Development of machine learning for forecasting optimization implemented in morphology plant growth

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.331745

Keywords:

forecasting optimization, plant morphology, machine learning, multilinear regression neural network

Abstract

The object of the study is the forecasting and optimizing the plant growth rather. The data distribution at each iteration in the continuous optimization process tends to produce premature convergence because the optimum points are found at the beginning of the iteration, so that the actual optimum condition cannot be achieved. For this reason, a method is needed to see the optimum points at each iteration in the continuous optimization process. A multi-linear regression approach is used to predict the variables generated at each iteration, and then optimized using a neural network method approach for each optimum point found. This research is implemented and observed on the growth morphology of chili plants with a total sample of 100 stems, for 100 days of growth. The testing process consists of 5 different experimental scenarios based on the activation function, and the iteration process is carried out at 250, 500, and 1000 epochs. Furthermore, with a percentage of 70% training data and 30% testing data, the results obtained using the ReLU activation function have an ideal value compared to the Tanh, Softplus, Elu, and Sigmoid activation functions. Compared to the time series method with an MSE value of 4.62, this value is much better than the value of 8.6 for the time series. The RMSE and MAPE values of 16.36 and 36.53 are also excellent. Comparison of the level of forecasting accuracy of the results of continuous optimization carried out with the activation function ReLU and tanh compared to the time series method, the value with the activation function ReLU and tanh has a percentage value 46.36% and 46.86% and this value is a good value compared to using the time series method, which is exactly 67.39%

Author Biographies

Ertina Sabarita Barus, Universitas Sumatera Utara; Universitas Prima Indonesia

Student Doctor of Computer Science

Department of Computer Science and Information Technology

Lecturer of Information Systems

Department of Science and Technology

Muhammad Zarlis, BINUS University

Lecturer of Information Systems Management - Professor

Department of Computer Science and Data Science

Zulkifli Nasution, North Sumatra University

PhD Lecturer of FP - Professor

Program Studi S3 Ilmu Pertanian

Sutarman Sutarman, North Sumatra University

Lecturer of Mathematics - Doctoral

Department MIPA (Mathematics and Natural Sciences)

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Development of machine learning for forecasting optimization implemented in morphology plant growth

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Published

2025-06-30

How to Cite

Barus, E. S., Zarlis, M., Nasution, Z., & Sutarman, S. (2025). Development of machine learning for forecasting optimization implemented in morphology plant growth. Eastern-European Journal of Enterprise Technologies, 3(2 (135), 42–53. https://doi.org/10.15587/1729-4061.2025.331745

Issue

Vol. 3 No. 2 (135) (2025): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2025 Ertina Sabarita Barus, Muhammad Zarlis, Zulkifli Nasution, Sutarman Sutarman

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