Implementation of Convolutional Neural Network Algorithm for Detecting Empty Parking Area Based on Raspberry Pi

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Published Jun 27, 2024
https://doi.org/10.37339/e-komtek.v8i1.1220
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Vol. 8 No. 1 (2024)

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Suhartono
Universitas Negeri Makassar
Satria Gunawan Zain
Universitas Negeri Makassar
Ayu Futri Sadaruddin
Universitas Negeri Makassar

Abstract

This study aims to implement a convolution neural network algorithm to detect empty parking areas based on Raspberry Pi 4 and use the Convolutional Neural Network (CNN) method of the YOLO V5 model. This research consists of several stages, starting from the potential and problem stages, needs analysis, literacy studies, building prototypes, system design, and system testing. The datasets collected were taken using smartphone cameras and webcams with a total of 645 image datasets which were divided into two categories, namely training data and validation. System testing is carried out in two conditions, namely during the day and at night. The results of the detection test for observing variations in the position of filled and unfilled vehicles obtained the highest average accuracy during daytime conditions, while for observing cars entering and leaving the parking lot during day and night conditions, the results were the same percentage of success.

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Suhartono, Satria Gunawan Zain, & Sadaruddin, A. F. (2024). Implementation of Convolutional Neural Network Algorithm for Detecting Empty Parking Area Based on Raspberry Pi . Jurnal E-Komtek (Elektro-Komputer-Teknik), 8(1), 154-167. https://doi.org/10.37339/e-komtek.v8i1.1220

References

N. Afriliana, Rosalina, and R. Valeria, “Pendeteksian Ruang Kosong Parkir di dalam Ruangan,” Ultim. Comput. J. Sist. Komput., vol. 10, no. 1, pp. 34–40, 2018.
[2] A. Firmansyah and D. A. Pratama, “Perancangan Smart Parking System Berbasis Arduino Uno,” J. Teknol. Pelita Bangsa, vol. 10, no. 1, pp. 1–9, 2019.
[3] S. Jupiyandi, F. R. Saniputra, Y. Pratama, M. R. Dharmawan, and I. Cholissodin, “Pengembangan Deteksi Citra Mobil Untuk Mengetahui Jumlah Tempat Parkir Menggunakan Cuda Dan Modified Yolo,” J. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 4, pp. 413–419, 2019.
[4] Fathahillah, S. G. Zain, W. Setialaksana, and M. Asriadi, “Development of Internet of Things (IoT) Based Electric Equipment Control,” Int. J. Environ. Eng. Educ., vol. 4, no. 2, pp. 60–65, 2022.
[5] M. W. Lestari, N. D. Siahaan, and R. Sianipar, “Rancang Bangun Sistem Ketersediaan Tempat Parkir Mobil Menggunakan Sensor Infrared Berbasis Arduino,” J. Borneo Inform. Tek. Komput., vol. 1, no. 1, pp. 8–14, 2021.
[6] E. Tanuwijaya and C. Fatichah, “Penandaan Otomatis Tempat Parkir Menggunakan YOLO untuk Mendeteksi Ketersediaan Tempat Parkir Mobil pada Video CCTV,” J. Ris. dan Konseptual, vol. 5, no. 1, pp. 189–198, 2020.
[7] M. Yulianti, C. Suhery, and I. Ruslianto, “Pendeteksi Tempat Parkir Mobil Kosong Menggunakan Metode CANNY,” J. Coding, Sist. Komput. Untan, vol. 5, no. 3, pp. 48–56, 2017.
[8] E. Triansyah and Y. I. N, “Implementasi Metode Pattern Recognition Untuk Pengenalan Ucapan Huruf Hijaiyyah,” J. Ilm. Teknol. Inf. Terap., vol. 4, no. 1, pp. 1–10, 2017.
[9] T. N. Wenny, J. M. Parenreng, and Suhartono, “Development of Lecture Attendance System Using QR Code in Information and Computer Engineering Education Study Program of Universitas Negeri Makassar,” J. ELINVO (Electronics, Informatics, Vocat. Educ., vol. 7, no. 1, pp. 19–26, 2022.
[10] Suhartono, S., Zain, S. G., & Sugiawan, S.2022. Sistem Object Recognition Plat Nomor Kendaraan Untuk Sistem Parkir Bandara.Journal of embedded system security and inteligent system.