Perancangan Automatic Sorting Conveyor Berdasarkan Kematangan Buah Sawit Menggunakan Algoritma Neural Network

Ilham Wahyudi; Habib Satria

doi:10.30606/rjti.v4i2.3413

Authors

Ilham Wahyudi Universitas Medan Area
Habib Satria Universitas Medan Area

DOI:

https://doi.org/10.30606/rjti.v4i2.3413

Keywords:

Automatic Sorting Conveyor, Arduino, Palm Fruit Ripeness Level, Neural Network Algorithm

Abstract

The goods sorting system is one of the important processes in the logistics system, especially when applied to work in the palm oil processing industry. The problem that occurs in the ripeness of palm fruit is that palm oil farmers find it difficult to distinguish the level of ripeness of palm fruit which will later affect the selling price of palm oil. Therefore, an automatic sorting conveyor control system is designed using an Arduino microcontroller to detect the level of ripeness of palm fruit by applying the neural network algorithm method in identifying the type of palm fruit based on the level of ripeness of the palm fruit. Then the integration of several sensors connected to the Arduino will later make it easier for the servo motor to determine the ripeness of the palm fruit. The results obtained using the neural network algorithm in processing the level of accuracy in identifying raw or ripe palm fruit can operate with the program settings that have been designed. In the Matlab simulation with the neural network method, an image pattern is obtained where if the result is close to 1 then the palm fruit is ripe, otherwise if there is nothing close to the value 1 then the palm fruit is still raw. The recorded data can later be a reference for palm oil farmers in making sales more accurately.

Downloads

References

J. Susilo, A. Febriani, U. Rahmalisa, and Y. Irawan, “Car parking distance controller using ultrasonic sensors based on arduino uno,” J. Robot. Control, vol. 2, no. 5, 2021, doi: 10.18196/jrc.25106.

S. F. Pane, F. S. Lase, and O. bere Mali, “Smart Conveyor Pada Outbond Dengan Arduino,” Smart Conveyor Pada Outbound Dengan Arduino, 2020.

S. Y. Wang, J. F. Wan, C. H. Zhang, and W. J. Zhang, “Structural design and intelligent control of flexible conveying system for smart production lines,” Huanan Ligong Daxue Xuebao/Journal South China Univ. Technol. (Natural Sci., vol. 44, no. 12, 2016, doi: 10.3969/j.issn.1000-565X.2016.12.005.

P. Tanuska, L. Spendla, M. Kebisek, R. Duris, and M. Stremy, “Smart anomaly detection and prediction for assembly process maintenance in compliance with industry 4.0,” Sensors, vol. 21, no. 7, 2021, doi: 10.3390/s21072376.

N. Azizah, E. Moralista, and Zaenal, “Remaining Service Life Discharge Conveyor G pada Tambang Batubara PT GHI di Kabupaten Tanah Laut, Provinsi Kalimantan Selatan,” Bandung Conf. Ser. Min. Eng., vol. 3, no. 1, 2023, doi: 10.29313/bcsme.v3i1.5490.

R. Nur, N. P. Parahdiba, I. Abdullah, D. F. Roji, S. Sahriana, and I. Mansur, “Rancang Bangun Mesin Ektrusi Pembuat Filamen dengan Sistem Screw Conveyor,” J. Tek. Mesin Sinergi, vol. 20, no. 1, 2022, doi: 10.31963/sinergi.v20i1.3433.

R. L. Damanik, N. Nurhakim, R. Riswan, and A. Azwar, “EVALUASI SISTEM BELT CONVEYOR DAN ANALISIS SISTEM CRUSHING PADA COAL CRUSHING PLANT,” J. GEOSAPTA, vol. 2, no. 1, 2017, doi: 10.20527/jg.v2i1.4208.

N. Guedri and R. Gharbi, “Bulk material flow measurement based only on a smart camera fixed above a moving belt conveyor,” Multimed. Tools Appl., vol. 82, no. 9, 2023, doi: 10.1007/s11042-022-13893-x.

