Forecasting of Rubber Production in North Sumatra with Backpropagation Algorithm

Authors

  • Josua Fernando Simanjuntak STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Riki Winanjaya STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Wendi Robiansyah STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI:

https://doi.org/10.55123/jomlai.v1i3.917

Keywords:

ANN, Prediction, Rubber Production, Machine Learning

Abstract

Rubber is a commodity to produce tires, balloons, and other rubber-based products. Indonesia is the second largest rubber producer and distributor in the world. But, rubber production level tends to fluctuate. Therefore, an analysis is needed to predict rubber production in the future thus rubber plantations, especially folk-owned, can take steps to prevent if declines in production are found. One way that can be done to predict is by utilizing Artificial Neural Network with Backpropagation method, since it provides accurate results. In this research, 10 network architecture models were tested and the best architecture achieved was 10-10-11-1 with accuracy of 96%. With that architecture, predictions are done and resulted in estimated rubber production in North Sumatra for 2021-2025.

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Published

2022-10-18

How to Cite

Simanjuntak, J. F., Winanjaya, R., & Robiansyah, W. (2022). Forecasting of Rubber Production in North Sumatra with Backpropagation Algorithm. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 1(3), 207–214. https://doi.org/10.55123/jomlai.v1i3.917

Issue

Vol. 1 No. 3 (2022): September

Section

Articles

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Copyright (c) 2022 Josua Fernando Simanjuntak, Riki Winanjaya, Wendi Robiansyah

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