Rancang Bangun Timbangan Buah Digital Menggunakan Metode YOLO

Authors

  • Clarisna Evita Universitas Trunojoyo Madura
  • Riza Alfita Universitas Trunojoyo Madura
  • Haryanto Haryanto Universitas Trunojoyo Madura
  • Rosida Vivin Nahari Universitas Trunojoyo Madura
  • Miftachul Ulum Universitas Trunojoyo Madura
  • Mirza Pramudia Universitas Trunojoyo Madura

DOI:

https://doi.org/10.56795/fortech.v3i1.105

Keywords:

Fruit Scales, Load Cells, Yolo, Webcams, Raspberry Pi

Abstract

The rapid development of technology has created various conveniences in all aspects of human life. One of the fields of technology that is rapidly developing is the world of electronics, which demands digitization to facilitate human activities. For example in the field of trade, humans want a process that is practical and easy. Weight measurement is one of the problems that hinders the trading process, because it is still done manually and is less efficient. The purpose of making a design of automatic digital scales based on fruit images using the yolo method (you only look once) is to determine the type and weight of fruit using a Load Cell sensor simultaneously and accurately. The working principle of this tool is the first, the detection of fruit types is processed by a USB Webcam by taking video from the Webcam and then processing it using the Yolo method to identify the type of fruit. The second stage is input from the Load Cell sensor sending object data read to the Raspberry Pi as the main controller. to determine the weight of the fruit, then the last stage is displayed on the LCD and the results can be printed out as proof of purchase receipt. From the LCD directly displays output in the form of price, weight and type of object being weighed. The Yolo method can detect objects and colors and has high detection speed and accuracy.

 

Author Biographies

Clarisna Evita, Universitas Trunojoyo Madura

 

 

Riza Alfita, Universitas Trunojoyo Madura

 

 

 

Haryanto Haryanto, Universitas Trunojoyo Madura

 

 

 

Rosida Vivin Nahari, Universitas Trunojoyo Madura

 

 

 

Miftachul Ulum, Universitas Trunojoyo Madura

 

 

 

Mirza Pramudia, Universitas Trunojoyo Madura

 

 

 

References

J. S. Wakur, Alat Penyiram Tanaman Otomatis Mengunakan Arduino Uno. 2015.

R. B. Afrianto, “Timbangan Digital Otomatis Berbasis Mikrokontroler Arduino Uno,” Jur. Tek. Elektro, Fak. Tek. Univ. Muhammadiyah Surakarta, p. 19, 2020, [Online]. Available: http://eprints.ums.ac.id/81965/%0Ahttp://eprints.ums.ac.id/81965/1/Naskah Publikasi.pdf.

M. A. Momin, M. J. Rahman, and T. Mieno, “Development of compact load cell using multiwall carbon nanotube/cotton composites and its application to human health and activity monitoring,” J. Nanomater., vol. 2019, 2019, doi: 10.1155/2019/7658437.

Purnamasari, D. N., Ulum, M., Alfita, R., Rokhana, R., & Hermawan, H. (2022). Design and Implementation of Urine Glucose Measurements Based on Color Density. In Proceedings of the 2nd International Conference on Electronics, Biomedical Engineering, and Health Informatics (pp. 109-121). Springer, Singapore.

Saputro, A. K., Purnamasari, D. N., Ulum, M., Alfita, R., & Ibrahim, M. (2021, October). Electrical Parameter Analysis on DLP 3D Printers Using IoT (Internet of Things). In 2021 IEEE 7th Information Technology International Seminar (ITIS) (pp. 1-5). IEEE.

W. WAHYUDI, A. RAHMAN, and M. NAWAWI, “Perbandingan Nilai Ukur Sensor Load Cell pada Alat Penyortir Buah Otomatis terhadap Timbangan Manual,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 5, no. 2, p. 207, 2018, doi: 10.26760/elkomika.v5i2.207.

A. R. Mubarrok, Haryanto, and D. Rahmawati, “Rancang Bangun Timbangan Buah Anggur Digital Otomatis,” J. Sci. Electro, vol. 12, no. 2, pp. 44–50, 2020.

A. Muflihana, D. S. Arief, and A. S. Nugraha, “Rancang Bangun Timbangan Digital dengan Keluaran Berat Berbasis Arduino Uno pada Automatic Machine Measurement Mass and Dimension,” Jom FTEKNIK, vol. 6, pp. 1–7, 2019, [Online]. Available: https://jom.unri.ac.id/index.php/JOMFTEKNIK/article/download/22753/22021.

Nahari, R. V., Hasanah, M., Rahmanita, E., Alfita, R., & Ulum, M. (2020, October). Artificial Intelligence optimization For Low-Light Image Enhancement. In 2020 6th Information Technology International Seminar (ITIS) (pp. 249-252). IEEE.

S. Jupiyandi, F. R. Saniputra, Y. Pratama, M. R. Dharmawan, and I. Cholissodin, “Pengembangan Deteksi Citra Mobil untuk Mengetahui Jumlah Tempat Parkir Menggunakan CUDA dan Modified YOLO,” J. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 4, p. 413, 2019, doi: 10.25126/jtiik.2019641275.

G. Plastiras, C. Kyrkou, and T. Theocharides, “Efficient convnet-based object detection for unmanned aerial vehicles by selective tile processing,” ACM Int. Conf. Proceeding Ser., 2018, doi: 10.1145/3243394.3243692.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 779–788, 2016, doi: 10.1109/CVPR.2016.91.

Hanif, S., Rahmawati, D., Alfita, R., Awal, A. S., & Doni, A. F. (2020, July). Automatic Clean Water Treatment System Using The Sugeno Fuzzy Method. In Journal of Physics: Conference Series (Vol. 1569, No. 3, p. 032087). IOP Publishing.

Purnamasari, D. N., Ulum, M., Alfita, R., Rokhana, R., & Hermawan, H. (2022). Design and Implementation of Urine Glucose Measurements Based on Color Density. In Proceedings of the 2nd International Conference on Electronics, Biomedical Engineering, and Health Informatics (pp. 109-121). Springer, Singapore.

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Published

2022-03-23

How to Cite

Evita, C., Alfita, R., Haryanto, H., Vivin Nahari, R., Ulum, M., & Pramudia, M. (2022). Rancang Bangun Timbangan Buah Digital Menggunakan Metode YOLO . Jurnal FORTECH, 3(1), 34–42. https://doi.org/10.56795/fortech.v3i1.105