T. Wang, J. Cheng, Y. Yang, C. Esposito, H. Snoussi, and F. Tao, “Adaptive Optimization Method in Digital Twin Conveyor Systems via Range-Inspection Control,” IEEE Trans. Autom. Sci. Eng., vol. 19, no. 2, 2022, doi: 10.1109/TASE.2020.3043393.

D. M. Harfina and Z. Zaini, “Sistem Pengendali Motor Induksi pada Belt Conveyor dengan Teknik Vektor Kontrol pada VFD menggunakan MATLAB/Simulink,” JTEV (Jurnal Tek. Elektro dan Vokasional), vol. 6, no. 1, 2020, doi: 10.24036/jtev.v6i1.107267.

R. Maitriboriruks, P. Piya-Aromrat, and Y. Limpiyakorn, “Smart Conveyor Belt Sushi Bill Payment with a Mobile Shot,” in ACM International Conference Proceeding Series, 2022. doi: 10.1145/3548636.3548637.

S. Sadi, “SISTEM PENGENDALI KONVEYOR BELT PADA PT. XYZ TANGERANG,” J. Tek., vol. 1, no. 2, 2019, doi: 10.31000/jt.v1i2.1458.

P. Pujono, A. Setiawan, and D. Prabowo, “RANCANG BANGUN MEKANISME PERGERAKAN CONVEYOR PADA MESIN SORTIR SAMPAH KALENG DAN BOTOL PLASTIK,” Bangun Rekaprima, vol. 6, no. 2, 2020, doi: 10.32497/bangunrekaprima.v6i2.2121.

C. H. Kim, “Neural Network Predictive Control for Superconducting Hybrid MagLev Conveyor Systems in Smart Factory,” J. Korean Soc. Railw., vol. 25, no. 2, 2022, doi: 10.7782/JKSR.2021.25.2.107.

A. Mousavi, M. Sarhadi, S. Fawcett, S. Bowles, and M. York, “Tracking and traceability solution using a novel material handling system,” Innov. Food Sci. Emerg. Technol., vol. 6, no. 1, 2005, doi: 10.1016/j.ifset.2004.10.006.

M. Kamatchi, C. Mendoza, and K. Venusamy, “Design and Implementation of PLC based Smart Coffee Maker,” IOP Conf. Ser. Earth Environ. Sci., vol. 1055, no. 1, 2022, doi: 10.1088/1755-1315/1055/1/012011.

E. Hernandez-Molina, B. Ojeda-Magana, J. G. Robledo-Hernandez, and R. Ruelas, “Vision system prototype for inspection and monitoring with a smart camera,” IEEE Lat. Am. Trans., vol. 18, no. 9, 2020, doi: 10.1109/TLA.2020.9381804.

R. Bloss, “Pallet conveyor now smarter and slimmer,” Assem. Autom., vol. 25, no. 2, 2005, doi: 10.1108/01445150510590442.

J. Gu et al., “Recent advances in convolutional neural networks,” Pattern Recognit., vol. 77, 2018, doi: 10.1016/j.patcog.2017.10.013.

S. Cuomo, V. S. Di Cola, F. Giampaolo, G. Rozza, M. Raissi, and F. Piccialli, “Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next,” J. Sci. Comput., vol. 92, no. 3, 2022, doi: 10.1007/s10915-022-01939-z.

J. Zhou et al., “Graph neural networks: A review of methods and applications,” AI Open, vol. 1. 2020. doi: 10.1016/j.aiopen.2021.01.001.

S. Kiranyaz, T. Ince, A. Iosifidis, and M. Gabbouj, “Operational neural networks,” Neural Comput. Appl., vol. 32, no. 11, 2020, doi: 10.1007/s00521-020-04780-3.

Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A Comprehensive Survey on Graph Neural Networks,” IEEE Trans. Neural Networks Learn. Syst., vol. 32, no. 1, 2021, doi: 10.1109/TNNLS.2020.2978386.

W. Peng, T. Varanka, A. Mostafa, H. Shi, and G. Zhao, “Hyperbolic Deep Neural Networks: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12. 2022. doi: 10.1109/TPAMI.2021.3136921.