Sistem Pendeteksi Kendaraan Roda Dua Berbasis Internet of Things Menggunakan ESP32-Cam dan YOLOv3

Internet of Things-Based Two-Wheeled Vehicle Detection System Using ESP32-CAM and YOLOV3 for Smart Parking Access Control

Authors

  • Selvi Leoni Woof Universitas Papua
  • Abdul Zaid Patiran Universitas Papua
  • Andreas Leonardo Sumendap Universitas Papua

DOI:

https://doi.org/10.57152/malcom.v6i3.2826

Keywords:

Basis Data MySQL, Deteksi Motor, ESP32-CAM, Internet of Things, Portal Otomatis, YOLOv3

Abstract

Pengelolaan parkir secara konvensional masih memiliki keterbatasan dalam pemantauan kendaraan, pengendalian akses, dan pencatatan data secara terstruktur. Sistem dirancang untuk menangkap citra objek melalui ESP32-CAM, memproses deteksi motor menggunakan YOLOv3 pada Python, mengendalikan portal melalui motor servo, menyalakan lampu kilat pada kondisi tertentu, serta menyimpan data hasil deteksi ke basis data MySQL. Pengujian sistem dilakukan pada area parkir Lapangan Borasi, Manokwari, Papua Barat. Kontribusi penelitian ini terletak pada integrasi deteksi kendaraan roda dua berbasis YOLOv3 dengan sistem kontrol portal otomatis, pencahayaan adaptif menggunakan flash ESP32-CAM, serta penyimpanan data deteksi secara real-time ke basis data MySQL dalam satu sistem IoT yang terintegrasi. Pengujian dilakukan menggunakan 19 data uji yang terdiri atas 15 motor dan 4 objek bukan motor. Hasil pengujian menunjukkan bahwa 13 motor berhasil terdeteksi, sedangkan 2 motor tidak terdeteksi pada malam hari akibat rendahnya pencahayaan. Pada objek bukan motor, 2 mobil dan 1 orang berhasil dikenali sebagai bukan motor, sedangkan 1 sepeda salah terdeteksi sebagai motor. Berdasarkan confusion matrix, sistem memperoleh akurasi sebesar 84,21%, presisi motor sebesar 92,86%, dan recall sebesar 86,67%. Hasil ini menunjukkan bahwa sistem mampu melakukan deteksi motor dan pengendalian portal secara otomatis, meskipun masih dipengaruhi oleh kondisi pencahayaan dan kemiripan bentuk objek.

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References

G. R. Koten et al., “Penerapan Internet of Things pada smart parking system untuk kebutuhan pengembangan smart city,” J. Tek. Ind. dan Manaj. Rekayasa, vol. 1, no. 1, pp. 49–59, Jun. 2023, doi: 10.24002/JTIMR.V1I1.7204.

K. Auliya, M. Yusfi, and R. Rasyid, “Sistem Pemantauan Slot Parkir Menggunakan Sensor Ultrasonik JSN-SR04T dan Pengenalan Plat Nomor Kendaraan dengan ESP32-CAM,” J. Fis. Unand, vol. 12, no. 4, pp. 534–540, Oct. 2023, doi: 10.25077/JFU.12.4.534-540.2023.

V. A. Kusuma, H. Arof, S. S. Suprapto, B. Suharto, R. A. Sinulingga, and F. Ama, “An Internet of Things-based touchless parking system using ESP32-CAM,” Int. J. Reconfigurable Embed. Syst., vol. 12, no. 3, pp. 329–335, Nov. 2023, doi: 10.11591/IJRES.V12.I3.PP329-335.

S. Ali, A. Jalal, M. H. Alatiyyah, K. Alnowaiser, and J. Park, “Vehicle Detection and Tracking in UAV Imagery via YOLOv3 and Kalman Filter,” Comput. Mater. Contin., vol. 76, no. 1, pp. 1249–1265, 2023, doi: 10.32604/CMC.2023.038114.

D. D. Aboyomi and C. Daniel, “A Comparative Analysis of Modern Object Detection Algorithms: YOLO vs. SSD vs. Faster R-CNN,” ITEJ (Information Technol. Eng. Journals), vol. 8, no. 2, pp. 96–106, Dec. 2023, doi: 10.24235/ITEJ.V8I2.123.

N.?; Zhao et al., “CMCA-YOLO: A Study on a Real-time Object Detection Model for Parking Lot Surveillance Imagery,” Electron. 2024, Vol. 13, Page 1557, vol. 13, no. 8, p. 1557, Apr. 2024, doi: 10.3390/ELECTRONICS13081557.

K. D. Dextiro, I. M. A. D. Suarjaya, and K. S. Wibawa, “Rancang Bangun Sistem Smart Gate Pada Parkir Sepeda Motor Menggunakan Sensor Ultrasonik dan MQTT Berbasis Internet of Things,” JITTER J. Ilm. Teknol. dan Komput., vol. 5, no. 2, pp. 2167–2176, Jul. 2024, doi: 10.24843/JTRTI.2024.V05.I02.P04.

