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All Journal Jurnal Informatika dan Teknik Elektro Terapan Journal of Language and Literature LLT Journal: A Journal on Language and Language Teaching Kumawula: Jurnal Pengabdian Kepada Masyarakat JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) Jurnal Minfo Polgan (JMP) Malcom: Indonesian Journal of Machine Learning and Computer Science J-Icon : Jurnal Komputer dan Informatika Jurnal Inovatif Wira Wacana
Raynesta Mikaela Indri Malo, Raynesta Mikaela Indri
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Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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PREDIKSI HARGA BERAS DI SUMBA TIMUR MENGGUNAKAN ALGORITMA NEURAL NETWORK Manggal, Renol Bulu; Pekuwali, Arini Aha; Malo, Raynesta Mikaela Indri
J-Icon : Jurnal Komputer dan Informatika Vol 14 No 1 (2026): March 2026
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v14i1.27555

Abstract

Fluctuations in rice prices in East Sumba Regency are an important issue that directly affects farmers, traders, and consumers. Unstable price changes are influenced by weather conditions, supply availability, distribution, and market dynamics. Therefore, a prediction method is needed that can provide accurate estimates of rice prices as a basis for decision making. This study aims to predict the price of medium rice in East Sumba Regency using the Neural Network algorithm, specifically Long Short-Term Memory (LSTM), which is effective in modeling time series data. The data used are monthly rice price data for the period January 2021 to December 2025 obtained from Perum BULOG Waingapu Branch Office, with data processing and analysis carried out after all 2025 data became available. The research stages include data collection, data preprocessing, normalization using Min-Max Scaling, time series dataset formation, division of training and testing data, LSTM model training, and model performance evaluation. The evaluation was carried out using the Root Mean Square Error (RMSE) metric. The results show that the LSTM model is able to predict rice prices with an RMSE value of 360.91 Rp/Kg or around 3.35% of the average rice price. This value indicates that the prediction error of the model is relatively small, so the model can be said to have good prediction performance. Therefore, the developed LSTM model is considered feasible to be used as a tool for predicting rice prices and is expected to help farmers and traders in planning sales and become a consideration for the local government in maintaining rice price stability in East Sumba Regency.
Co-Authors Adindarena, Vindya Donna Beba, Ayu Asari Rambu Fajar Hariadi Gehur, Amir Gutu Jawa, Marheni Putri Kapoe, Suryani Kurniawi K. L. Koro, Januard Alberto Manggal, Renol Bulu Mbadi, Jelita Anggrainy Mbani, Yulensi Mau Mbaru, Arto Rehi Mete, Merling Agreni Inna Pekuwali , Arini Aha Pekuwali, Arini Aha Rada, Yustina Wawu, Ardi Kalendi