Prototipe Deteksi Ketersediaan Slot Parkir Berbasis Pengolahan Citra
Abstract
Tempat parkir adalah sarana pokok di setiap penyedia layanan sarana dan prasarana seperti pusat perbelanjaan, pelabuhan, bandara, dan lain-lain. Tempat parkir yang luas bahkan dengan pola bertingkat menjadikan pengemudi kesulitan menemukan slot parkir kosong apabila slot parkir yang tersisa tinggal sedikit. Pada percobaan kali ini, penulis merancang sistem pendeteksi alamat parkir berbasis pengolahan citra atau image processing. Metode yang diterapkan untuk image processing adalah metode deep learning dengan algoritma YOLO atau disebut juga You Only Look Once. Algoritma YOLO mampu mendeteksi serta mengenali objek dengan background yang berbeda. Sistem kerja dari penelitian ini yaitu apabila alamat parkir terdeteksi oleh kamera menandakan slot parkir kosong, namun apabila alamat parkir tidak terdeteksi oleh kamera menandakan slot parkir tersebut sudah ada mobil yang menempati slot parkir tersebut. Dengan adanya penelitian ini diharapkan dapat membantu pengemudi mobil lebih cepat dalam menemukan slot parkir kosong. Berdasarkan hasil pengujian, penulis mendapatkan sistem yang dapat mendeteksi dan mengklasifikasi alamat parkir pada jarak yang berbeda dengan tingkat akurasi hingga 96%.
Downloads
References
Susanto, E. Rudiawan, R. Analia, P. D. Sutopo, and H. Soebakti, “The deep learning development for real-time ball and goal detection of barelang-FC,” Proceedings IES-ETA 2017 - International Electronics Symposium on Engineering Technology and Applications, vol. 2017-December, pp. 146–151, Dec. 2017, doi: 10.1109/ELECSYM.2017.8240393.
A. S. Putra, “Analisa Dan Perancangan Sistem Tata Kelola Parkir Cerdas Di Kota Pintar Jakarta,” ikraith-informatika, vol. 4, no. 3, pp. 13–21, Nov. 2020, Accessed: Dec. 28, 2022. [Online]. Available: https://journals.upi-yai.ac.id/index.php/ikraith-informatika/article/view/829
A. di Pencahayaan Penerangan Jalan Umum Jalan Tol Kabupaten Pangandaran Dan Peluang Hemat Energi Rudini, E. Priatna, I. Usrah, P. Studi Teknik Elektro, F. Teknik, and U. Siliwangi, “Analisis Pencahayaan Penerangan Jalan Umum Di Jalan Tol Kabupaten Pangandaran Dan Peluang Hemat Energi,” Journal of Energy and Electrical Engineering (JEEE), vol. 3, no. 1, p. 2021, Oct. 2021, doi: 10.37058/JEEE.V3I1.2693.
F. Muwardi and A. Fadlil, “Sistem Pengenalan Bunga Berbasis Pengolahan Citra dan Pengklasifikasi Jarak,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 3, no. 2, pp. 124–131, Jan. 2018, doi: 10.26555/JITEKI.V3I2.7470.
X. Zhang, Y. Hu, L. Zhang, Y. Kong, X. Gao, and H. Wei, “Review of Deep Neural Network Based on Auto-encoder,” DEStech Transactions on Computer Science and Engineering, no. iciti, Apr. 2019, doi: 10.12783/DTCSE/ICITI2018/29087.
C. H. Kuo, Y. H. Chou, and P. C. Chang, “Using Deep Convolutional Neural Networks for Image Retrieval,” IS and T International Symposium on Electronic Imaging Science and Technology, 2016, doi: 10.2352/ISSN.2470-1173.2016.2.VIPC-231.
J. Masci, U. Meier, D. Cireşan, and J. Schmidhuber, “Stacked convolutional auto-encoders for hierarchical feature extraction,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6791 LNCS, no. PART 1, pp. 52–59, 2011, doi: 10.1007/978-3-642-21735-7_7/COVER.
Y. Yang and H. Deng, “Gc-yolov3: You only look once with global context block,” Electronics (Switzerland), vol. 9, no. 8, pp. 1–14, Aug. 2020, doi: 10.3390/ELECTRONICS9081235.
L. Chen et al., “Convolutional neural networks (CNNs)-based multi-category damage detection and recognition of high-speed rail (HSR) reinforced concrete (RC) bridges using test images,” Eng Struct, vol. 276, p. 115306, Feb. 2023, doi: 10.1016/J.ENGSTRUCT.2022.115306.
F. Manavi, A. Sharma, R. Sharma, T. Tsunoda, S. Shatabda, and I. Dehzangi, “CNN-Pred: Prediction of single-stranded and double-stranded DNA-binding protein using convolutional neural networks,” Gene, vol. 853, p. 147045, Feb. 2023, doi: 10.1016/J.GENE.2022.147045.
O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. E. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon, vol. 4, no. 11, p. e00938, Nov. 2018, doi: 10.1016/J.HELIYON.2018.E00938.
Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects,” IEEE Trans Neural Netw Learn Syst, pp. 1–21, Jun. 2021, doi: 10.1109/TNNLS.2021.3084827.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-December, pp. 779–788, Dec. 2016, doi: 10.1109/CVPR.2016.91.
C. Y. Cao, J. C. Zheng, Y. Q. Huang, J. Liu, and C. F. Yang, “Investigation of a promoted you only look once algorithm and its application in traffic flow monitoring,” Applied Sciences (Switzerland), vol. 9, no. 17, Sep. 2019, doi: 10.3390/APP9173619.
“Implementasi Sistem Deteksi Slot Parkir Mobil Menggunakan Metode Morfologi dan Background Subtraction | Lembaga Ilmu Pengetahuan Indonesia.” http://lipi.go.id/publikasi/implementasi-sistem-deteksi-slot-parkir-mobil-menggunakan-metode-morfologi-dan-background-subtraction/10799 (accessed Dec. 28, 2022).
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Open Access Policy
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
Its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself.
