PERBANDINGAN ALGORITMA YOLOV3 DAN YOLOV4 DALAM PENGELOMPOKAN UKURAN TELUR AYAM SECARA REAL TIME
Abstract
The common problem currently faced by MSMEs producing chicken eggs is the difficulty in calculating the number of eggs and grouping egg sizes where everything is still done manually so that errors often occur and many entrepreneurs often experience losses. To improve and strengthen productivity, management, and marketing in this business, technological innovation is needed. This study aims to detect the number of eggs and group egg sizes based on their type using the Yolov3 and Yolov4 algorithms. Based on the results of the tests carried out, it shows that the Yolov3 and Yolov4 algorithms are able to detect chicken eggs in real time with the best accuracy value obtained by the Yolov3 algorithm. The comparison was carried out using 10 epoch tests with an F1-Score value of 0.89 where the F1-Score value approaching 1 indicates that the system performance has been running well. The results of this classification can be used to create a real time egg calculation application that can help calculate the number of eggs every day by each MSME.
Downloads
References
Guntara, Y. V, & Sukemi, S. (2020). Klasifikasi Telur Ayam dengan menggunakan Metode Component Connected Analysis. Annual Research Seminar (ARS), 5(1), 978–979.
Guo, S. S., Lee, K. H., Chang, L., Tseng, C. D., Sie, S. J., Lin, G. Z., Chen, J. Y., Yeh, Y. H., Huang, Y. J., & Lee, T. F. (2022). Development of an Automated Body Temperature Detection Platform for Face Recognition in Cattle with YOLO V3-Tiny Deep Learning and Infrared Thermal Imaging. Applied Sciences (Switzerland), 12(8), 129–137.
Muhaimin, M., & Sen, T. W. (2021). Real-Time Detection of Face Masked and Face Shield Using YOLO Algorithm with Pre-Trained Model and Darknet. Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), 4(2), 97–107.
Olorunshola, O. E., Irhebhude, M. E., & Evwiekpaefe, A. E. (2023). A Comparative Study of YOLOv5 and YOLOv7 Object Detection Algorithms. Journal of Computing and Social Informatics, 2(1), 1–12.
Pamungkas, O. E., Rahmawati, P., Supriadi, D. M., Khalika, N. N., Maliyano, T., Pangestu, D. R., ... & Wicaksono, A. (2022). Classification of Rupiah to Help Blind with The Convolutional Neural Network Method. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 6(2), 259-268.
Pratama, M. F. A., Prasasti, A. L., & Paryasto, M. W. (2023). Klasifikasi Ukuran dan Kualitas Telur Ayam Menggunakan Algoritma Convolutional Neural Network. eProceedings of Engineering, 10(1).
Rizal, R. A., Purba, N. O., Siregar, L. A., Sinaga, K., & Azizah, N. (2020). Analysis of Tuberculosis (TB) on X-ray Image Using SURF Feature Extraction and the K-Nearest Neighbor (KNN) Classification Method. Jaict, 5(2), 9.
Salam, H., Jaleel, H., & Hameedi, S. (2021). You Only Look Once ( YOLOv3 ): Object Detection and Recognition for Indoor Environment. Multicultural Education, 7(6), 171–181.
Salim, H. A. A., Kristian, Y., & Setyati, E. (2020). Detection of Militia Object in Libya by Using YOLO Transfer Learning. Jurnal Teknologi Dan Manajemen Informatika, 6(1), 35–43.
Sandy, C. L. M., Husna, A., Rizal, R. A., & Muhathir, M. (2023). Real Time Detection of Chicken Egg Quantity. Jurnal Techno Nusa Mandiri, 20(2), 108–114.
Srivastava, S., Divekar, A. V., Anilkumar, C., Naik, I., Kulkarni, V., & Pattabiraman, V. (2021). Comparative analysis of deep learning image detection algorithms. Journal of Big Data, 8(1).
Virgiawan, I., Maulana, F., Putra, M. A., Kurnia, D. D., & Sinduningrum, E. (2024). Deteksi dan tracking objek secara real-time berbasis computer vision menggunakan metode YOLO V3. Humantech :Jurnal Ilmiah Multidisiplin Indonesia, 3(3).
Yang, X., Bist, R. B., Subedi, S., & Chai, L. (2023). A Computer Vision-Based Automatic System for Egg Grading and Defect Detection. Animals, 13(14).
Yusri, M. N., Ramadhani, I. P., & Aswar, A. B. (2021). Citra Digital Dan Jaringan Syaraf Tiruan. Journal of Embedded System Security and Intelligent System, 02(May), 36–43.
Yusup, R. M., Anugrah, A. F., Muslimah, D. D., Permana, S. M. W. N., & Yuliani, S. (2024). Pendeteksian Objek Menggunakan Opencv Dan Metode Yolov4-Tiny Untuk Membantu Tunanetra. Journal of Computer Science and Information Technology, 1(2), 59-68.
Zhu, C., Liang, J., & Zhou, F. (2023). Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection. Information (Switzerland), 14(10).
Abstract viewed = 35 times
PDF downloaded = 15 times
Copyright (c) 2024 Lysheeba Abbygail Sembiring, Brian Fernanda Manik, Jovi Jonathan, Steven Giovano, Reyhan Achmad Rizal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Penulis yang menerbitkan jurnal ini menyetujui ketentuan berikut:
1. Penulis memegang hak cipta dan memberikan hak jurnal mengenai publikasi pertama dengan karya yang dilisensikan secara bersamaan di bawah Creative Commons Attribution 4.0 International License. yang memungkinkan orang lain untuk berbagi karya dengan pengakuan atas karya penulis dan publikasi awal pada jurnal.
2. Penulis dapat memasukkan pengaturan kontrak tambahan yang terpisah untuk distribusi non-eksklusif dari versi jurnal yang diterbitkan (misalnya, mengirimkannya ke repositori institusional atau menerbitkannya dalam sebuah buku), dengan pengakuan atas publikasi awalnya pada Jurnal.
3. Penulis diizinkan dan didorong untuk memposting karya mereka secara online (misalnya, dalam penyimpanan institusional atau di situs web mereka) sebelum dan selama proses pengiriman, karena hal itu dapat menghasilkan pertukaran yang produktif, serta kutipan dari karya yang diterbitkan sebelumnya.
