Hyperparameter Optimization with MobileNet Architecture and VGG Architecture for Urban Traffic Density Classification Using Bali Camera Image Data

Authors

  • I Putu Arsana Suputra Universitas Pendidikan Ganesha, Indonesia
  • I Gede Aris Gunadi Universitas Pendidikan Ganesha, Indonesia
  • I Made Gede Sunarya Universitas Pendidikan Ganesha, Indonesia

DOI:

10.33395/sinkron.v9i3.14971

Keywords:

Convolutional Neural Network,Classification, Density, Deep Learning, Hyperparameter, MobileNet, Optimization, Traffic

Abstract

Traffic congestion in urban areas is a critical issue, particularly in densely populated regions such as Bali. This study addresses the challenge by implementing a Convolutional Neural Network (CNN) method to classify traffic density levels based on images captured by road surveillance cameras. The primary focus of this research is hyperparameter optimization to enhance the model's performance in classifying traffic conditions. Various combinations of hyperparameters—such as the number of neurons in the dense layer, dropout rate, learning rate, batch size, and number of epochs—were tested on two popular CNN architectures: MobileNet and VGG16. MobileNet offers lightweight computing, while VGG16 provides strong feature extraction capabilities, albeit with higher computational resource demands. Quantitative results show that after hyperparameter tuning, the MobileNet architecture achieved an accuracy of 96.94% and an F1 score of 0.969, while the VGG16 architecture achieved an accuracy of 97.22% and an F1 score of 0.972 in traffic density classification. These findings confirm that hyperparameter optimization can significantly improve classification accuracy. The scientific contribution of this research lies in the structured approach to CNN hyperparameter optimization and the demonstration that this process directly impacts the enhancement of model performance in traffic image classification tasks. This study offers valuable insights for the development of intelligent traffic management systems, especially in urban areas with limited resources.

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References

Ady Saputro, I. (2022). Forensika Citra Digital Untuk Menganalisis Kecocokan Objek Menggunakan Metode SIFT. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(4), 3170–3179. https://doi.org/10.35957/jatisi.v9i4.2758

Al Rivan, M. E., & Riyadi, A. G. (2021). Perbandingan Arsitektur LeNet dan AlexNet Pada Metode Convolutional Neural Network Untuk Pengenalan American Sign Language. Jurnal Komputer Terapan, 7(1), 53–61. https://doi.org/10.35143/jkt.v7i1.4489

Alomar, K., Aysel, H. I., & Cai, X. (2023). Data Augmentation in Classification and Segmentation: A Survey and New Strategies. Journal of Imaging, 9(2). https://doi.org/10.3390/jimaging9020046

Andriansyah, D.-, & Eka Wulansari Fridayanthie. (2023). Optimization of Support Vector Machine and XGBoost Methods Using Feature Selection to Improve Classification Performance. Journal of Informatics and Telecommunication Engineering, 6(2), 484–493. https://doi.org/10.31289/jite.v6i2.8373

ANHAR, A., & PUTRA, R. A. (2023). Perancangan dan Implementasi Self-Checkout System pada Toko Ritel menggunakan Convolutional Neural Network (CNN). ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 11(2), 466. https://doi.org/10.26760/elkomika.v11i2.466

Baldi, J. R. T., Yohannes, Y., & ... (2023). Penggunaan Convolutional Neural Network Sebagai Pengenalan Huruf Bahasa Ibrani. Jutisi: Jurnal Ilmiah …. http://ojs.stmik-banjarbaru.ac.id/index.php/jutisi/article/view/1090%0Ahttp://ojs.stmik-banjarbaru.ac.id/index.php/jutisi/article/viewFile/1090/729

Dada, E. G., Oyewola, D. O., Joseph, S. B., Emebo, O., & Oluwagbemi, O. O. (2023). Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10). Applied Computational Intelligence and Soft Computing, 2023. https://doi.org/10.1155/2023/2457898

Fajri, R. G., Santoso, I., & Adi Soetrisno, Y. A. (2020). Perancangan Program Pendeteksi Dan Pengklasifikasi Jenis Kendaraan Dengan Metode Convolutional Neural Network (Cnn) Deep Learning. Transient: Jurnal Ilmiah Teknik Elektro, 9(1), 97–106. https://doi.org/10.14710/transient.v9i1.97-106

Fan, G., Chen, F., Chen, D., & Dong, Y. (2020). Recognizing multiple types of rocks quickly and accurately based on lightweight CNNs model. IEEE Access, 8, 55269–55278. https://doi.org/10.1109/ACCESS.2020.2982017

Gadosey, P. K., Li, Y., Agyekum, E. A., Zhang, T., Liu, Z., Yamak, P. T., & Essaf, F. (2020). SD-UNET: Stripping down U-net for segmentation of biomedical images on platforms with low computational budgets. Diagnostics, 10(2), 1–18. https://doi.org/10.3390/diagnostics10020110

Hafifah, F., Rahman, S., & Asih, S. (2021). Klasifikasi Jenis Kendaraan Pada Jalan Raya Menggunakan Metode Convolutional Neural Networks (CNN). TIN: Terapan Informatika Nusantara, 2(5), 292–301. https://ejurnal.seminar-id.com/index.php/tin

Heryadi, Y., & Wahyono, T. (2021a). Dasar Dasar Deep Learning Dan Implementasinya. Gava Media.

