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All Journal Jurnal TIMES Riau Journal of Computer Science Building of Informatics, Technology and Science Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Pengabdian Masyarakat IPTEK Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) JURNAL PERSEGI BULAT
Cut Try Utari, Cut Try
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Analisis Komparasi Algoritma ARIMA dan LSTM pada Prediksi Harga Cabai Merah Keriting Harian Utari, Cut Try; Sembiring, M Thariq Arya Putra; Siregar, M Habibi Rizq Zhafar
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8784

Abstract

Curly red chili is a strategic national commodity characterized by extreme price fluctuations, which significantly impact regional inflation and farmer welfare. Although conventional statistical methods are frequently used for forecasting, these approaches have inherent limitations in capturing non-linear volatility and dynamic price patterns. This research aims to address this gap by comprehensively comparing the performance of the AutoRegressive Integrated Moving Average (ARIMA) statistical model and the Long Short-Term Memory (LSTM) Deep Learning model. This study utilizes a univariate prediction approach based on daily historical price data from January 2024 to October 2025. The dataset is partitioned into 80% for training and 20% for testing purposes. Model performance is rigorously evaluated using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (). The experimental results demonstrate that the LSTM model significantly outperforms ARIMA in tracking daily price trends. LSTM achieved an average MAPE of 13.76% (classified as "Good") with an value of 0.92, whereas the ARIMA model recorded a significantly higher MAPE of 41.21% and a negative value. This study concludes that Deep Learning-based algorithms are superior and more effective in handling food commodity price volatility compared to classical linear statistical methods.
Co-Authors Afifa, Rizky Maulidya Ayu Fitriani Dalimunthe, Aulia Rahman Joel Panjaitan Mughnyanti, Mayang Muhammad Riki Atsauri Nugroho Syahputra Olando, Mayka Dowanti Pakpahan, Arnold Pranata, Ardianto Purba, Bister Regina Sirait - Rian Syahputra Sabila, Puji Chairu Sembiring, M Thariq Arya Putra Simangunsong, Putra Galatia Danzen Simanjuntak, Taufik Ismail Sipayung, Apriani Siregar, M Habibi Rizq Zhafar Syahputra, Syofyan Anwar Tafonao, Lianus