K. Impana Manik et al., “Integrasi Sensor Ultrasonik dan Computer vision (YOLO) Berbasis ESP32-CAM untuk Klasifikasi Objek pada Sistem Parkir,” J. Nas. Komputasi dan Teknol. Inf., vol. 9, no. 1, pp. 12–20, Feb. 2026, doi: 10.32672/jnkti.v9i1.10263.

G. P C P da Luz, G. Massuyoshi Sato, L. Fernando Gomez Gonzalez, and J. Freitag Borin, “Smart parking with Pixel-Wise ROI Selection for Vehicle Detection Using YOLOv8, YOLOv9, YOLOv10, and YOLOv11,” Internet Things (The Netherlands), vol. 36, Dec. 2024, doi: 10.1016/j.iot.2025.101858.

B. Alsamani, S. Chatterjee, A. Anjomshoae, and P. Ractham, “Smart Space Design–A Framework and an IoT Prototype Implementation,” Sustain. 2023, Vol. 15, Page 111, vol. 15, no. 1, p. 111, Dec. 2022, doi: 10.3390/SU15010111.

W. A. Jabbar, L. Y. Tiew, and N. Y. Ali Shah, “Internet of Things enabled parking management system using long range wide area network for smart city,” Internet Things Cyber-Physical Syst., vol. 4, pp. 82–98, Jan. 2024, doi: 10.1016/J.IOTCPS.2023.09.001.

A. Herwandi, A. A. Ramadhan, N. T. Sunggono, and F. Ferawati, “Analisis Kinerja ESP32-CAM Dalam Mendeteksi Objek,” bit-Tech, vol. 7, no. 3, pp. 1014–1021, Apr. 2025, doi: 10.32877/BT.V7I3.2296.

A. A. Murat and M. S. Kiran, “A comprehensive review on YOLO versions for object detection,” Eng. Sci. Technol. an Int. J., vol. 70, p. 102161, Oct. 2025, doi: 10.1016/J.JESTCH.2025.102161.

A. Al Mamun, A. Hasib, A. S. M. Mussa, R. Hossen, and A. Rahman, “IoT-Enabled Smart Car Parking System through Integrated Sensors and Mobile Applications,” Int. Conf. Robot. Electr. Signal Process. Tech., pp. 211–216, Dec. 2024, doi: 10.1109/ICREST63960.2025.10914374.

M. A. Ala’anzy, A. Abilakim, R. Zhanuzak, and L. Li, “Real time smart parking system based on IoT and fog computing evaluated through a practical case study,” Sci. Reports 2025 151, vol. 15, no. 1, pp. 33483-, Sep. 2025, doi: 10.1038/s41598-025-15507-6.

J. W. Simatupang, A. M. Lubis, and Vincent, “IoT-Based Smart parking Management System Using ESP32 Microcontroller,” Int. Conf. Electr. Eng. Comput. Sci. Informatics, vol. 2022-October, pp. 305–310, 2022, doi: 10.23919/EECSI56542.2022.9946608.

H. Lin et al., “A Study on Data Selection for Object Detection in Various Lighting Conditions for Autonomous Vehicles,” J. Imaging 2024, Vol. 10, Page 153, vol. 10, no. 7, p. 153, Jun. 2024, doi: 10.3390/JIMAGING10070153.

M. Chaman, A. El Maliki, H. Dahou, and A. Hadjoudja, “Benchmarking YOLO-based deep learning models for real-time object detection in hybrid ADAS and intelligent transportation systems,” Results Eng., vol. 29, p. 108942, Mar. 2026, doi: 10.1016/J.RINENG.2025.108942.

J. Krej?í, M. Babiuch, J. Suder, V. Krys, and Z. Bobovský, “Latency-Sensitive Wireless Communication in Dynamically Moving Robots for Urban Mobility Applications,” Smart Cities 2025, Vol. 8, Page 105, vol. 8, no. 4, p. 105, Jun. 2025, doi: 10.3390/SMARTCITIES8040105.

D. Cornei, C. Fo?al?u, and L. Cornei, “A Study Regarding Power Consumption of An IoT Node For Image Retrieval and its Optimization,” Bull. Polytech. Inst. Ia?i. Electr. Eng. Power Eng. Electron. Sect., vol. 69, no. 1, pp. 61–84, Jul. 2024, doi: 10.2478/BIPIE-2023-0004.

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Published

2026-06-26

How to Cite

Woof, S. L., Patiran, A. Z., & Sumendap, A. L. (2026). Sistem Pendeteksi Kendaraan Roda Dua Berbasis Internet of Things Menggunakan ESP32-Cam dan YOLOv3: Internet of Things-Based Two-Wheeled Vehicle Detection System Using ESP32-CAM and YOLOV3 for Smart Parking Access Control. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 6(3), 1383-1401. https://doi.org/10.57152/malcom.v6i3.2826