Sistem Deteksi Kerumunan Fasilitas Pelayanan Publik dengan Crowd Counting

Prihandoko P; Muhammad Habib Yuhandri; Abdul Hanif  Pratama

doi:10.35134/komtekinfo.v10i4.410

Authors

Prihandoko P Universitas Gunadarma
Muhammad Habib Yuhandri Universitas Putra Indonesia YPTK Padang
Abdul Hanif Pratama Universitas Gunadarma

DOI:

https://doi.org/10.35134/komtekinfo.v10i4.410

Keywords:

Public Service, Detection System, Crowd Counting, YOLO, Streamlit

Abstract

Improvement of system management in public services needs special attention in an era of increasing population growth. Crowd Counting is proposed to ensure that the detection system for crowd objects in public facilities can run optimally. This study aims to develop Crowd Counting in a crowd object detection system in public facilities. This development is carried out to improve the performance of the You Only Look Once (YOLO) algorithm based on the Streamlit Framework. The performance of the YOLO algorithm can provide maximum results by combining the streamlit framework based on the image of the captured object at the train station. The test results of the development of Crowd Counting presented provide output with an mAP value of 90%, Recall 95%, and Precision 93.6%. Blackbox testing has also shown that the performance of Crowd Counting has provided quite significant detection accuracy. This research can contribute to the renewal of the detection system and be used as a form of solution in handling crowd problems in public facilities

References

Chandana, R. K., & Ramachandra, A. C. (2022). Real time object detection system with YOLO and CNN models: A review. ArXiv Prepr. ArXiv2208, 773.

Darmadi, & Doni, H. N. (2024). Traffic Counting using YOLO Version-5 (A case study of Jakarta-Cikampek Toll Road). IOP Conference Series: Earth and Environmental Science, 1321(1), 115–124. https://doi.org/10.1088/1755-1315/1321/1/012015

Datta, S. D., Islam, M., Sobuz, M. H. R., Ahmed, S., & Kar, M. (2024). Artificial intelligence and machine learning applications in the project lifecycle of the construction industry: A comprehensive review. Heliyon.

De Silva, D., & Alahakoon, D. (2022). An artificial intelligence life cycle: From conception to production. Patterns, 3(6).

ghani Abdulghani, A. M. A., & Dalveren, G. G. M. (2022). Moving object detection in video with algorithms YOLO and faster R-CNN in different conditions. Avrupa Bilim ve Teknoloji Dergisi, 33, 40–54.

Gothane, S. (2021). A practice for object detection using YOLO algorithm. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(2), 268–272.

Hassan, M., Hussain, F., Khan, S. D., Ullah, M., Yamin, M., & Ullah, H. (2023). CROWD COUNTING USING DEEP LEARNING BASED HEAD DETECTION. IS and T International Symposium on Electronic Imaging Science and Technology, 35(9). https://doi.org/10.2352/EI.2023.35.9.IPAS-293

Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B. (2022). A Review of Yolo algorithm developments. Procedia Computer Science, 199, 1066–1073.

Khan, M. A., Menouar, H., & Hamila, R. (2022). Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives.

Kim, J.-H., Kim, N., Park, Y. W., & Won, C. S. (2022). Object detection and classification based on YOLO-V5 with improved maritime dataset. Journal of Marine Science and Engineering, 10(3), 377.

Kundu, D., & Pandey, A. K. (2020). World urbanisation: trends and patterns. Developing National Urban Policies: Ways Forward to Green and Smart Cities, 13–49.

Kusuma, T. A. A. H., Usman, K., & Saidah, S. (2021). People Counting for Public Transportations Using You Only Look Once Method. Jurnal Teknik Informatika (Jutif), 2(1), 57–66. https://doi.org/10.20884/1.jutif.2021.2.2.77

Lapuente, V., & Van de Walle, S. (2020). The effects of new public management on the quality of public services. Governance, 33(3), 461–475.

Lee, Y.-H., & Kim, Y. (2020). Comparison of CNN and YOLO for Object Detection. Journal of the Semiconductor & Display Technology, 19(1), 85–92.

Li, X., Stringer, L. C., & Dallimer, M. (2022). The impacts of urbanisation and climate change on the urban thermal environment in Africa. Climate, 10(11), 164.

Mia, J., Bijoy, H. I., Uddin, S., & Raza, D. M. (2021). Real-time herb leaves localization and classification using YOLO. 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–7.

Obulesu, D. O., Hanshika, A., Lahari, A., Arunodaya, P., & Ashwini, R. (2023). People Counting and Tracking System in Real-Time using Deep Learning Techniques. International Journal for Research in Applied Science and Engineering Technology, 11(7), 713–720. https://doi.org/10.22214/ijraset.2023.54644

Oeschger, G., Carroll, P., & Caulfield, B. (2020). Micromobility and public transport integration: The current state of knowledge. Transportation Research Part D: Transport and Environment, 89, 102628.

Pradiptya, A. (2023). Sistem Penghitung Jumlah Kerumunan Orang dengan Metode YOLO (You Only Look Once). Repositori Universitas Dinamika, 4(1), 88–100.

Ranjan, A., Pathare, N., Dhavale, S., & Kumar, S. (2022). Performance analysis of YOLO algorithms for real-time crowd counting. 2022 2nd Asian Conference on Innovation in Technology (ASIANCON), 1–8.

Ruchika, Purwar, R. K., & Verma, S. (2022). Analytical study of YOLO and its various versions in crowd counting. In Intelligent Data Communication Technologies and Internet of Things: Proceedings of ICICI 2021 (pp. 975–989). Springer.

Shu, W., Wan, J., Tan, K. C., Kwong, S., & Chan, A. B. (2022). Crowd counting in the frequency domain. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 19618–19627.

Suhane, A. K., Vani, A., Parihar, H., Raghuwanshi, U., Nimbark, A., & Saxena, L. (2023). Human Detection and Crowd Counting Using Yolo. April.

Syahputra, H., Yanto, M., Putra, M. R., Hadi, A. F., & Fenia, S. Z. (2023). Machine learning classification analysis model community satisfaction with traditional market facilities as public service. IAES International Journal of Artificial Intelligence, 12(4), 1744–1754. https://doi.org/10.11591/ijai.v12.i4.pp1744-1754

Ullah, M. B. (2020). CPU based YOLO: A real time object detection algorithm. 2020 IEEE Region 10 Symposium (TENSYMP), 552–555.

Zhou, Y. (2024). A YOLO-NL object detector for real-time detection. Expert Systems with Applications, 238, 122256.

Zhu, Y., Ni, K., Li, X., Zaman, A., Liu, X., & Bai, Y. (2024). Artificial Intelligence Aided Crowd Analytics in Rail Transit Station. Transportation Research Record, 2678(2), 481–492. https://doi.org/10.1177/03611981231175156