakarta, Universitas Pendidikan Indonesia, Kota Bandung, Provinsi Jawa Barat, Indonesia.
Arditya Baskara Mahbubi
Program Studi Mekatronika dan Kecerdasan Buatan, Kampus Purwakarta, Universitas Pendidikan Indonesia, Kota Bandung, Provinsi Jawa Barat, Indonesia.
Miftah Fauzi Januarta
Program Studi Mekatronika dan Kecerdasan Buatan, Kampus Purwakarta, Universitas Pendidikan Indonesia, Kota Bandung, Provinsi Jawa Barat, Indonesia.
Tasya Syabila
Program Studi Mekatronika dan Kecerdasan Buatan, Kampus Purwakarta, Universitas Pendidikan Indonesia, Kota Bandung, Provinsi Jawa Barat, Indonesia.
Diky Zakaria
Program Studi Mekatronika dan Kecerdasan Buatan, Kampus Purwakarta, Universitas Pendidikan Indonesia, Kota Bandung, Provinsi Jawa Barat, Indonesia.
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Anaël, F., VrAIn, C., Ros, F., Dao, T., & Lucas, Y. (2024). Dataset for image classification with knowledge. Data in Brief. https://doi.org/10.1016/j.dib.2024.110893.
-
Berezsky, O., Liashchynskyi, P., Pitsun, O., & Izonin, I. (2024). Synthesis of convolutional neural network architectures for biomedical image classification. Biomedical Signal Processing and Control, 95(PB), 106325. https://doi.org/10.1016/j.bspc.2024.106325.
-
Browne, M., & Ghidary, S. S. (2003). Convolutional Neural Networks for image processing: An application in robot vision. In Lecture Notes in Computer Science (Vol. 2903, pp. 641–652). https://doi.org/10.1007/978-3-540-24581-0_55.
-
Carreira, D., Rodrigues, N., Miragaia, R., Costa, P., Ribeiro, J., Gaspar, F., & Pereira, A. (2024). A branched convolutional neural network for RGB-D image classification of ceramic pieces. Applied Soft Computing, 165, 112088. https://doi.org/10.1016/j.asoc.2024.112088.
-
Handa, N., Kaushik, Y., Sharma, N., Dixit, M., & Garg, M. (2021). Image classification using convolutional Neural Networks. In Communications in Computer and Information Science, 1393(6), 510–517. https://doi.org/10.1007/978-981-16-3660-8_48.
-
Hossain, M. I., Jahan, S., Al Asif, M. R., Samsuddoha, M., & Ahmed, K. (2023). Detecting tomato leaf diseases by image processing through deep convolutional Neural Networks. Smart Agricultural Technology, 5(June), 100301. https://doi.org/10.1016/j.atech.2023.100301.
-
Ignatov, A., Patel, J., & Timofte, R. (2020). Rendering natural camera bokeh effect with Deep Learning. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 1676–1686). https://doi.org/10.1109/CVPRW50498.2020.00217.
-
Kaya, Y., & Gürsoy, E. (2023). A novel multi-head CNN design to identify plant diseases using the fusion of RGB images. Ecological Informatics, 75. https://doi.org/10.1016/j.ecoinf.2023.101998.
-
Kusniyati, H. (2024). Komparasi performa VGG19, ResNet50, DenseNet121 dan MobileNetV2 dalam mendeteksi gambar deepfake. Jurnal Teknologi Informasi dan Komunikasi, 9(2), 397–411.
-
Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870. https://doi.org/10.1080/01431160600746456.
-
Naranjo-Torres, J., Mora, M., Hernández-García, R., Barrientos, R. J., Fredes, C., & Valenzuela, A. (2020). A review of convolutional neural network applied to fruit image processing. Applied Sciences, 10(10). https://doi.org/10.3390/app10103443.
-
Nasrullah, A. H., & Annur, H. (2023). Implementasi metode convolutional neural network untuk identifikasi citra digital daun. Jurnal Media Informatika Budidarma, 7(2), 726. https://doi.org/10.30865/mib.v7i2.5962.
-
Nurhadi, M., & Purnomo, J. (2022). Implementation of image classification using convolutional neural network (CNN) algorithm on vehicles images. ASEAN Journal of Systems Engineering, 6(1), 1–5. https://doi.org/10.22146/ajse.v6i1.72411.
-
Reyes, D., & Sánchez, J. (2024). Performance of convolutional Neural Networks for the classification of brain tumors using magnetic resonance imaging. Heliyon, 10(3), e25468. https://doi.org/10.1016/j.heliyon.2024.e25468.
-
Roncancio, R., El Gamal, A., & Gore, J. P. (2022). Turbulent flame image classification using convolutional Neural Networks. Energy and AI, 10(July), 100193. https://doi.org/10.1016/j.egyAI.2022.100193
-
Sarangi, P. K., Sharma, B., Rani, L., & Dutta, M. (2024). Satellite image classification using convolutional neural network. In Advances in Aerial Sensing and Imaging (pp. 333–354). https://doi.org/10.1002/9781394175512.ch15
-
Tatar, A., Haghighi, M., & Zeinijahromi, A. (2024). Experiments on image data augmentation techniques for geological rock type classification with convolutional Neural Networks. Journal of Rock Mechanics and Geotechnical Engineering. https://doi.org/10.1016/j.jrmge.2024.02.015.
-
Wang, R., Li, W., Qin, R., & Wu, J. Z. (2017). Blur image classification based on Deep Learning. In Proceedings of IEEE International Conference on Imaging Systems and Techniques (IST 2017), 1–6. https://doi.org/10.1109/IST.2017.8261503.
-
Wilhelmi, M., & Rusiecki, A. (2024). Simple CNN as an alternative for large pretrained models for medical image classification – MedMNIST case study. Procedia Computer Science, 239, 1298–1303. https://doi.org/10.1016/j.procs.2024.06.299.
-
Yan, J. (2024). Application of CNN in computer vision. Applied and Computational Engineering, 30, 104-110.
-
Yang, F., Ma, Z., & Xie, M. (2021). Image classification with superpixels and feature fusion method. Journal of Electronic Science and Technology, 19(1), 100096.
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