A Quantum Perceptron: A New Approach for Predicting Rice Prices at the Indonesian Wholesale Trade Level

Solikhun; Tri Yunita

doi:10.29207/resti.v8i4.5869

Solikhun AMIK & STIKOM Tunas Bangsa
Tri Yunita STIKOM Tunas Bangsa

DOI: https://doi.org/10.29207/resti.v8i4.5869

Keywords: prediction, rice price, indonesia, perceptron, quantum computing

Abstract

The wholesale rice trade in Indonesia encounters various challenges in forecasting prices. These challenges are influenced by factors such as weather, government policies, global market conditions, and other economic variables. Accurate price predictions are crucial for informing government policy in a timely manner. This research introduces a new approach that utilizes the Quantum Perceptron algorithm to forecast rice prices. The algorithm, an innovative method in quantum computing, is expected to enhance the efficiency and effectiveness of price predictions. Although the research is still in the analytical stage, the use of Quantum Perceptron shows promise in dynamically addressing the complexity of market data and the variability of factors affecting rice prices. The method focuses on developing models that can leverage quantum computing to process information more effectively than classical methods. By harnessing the unique properties of quantum mechanics, such as superposition and entanglement, Quantum Perceptron can identify complex patterns and optimize predictions of future rice prices. The research describes the implementation of quantum algorithms in the context of the Indonesian rice wholesale market, including the technical challenges encountered and future development prospects. The research utilizes quantum computing along with the perceptron algorithm. The researchers focused on analyzing the quantum perceptron algorithm because of the limited availability of quantum computing devices. The findings of this research are confined to analysis. In order to advance this research, the author recommends that future studies employ quantum devices to achieve more accurate predictions

Downloads

Download data is not yet available.

References

F. Yoviono, Y. Sandra, and F. Arifandi, "Comparison of Starch Content in Black Rice Compared to White Rice Using the Iodide Test," Cerdika J. Ilm. Indonesia., vol. 2, no. 11, pp. 976–981, 2022, doi: 10.36418/cerdika.v2i11.468.

M. I. Syairozi, "Analysis Of Poverty In The Agricultural Sector (Case Study of Rice Commodities in Malang Regency)," Media Ekon., vol. 28, no. 2, pp. 113–128, 2021, doi: 10.25105/me.v28i2.7169.

Wahyu Ngestisari, B. Susanto, and T. Mahatma, "The Comparison of ARIMA Methods and Artificial Neural Networks for Rice Price Forecasting," Indonesia. J. Data Sci., vol. 1, no. 3, pp. 96–107, 2020, doi: 10.33096/ijodas.v1i3.18.

H. Hamdina, Z. Rahmat, and W. S, "Employee Performance Assessment Analysis to Determine Workability Quality Using Backpropagation Neural Networks," Movere J., vol. 2, no. 1, pp. 12–24, 2020, doi: 10.53654/mv.v2i1.87.

J. Nurhakiki et al., "Literature Study: Introduction to 4 Algorithms in Deep Learning and Their Implications," J. Educator. Character, no. 1, pp. 270–281, 2024, [Online]. Available: https://doi.org/10.51903/detikar.v2i1.598

I. Arifin, R. F. Haidi, and M. Dzalhaqi, "Application of Computer Vision Using Deep Learning Methods from the Perspective of the Ulul Albab Generation," J. Teknol. Integrated, vol. 7, no. 2, pp. 98–107, 2021, doi: 10.54914/jtt.v7i2.436.

anderas anandri Bagus Angkak, D. Mauritsius, and Y. M.Y. jacob, "Judicial Review of Rice Price Instability in View of Law Number 8 of 1999 concerning Consumer Protection," Artemis Law J., vol. 1, pp. 671–685, 2024.

R. Dewi and M. R. Lubis, “Analisis Metode Quantum untuk Optimalisasi Algoritma Best First Search,” J. Edukasi dan Penelit. Inform., vol. 7, no. 2, p. 300, 2021, doi: 10.26418/jp.v7i2.48557.

B. Ghulam, A. Shidiq, M. T. Furqon, and L. Muflikhah, "Rice Price Prediction using the Least Square Method," Pemb. Technol. Inf. and Computer Science., vol. 6, no. 3, pp. 1149–1154, 2022.

S. Sarbaini, D. Yanti, and Nazaruddin, "Prediction of Belida Rice Prices in Pekanbaru City Using Fuzzy Time Series Cheng," J. Teknol. and Manaj. Ind. Terap., vol. 2, no. 3, pp. 234–241, 2023, doi: 10.55826/tmit.v2i3.183.

