New Approach to The Perceptron Algorithm with Quantum Computing for Prediction Analysis of Rice Imports in Indonesia

  • Solikhun Solikhun Informatics Engineering Department, STIKOM Tunas Bangsa
  • Jeni Sugiandi Informatics Engineering Department, STIKOM Tunas Bangsa
  • Lise Pujiastuti Information System Department, STMIK Antar Bangsa
https://doi.org/10.25139/inform.v10i1.9193
Abstract views: 95 , PDF downloads: 68
Keywords: Rice Import Prediction, Quantum Computing, Quantum Perceptron Algorithm

Abstract

Rice imports are crucial to ensure a country's food availability, especially when domestic production is insufficient. Because rice is the staple food of Indonesians, a spike in rice prices could cause social unrest. Rice imports have a strategic role in maintaining food stability and reducing the risk of price instability. This research aims to utilize the Quantum Perceptron algorithm to predict rice imports more effectively. Quantum Perceptron is a new approach that combines the principles of quantum mechanics with artificial intelligence to improve prediction performance. Researchers used data on the number of rice imports from the leading countries of origin obtained from the Central Statistics Agency using 7 variables x1 to x7. The research results show that the quantum perceptron algorithm can make predictions very well, proven by a perfect accuracy of 100% with a total of 20 epochs. This result is still better than the classical perceptron, which has 100% accuracy but with a larger number of epochs, namely 50. Quantum perceptron has better performance and shorter time, which can be seen from the smaller number of epochs compared to the classical perceptron.

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Published
2025-01-30
How to Cite
Solikhun, S., Sugiandi, J., & Pujiastuti, L. (2025). New Approach to The Perceptron Algorithm with Quantum Computing for Prediction Analysis of Rice Imports in Indonesia. Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 10(1), 61-65. https://doi.org/10.25139/inform.v10i1.9193
Issue
Vol. 10 No. 1 (2025)
Section
Articles

Copyright (c) 2025 Solikhun Solikhun, Jeni Sugiandi, Lise Pujiastuti

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