Penerapan Data Sintetis dalam Simulasi Kinerja Mesin Pemipil Jagung
Abstract
Corn sheller machine simulation is a process of modeling the performance of corn sheller machines in a virtual environment using software. The purpose is to predict machine performance, test and improve machine design before mass production, and minimize development cost and time. This study conducted a simulation of corn sheller machine performance comparison using real and synthetic data, by simulating the input of 1000 kg of cobbed corn mass. Synthetic data was created using linear interpolation method using data from previous testing. The evaluation results show that synthetic data can be used as input data for simulation with sufficiently accurate results. The evaluation results show a Mean Absolute Error (MAE) value of 0.20, Mean Squared Error (MSE) value of 0.12, and Root Mean Squared Error (RMSE) value of 0.34 for net and damaged corn shelling results. However, the evaluation results for fuel consumption show an MAE value of 0.09, MSE value of 0.020, and RMSE value of 0.14. The evaluation also shows that the machine RPM affects the net and damaged corn shelling results, processing time, and fuel consumption.
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MADANHIRE, Ignatio, et al. Design and simulation of maize sheller for small scale farmers. In: Proceedings of the International Conference on Industrial Engineering and Operations Management. 2019. p. 23-25.
WIN, DR HTAY HTAY; TUN, MGSANMYA. Design and Structural Analysis of Shelling Shaft for Motorized Maize Shelling Machine. 2019.
ADOLFSSON, Josef, et al. Design and simulation of component-based manufacturing machine systems. Mechatronics, 2002, 12.9-10: 1239-1258.
KARAGUL, Kenan, et al. A simulated annealing algorithm based solution method for a green vehicle routing problem with fuel consumption. Lean and Green Supply Chain Management: Optimization Models and Algorithms, 2019, 161-187.
WANG, Benfeng, et al. Deep-learning-based seismic data interpolation: A preliminary result. Geophysics, 2019, 84.1: V11-V20.
FANG, Wenqian, et al. Dealiased seismic data interpolation using a deep-learning-based prediction-error filter. Geophysics, 2021, 86.4: V317-V328.
ARDIANTO, Dhedy; SALIM, Iqbal; WARIS, Abdul. Uji Kinerja Mesin Pemipil Jagung Berekelobot Produksi BBPP Batangkaluku. Jurnal Agritechno, 2019, 9-16.
MANDELLI, Sara, et al. Interpolation and denoising of seismic data using convolutional neural networks. arXiv preprint arXiv:1901.07927, 2019.
LENI, D., et al. Evaluasi sifat mekanik baja paduan rendah bedasarkan komposisi kimia dan suhu perlakuan panas menggunakan teknik exploratory data analysis (EDA). Dinamika Teknik Mesin, 2023, 13.1: 74-83.
CHICCO, Davide; WARRENS, Matthijs J.; JURMAN, Giuseppe. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 2021, 7: e623.
HARDIN, Johanna; GARCIA, Stephan Ramon; GOLAN, David. A method for generating realistic correlation matrices. The Annals of Applied Statistics, 2013, 1733-1762.
WANG, B. F., et al. Seismic data interpolation using deep learning based residual networks. In: 80th EAGE Conference and Exhibition 2018. EAGE Publications BV, 2018. p. 1-5.
DOI: https://doi.org/10.30596/rmme.v6i1.14219
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