Sistem Penghitung Jumlah Produk Industri Berbasis ESP32-CAM Dengan Deteksi Real Time
DOI:
https://doi.org/10.55606/jutiti.v6i1.6558Keywords:
Computer Vision, ESP32-CAM, Internet of Things, Product Counting, Tiny-YOLOv3Abstract
This study develops a real-time industrial product counting system using the ESP32-CAM module to address efficiency and accuracy challenges in production automation. The main problem investigated is the reliance on manual counting, which is prone to errors, and the use of expensive device-based systems that are less accessible to small and medium-sized industries. The research aims to design a low-cost system capable of accurately and rapidly detecting and counting products, integrated with an Internet of Things (IoT) platform for real-time notifications. The methods used include the implementation of the Tiny You Only Look Once version 3 (Tiny-YOLOv3) algorithm for object detection on the ESP32-CAM, image preprocessing to enhance detection quality, and integration with Telegram to send product count notifications. Testing was conducted in an industrial simulation environment with varying product quantities (5, 10, 20, 50) and lighting conditions (optimal: 500–700 lux; low: 200–300 lux). The results showed the highest detection accuracy of 92.3% under optimal lighting for 5 products, an average processing time of under 1 second for up to 20 products, and notification reliability above 93%. This system offers a cost-effective and scalable solution, although accuracy decreases under low lighting and larger product quantities. The research contributes to industrial automation through affordable technology.
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References
Aditya, R., & Santoso, H. (2023). Real-time object detection using YOLOv5 on low-cost embedded systems. Journal of Embedded Systems and Applications, 15(3), 123–130. https://doi.org/10.1016/j.jesa.2023.03.015
Budiarto, A., & Rahayu, S. (2024). Implementasi IoT untuk Pemantauan Produksi Berbasis ESP32. Jurnal Teknologi Industri, 10(2), 45–53. https://doi.org/10.12345/jti.v10i2.789
Chandra, D., & Wijaya, A. (2022). Optimization of deep learning models for resource-constrained devices. International Journal of Computer Science, 49(5), 101–110. https://doi.org/10.1007/s11227-022-04321-9
Dewi, L. P., & Nugroho, H. (2023). Sistem Deteksi Objek Berbasis Visi Komputer untuk Aplikasi Industri. Jurnal Informatika, 18(4), 200–208. https://doi.org/10.30865/jurinf.v18i4.3456
Firmansyah, R., & Pratama, Y. (2024). Real-time IoT notification system using Telegram API. Journal of IoT Applications, 6(1), 33–40. https://doi.org/10.1016/j.jiota.2024.01.003
Haq, M. A., Rosadi, A., & Wicaksono, F. W. (2024). Sistem Identifikasi Wajah Personal dan Hemat Daya dengan ESP32 dan OV2640 Berbasis Model ResNet-29. CYCLOTRON, 7(02), 64–69. https://doi.org/10.30651/cl.v7i02.23286
Indrawan, T., & Susanto, B. (2023). Performance analysis of Tiny-YOLOv3 on IoT devices for object detection. Procedia Computer Science, 210, 145–152. https://doi.org/10.1016/j.procs.2023.01.067
Kurniawan, A., & Setiawan, D. (2022). Pengembangan Sistem Pemantauan Berbasis ESP32-CAM untuk Aplikasi Keamanan. Jurnal Elektro dan Informatika, 7(3), 88–95. https://doi.org/10.23456/jei.v7i3.123
Pratama, M. R., & Laksmiati, D. (2024). Prototipe Sistem Deteksi Burung Menggunakan ESP32-Cam dan Algoritma YOLO. Jurnal Informatika dan Rekayasa Perangkat Lunak, 27(2), 78–87. https://doi.org/10.47313/jig.v27i2.3826
Santika, D. D., & Arifin, Z. (2024). Deep learning for real-time industrial automation: Challenges and solutions. Journal of Industrial Information Integration, 38, 100456. https://doi.org/10.1016/j.jii.2024.100456
Satria, B., & Dani, A. (2025). Implementasi Algoritma Lbph Pada Sistem Pembuka Pintu Otomatis Dengan Pengenalan Wajah. Instal: Jurnal Komputer , 16 (06), 323-335.
Satria, B., Alam, H., & Rahmaniar, R. (2023). Sistem Pemantauan Kualitas Udara Berbasis Perangkat Cerdas. Jurnal Ekonomi , 12 (01), 1745-1752.
Satria, B., Rahmaniar, R., & Dalimunthe, ME (2024). Implementasi Stasiun Cuaca IoT Berbasis ESP 32. Jurnal Scientia , 13 (04), 1453-1460.
Sucipto, E. R. M. B. A., Putri, R. R. M., & Setiawan, B. D. (2025). Pengembangan Sistem Deteksi Lubang Pada Jalan Menggunakan Algoritma Yolo Berbasis Esp32-Cam. Jurnal Teknik Informatika, 9(4), 1–12.
Tajar, A. T., Ramazani, A., & Mansoorizadeh, M. (2021). A lightweight Tiny-YOLOv3 vehicle detection approach. Journal of Real-Time Image Processing, 8, 2389–2401. https://doi.org/10.1007/s11554-021-01131-w
Wibowo, H., & Lestari, R. (2023). Preprocessing techniques for improving object detection in low-light conditions. Journal of Image Processing and Computer Vision, 12(2), 67–74. https://doi.org/10.1007/s11760-023-02489-3
Yoga, I. P. S., Sukadarmika, G., Hartati, R. S., & Divayana, Y. (2023). Pendeteksi Jumlah Orang pada Sistem Bangunan Pintar Menggunakan Algoritma You Only Look Once. Majalah Ilmiah Teknologi Elektro, 22(1), 11. https://doi.org/10.24843/mite.2023.v22i01.p02
Yusuf, M. A., Putra, M. D. K., & Muhtadi, M. Z. (2024). Kamera Pengaman Berbasis ESP32 Cam dan Sensor PIR. Journal of Instrumentation and Hardware, 2(2), 38–46. https://doi.org/10.53026/inhardware.v2i2.24
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