F. I. Sanjaya and D. Heksaputra, "Prediction of Average Indonesian Wholesale Rice Prices with Long Short Term Memory," JATISI (Journal of Information Technology and Information Systems), vol. 7, no. 2, pp. 163–174, 2020, doi: 10.35957/jatisi.v7i2.388.

S. Solikhun and V. Yasin, “Analisis Quantum Perceptron Untuk Memprediksi Jumlah Pengunjung Ucok Kopi Pematangsiantar Pada Masa Pandemi Covid-19,” J. Edukasi dan Penelit. Inform., vol. 8, no. 1, p. 162, 2022, doi: 10.26418/jp.v8i1.52191.

M. Kurniawan, M. Hakimah, and S. Agustini, “Perbandingan SVM dan Perceptron dengan Optimasi Heuristik,” J. Telemat., vol. 15, no. 2, pp. 85–92, 2020.

T. A. Putra, P. A. Widya, R. Afira, and Y. Elva, “Optimization Analysis Model Determining PNMP Mandiri Loan Status Based on Pearson Correlation,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 6, pp. 966–973, 2022, doi: 10.29207/resti.v6i6.4469.

P. A. Nugroho, "Implementation of a Multi-Layer Perceptron Artificial Neural Network for Predicting Sunlight in the City of Bandung," Komputa J. Ilm. Comput. and Inform., vol. 12, no. 1, pp. 83–90, 2023, doi: 10.34010/komputa.v12i1.9419.

S. N. Kapita, S. Mahdi, and F. Tempola, "Assessment of Student Knowledge Using the Perceptron Algorithm Artificial Neural Network," Techno J. Peelit., vol. 9, no. 1, p. 372, 2020, doi: 10.33387/tjp.v9i1.1712.

Sudianto, A. D. Sripamuji, I. Ramadhanti, R. Riski Amalia, J. Saputra, and B. Prihatnowo, "Application of Support Vector Machine and Multi-Layer Perceptron Algorithms in News Topic Classification," Janapati, vol. 11, no. 2, pp. 84–91, 2022, [Online]. Available: https://doi.org/10.23887/janapati.v11i2.44151

N. Kahar and W. Aritonang, "Implementation of Artificial Neural Networks Using the Perceptron Algorithm in Determining New Student Study Programs," J. Akad., vol. 14, no. 2, pp. 74–80, 2022, doi: 10.53564/akademika.v14i2.864.

K. L. Kohsasih, M. Dipo, A. Rizky, T. Fahriyani, V. Wijaya, and R. Rosnelly, "Comparative Analysis of Convolutional Neural Network Algorithms and Multi-Layer Perceptron Neural Algorithms in Garbage Image Classification," J. Technol. Informatics and Comput. Syst., vol. 10, no. 2, pp. 22–28, 2021, [Online]. Available: http://ejournal.stmik-time.ac.id

A. F. Hardiyanti and D. Fitrianah, "Comparison of the C4.5 Algorithm and Multilayer Perceptron for Hospital Class Classification in DKI Jakarta," J. Telekomun. and Comput., vol. 11, no. 3, p. 198, 2021, doi: 10.22441/incomtech.v11i3.10632.

P. C. Siswipraptini et al., "Perceptron Algorithm Using Machine Learning Techniques to Model Smart Distribution of Electrical Loads," J. Ilm., vol. 14, no. 2, pp. 150–159, 2022, [Online]. Available: https://doi.org/10.33322/energi.v14i2.1857

M. H. Yuhandri and L. Mayola, "Identification of Selection Patterns for Determining Nagari Guardian Candidates Using the Artificial Neural Network Perceptron Algorithm," J. KomtekInfo, vol. 10, pp. 158–165, 2023, doi: 10.35134/komtekinfo.v10i4.485.

Y. A. Rindri and A. Fitriyani, "Comparative Analysis of the Performance of Multilayer Perceptron and K-Nearest Neighbor Algorithms in Migraine Type Classification," J. Teknol. and Inf., vol. 13, no. 1, pp. 44–55, 2023, doi: 10.34010/jati.v13i1.9111.

H. Haryani, C. I. Agustyaningrum, A. Surniandari, S. Sahara, and R. K. Sari, "Multilayer Perceptron Classification Algorithm in Forest Fire Data Analysis," J. Infortech, vol. 5, no. 1, pp. 64–70, 2023, doi: 10.31294/infortech.v5i1.15792.