Comparison of Shallot Price Prediction In Pati City With LSTM, GRU and Linear Regression

Authors

  • Fajar Husain Asyari SEKOLAH TINGGI TEKNIK PATI
  • Ellen Proborini
  • Melina Dwi Safitri
  • Eko Hari Rachmawanto

Abstract

Shallots are superior vegetable plant and contribute quite significantly to the development of the national economy. The price of shallots fluctuates almost every year. At certain times the price of shallots soars due to high demand while the supply in the market is insufficient. Therefore, an analysis is needed to see what phenomena significantly affect the increase in the price of shallots. The methods used in the study were LSTM, GRU and LR. The results of the analysis show that the LSTM algorithm gets a MAE value of 0.011072172783, MAPE 3.93678% and RMSE 0.03139695060, this error is the lowest compared to GRU getting MAE value is 0.01185741, MAPE 4.2282357% and RMSE 0.03122299395 and LR with MAE 0.0134737280395416, MAPE 5.45081% and RMSE is 0.0313332635305961, so LSTM is a suitable algorithm for predicting shallot data in Pati district.

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Published

2024-08-14

Issue

Vol. 9 No. 2 (2024): Journal of Applied Intelligent System

Section

Articles

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