Long Short-Term Memory Method Based on Normalization Data For Forecasting Analysis of Madura Ginger Selling Price

DOI

10.2991/978-94-6463-288-0_52How to use a DOI?

Keywords

Forecasting Analysis; Ginger Selling Price; Long Short-Term Memory; Max-Min Normalization

Abstract

Forecasting is a method for estimating a future value using past data. The selling price of Madura ginger needs a forecasting analysis to predict future prices because, until now, the selling price has increased significantly. This analysis aims to increase trade business competition and maintain sales objectives related to financing, revenue planning, and marketing. In this study, the forecasting analysis system uses the Long Short-Term Memory (LSTM) method. LSTM is one of the forecasting methods with the development of a neural network that can be used for modeling time series data collected according to a time sequence within a specific time. This research contributes to forecasting ginger’s selling price in Madura using LSTM with improved model performance using max-min normalization for the preprocessing process. Max-min normalization eliminates data redundancy by converting a data set to a scale from 0 (min) to 1 (max) to make the data consistent. And for this study’s forecasting analysis, use error parameters including Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Based on the simulation results of ginger spice sales price data in Madura, it was obtained that the 2019 ginger selling price prediction was 250 data with a value of RMSE 1431.71 and MAPE 9.57. This shows that the results of the LSTM modeling have shown excellent performance in predicting training and testing the selling price of ginger so that the prediction of the selling price of ginger in 2020 can increased tolerance for time-series data and the accuracy with normalization model.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Devie Rosa Anamisa
AU  - Fifin Ayu Mufarroha
AU  - Achmad Jauhari
AU  - Muhammad Yusuf
AU  - Bain Khusnul Khotimah
AU  - Ahmad Farisul Haq
PY  - 2023
DA  - 2023/11/19
TI  - Long Short-Term Memory Method Based on Normalization Data For Forecasting Analysis of Madura Ginger Selling Price
BT  - Proceedings of the 4th International Conference on Informatics, Technology and Engineering 2023 (InCITE 2023)
PB  - Atlantis Press
SP  - 628
EP  - 639
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-288-0_52
DO  - 10.2991/978-94-6463-288-0_52
ID  - Anamisa2023
ER  -