Jasim Habeeb, N. (2020). Performance Enhancement of Face Recognition under High-Density Noise Using PCA and De-Noising Technique. Ibn AL- Haitham Journal For Pure and Applied Sciences, 33(4), 148. https://doi.org/10.30526/33.4.2527

Lee, J., & Lee, S. (2023). Construction Site Safety Management: A Computer Vision and Deep Learning Approach. Sensors, 23(2). https://doi.org/10.3390/s23020944

Liu, Y. H. (2018). Feature Extraction and Image Recognition with Convolutional Neural Networks. Journal of Physics: Conference Series, 1087(6). https://doi.org/10.1088/1742-6596/1087/6/062032

Maysanjaya, I. M. D. (2020). Klasifikasi Pneumonia pada Citra X-rays Paru-paru dengan Convolutional Neural Network. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 9(2), 190–195. https://doi.org/10.22146/jnteti.v9i2.66

Meliuwati, P., & Kurniati, E. (2022). Ekstraksi Data Digital Menggunakan Teknik Max Pooling dan Average Pooling. Jurnal Riset Matematika, 137–144. https://doi.org/10.29313/jrm.v2i2.1338

Mihaita, A.-S., Li, H., & Rizoiu, M.-A. (2020). Traffic congestion anomaly detection and prediction using deep learning. 1–25. http://arxiv.org/abs/2006.13215

Nimma, D., & Uddagiri, A. (2024). Advancements in Deep Learning Architectures for Image Recognition and Semantic Segmentation. International Journal of Advanced Computer Science and Applications, 15(8), 1172–1185. https://doi.org/10.14569/IJACSA.2024.01508114

Peryanto, A., Yudhana, A., & Umar, R. (2020). Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation. Journal of Applied Informatics and Computing, 4(1), 45–51. https://doi.org/10.30871/jaic.v4i1.2017

Pratiwi, H. A., Cahyanti, M., & Lamsani, M. (2021). Implementasi Deep Learning Flower Scanner Menggunakan Metode Convolutional Neural Network. Sebatik, 25(1), 124–130. https://doi.org/10.46984/sebatik.v25i1.1297

Putra, J. W. G. (2020). Pengenalan Konsep Pembelajaran Mesin dan Deep Learning (1.4). wiragotama. https://wiragotama.github.io/ebook_machine_learning.html

Ramadhan, M., Mulyana, D. I., & Yel, M. B. (2022). Optimasi Algoritma CNN Menggunakan Metode Transfer Learning Untuk Klasifikasi Citra X-Ray Paru-Paru Pneumonia dan Non-Pneumonia. Jurnal Teknik Informatika Kaputama (JTIK), 6(2), 670–6679. http://jurnal.kaputama.ac.id/index.php/JTIK/article/view/883

Rambe, R. (2021). Perbaikan Kualitas Citra Digital Menggunakan Metode Kervel Konvolusi. TIN: Terapan Informatika Nusantara, 1(11), 557–561.

Suryawan, I. G. T., & Darma Udayana, I. P. A. E. (2022). Optimasi Convolutional Neural Network Untuk Deteksi Covid-19 pada X-ray Thorax Berbasis Dropout. Jurnal Teknologi Informasi Dan Ilmu Komputer, 9(3), 551. https://doi.org/10.25126/jtiik.2022935143

Yenusi, Y. N., Suryasatriya Trihandaru, & Setiawan, A. (2023). Comparison of Convolutional Neural Network (CNN) Models in Face Classification of Papuan and Other Ethnicities. JST (Jurnal Sains Dan Teknologi), 12(1), 261–268. https://doi.org/10.23887/jstundiksha.v12i1.46861

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How to Cite

Suputra, I. P. A., I Gede Aris Gunadi, & Sunarya, I. M. G. . (2025). Hyperparameter Optimization with MobileNet Architecture and VGG Architecture for Urban Traffic Density Classification Using Bali Camera Image Data. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(3), 1132-1145. https://doi.org/10.33395/sinkron.v9i3.14971

Issue

Vol. 9 No. 3 (2025): Article Research July 2025

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Articles

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Copyright (c) 2025 I Putu Arsana Suputra, I Gede Aris Gunadi, I Made Gede Sunarya

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