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Sitepu, Rasional
Universitas Katolik Widya Mandala Surabaya
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Journal : Insyst : Journal of Intelligent System and Computation

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Comparison of Premium Rice Price Prediction in East Java with ARIMA and LSTM (Case Study: National Food Agency Data) Purwanto, Devi Dwi; Sitepu, Rasional; Honggara, Eric Sugiharto
Intelligent System and Computation Vol 6 No 2 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i2.407

Abstract

Rice price prediction plays a crucial role in maintaining economic stability and food security, especially in East Java, one of Indonesia's major rice production centers. This study aims to forecast premium rice prices in East Java using the ARIMA (AutoRegressive Integrated Moving Average) method. The data utilized in this research comprises premium rice prices obtained from the National Food Agency over the period from March 15, 2021, to October 17, 2024. The analysis process begins with data exploration to identify trends and seasonal patterns in the rice price data. Subsequently, the data is analyzed using ARIMA and LSTM methods, both recognized for their effectiveness in time-series forecasting. The ARIMA(1,1,1) model was selected due to its capability to capture price dynamics through its autoregressive, integrated, and moving average components, making it well-suited for linear data with minimal seasonal variation. LSTM was employed as a comparative model because it is a subset of Machine Learning that integrates computational models and neural network algorithms, offering potential improvements in prediction accuracy. The LSTM model used for prediction consists of four layers, each with 50 neurons, dropout rates of 20% and 30%, and a single output layer representing the predicted price. The results indicate the ARIMA model provides highly accurate price estimates with a Mean Absolute Percentage Error (MAPE) of 0.485%, whereas the LSTM model achieves a MAPE of 1.95%. These findings serve as a reference for policymakers and food industry stakeholders in formulating strategic measures to stabilize rice prices in East Java.
Co-Authors Adriana Anteng Anggorowati AFL Tobing Albert Gunadhi Albert Gunadhi Alim, Natavijoy Andi Sahputra Depari Andrew F. Miyata Andrew Joewono Andrew Joewono Andrew Joewono Andrew Joewono Andrew Joewono Andrew Joewono, Andrew Andyardja, Widya Angka, Peter Rhatodirdjo Antonia, Diana L. Antonius F.L. Tobing Asep Sopandi Bilal, Zein Bryan Hulio Santoso Depari, Andi Sahputra Devi Dwi Purwanto Diana L. Antonia Dimas Fredy Arisandy Eric Sugiharto Honggara Ery Susiany R Fadia, Andi Faza Firmansyah, Erik Graha Prasidya Graha Prasidya Gunadhi, Albert Gunadhi, Albert Hadi Santosa Hendi Santoso Hijriah Hijriah Hijriah, Hijriah Ignatius Indra Indah Kuswardani L Suratno Lanny Agustine Lanny Agustine Lanny Agustine, Lanny Leli, Oktavia I Lestariningsih, Diana Martinus Edy S Miyata, Andrew F. N. Agus Sunarjanto Nekhasius Agus Sunarjanto Nugroho, Irvandianto Peter Angka Peter R Angka Peter R. Angka Peter R. Angka, Peter R. Peter Rathodirdjo Angka Peter Rathodirjo Angka Peter Rhatodirdjo Angka Pranjoto, Hartono Prasetyo, Sandif Prasidya, Graha Putri, Annajwa Aulia Putri, Farika Tono Rahmawati, Dyna Rassa, Delfiana M Sebastianus Adi Santoso Mola Suratno Suratno Tarsisius Dwi Wibawa B Tarsisius Dwi Wibawa Budianta Theophilus Ezra Nugroho Pandin Vincendy Constantinus Vincentius Christian Bintang P. Wanimbo, Mina Merry Weliamto, Widya A. Weliamto, Widya Andyardja Widya A. Weliamto Widya Andyardja Widya Andyardja Yayer, Kristina Natalia Tunga Yulianto Triwahyuadi Polly Yuliati - Yuliati Yuliati Yuliati Yuliati Zein Bilal