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Winata, Andry
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Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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An Analysis And Forecasting The Foodstuffs Prices In Surabaya Traditional Market Using LSTM Ericko, Teddy; Lauro, Manatap Dolok; Winata, Andry; Handhayani, Teny
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i2.27855

Abstract

Food is one of the essential things in society. Foodstuffs prices are important factors in the stability economy. In Indonesian society, some foodstuffs, e.g., rice, beef, chicken egg, cooking oil, and sugar are the main ingredients in their cuisine. Analyzing and predicting the foodstuffs price is interesting job. This research is conducted to develop models for forecasting the price of rice, beef, chicken egg, cooking oil, and sugar. It implements the Long Short-Term Memory (LSTM) model and a daily time-series dataset from a traditional market in Surabaya. Surabaya is the capital city of East Java province, and it is one of the densest cities in Indonesia. The experiments run univariate time-series forecasting. The experimental results show that LSTM works well to forecast the price of rice, beef, chicken egg, cooking oil, and sugar. The evaluation results obtain MAPE scores as 0.12%, 0.03%, 0.72%, 0.36%, and 0.08% for models of rice, beef, chicken egg, cooking oil, and sugar, respectively. The annual average price of beef, chicken egg, and cooking oil show an increasing trend and those foodstuffs have positive correlations with each other.
Analysis and Prediction of Foodstuffs Prices in Tasikmalaya Using ELM and LSTM Winata, Andry; Lauro, Manatap Dolok; Handhayani, Teny
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3145

Abstract

Foodstuffs price analysis and prediction is one of the important research topics. This paper applies Long Short-Term Memory (LSTM) and Extreme Learning Machines (ELM) as models for forecasting the price of rice, chicken meat, chicken egg, shallot, garlic, and red chili in the Tasikmalaya traditional market. The dataset is a daily time series obtained from April 2017 - February 2023. LSTM models perform accurately to forecast 5 foodstuffs prices and obtain MAPE scores of no more than 3%. ELM works well to predict the price of rice, chicken meat, chicken egg, shallot, and garlic with MAPE scores are less than 1%. The price of rice, chicken egg, shallot, and red chili has an increasing trend. The correlation analysis finds that the price of chicken egg, shallot, and red chili has a positive correlation with each other.
Analysis And Forecasting of Foodstuffs Prices In Bandung Using Gated Recurrent Unit Oni, Matthew; Lauro, Manatap Dolok; Winata, Andry; Handhayani@, Teny
Jurnal Esensi Infokom : Jurnal Esensi Sistem Informasi dan Sistem Komputer Vol 7 No 2 (2023)
Publisher : Institut Bisnis Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55886/infokom.v7i2.651

Abstract

Bandung adalah sebuah kota di provinsi Jawa Barat dan salah satu kota padat penduduk di Indonesia. Oleh karena itu, memprediksi dan menganalisis harga bahan pangan berdasarkan data historis bermanfaat untuk menemukan trend dan informasi yang berguna bagi pemerintah dan masyarakat. Penelitian ini mengembangkan model menggunakan gated recurrent unit or GRU yang merupakan versi spesifik dari recurrent neural network (RNN) untuk memprediksi harga daging ayam, beras, bawang merah, telur ayam, dan bawang putih di pasar tradisional Bandung. Model GRU dilatih menggunakan dataset dari Pusat Informasi Harga Pangan Strategis Nasional. Dataset dikumpulkan dari bulan Januari 2018-Februari 2023. Hasil percobaan menunjukkan bahwa GRU berhasil diimplementasikan untuk peramalan harga telur ayam, beras, bawang merah, daging ayam, dan bawang putih. Model terbaik menghasilkan Mean Absolute Error (MAE) masing-masing sebesar 338.1, 341.8, 118.3, 133.1, dan 4.3 untuk harga bawang putih, bawang merah, telur ayam, daging ayam, dan beras.
Co-Authors Ericko, Teddy Handhayani@, Teny Manatap Dolok Lauro, Manatap Dolok Oni, Matthew Teny Handhayani