Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer
Andry Winata
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PERBANDINGAN LSTM DAN ELM DALAM MEMPREDIKSI HARGA PANGAN KOTA TASIKMALAYA Andry Winata; Manatap Dolok Lauro; Teny Handhayani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26015

Abstract

Humans have needs that must be met, one of which is the need for food, but food prices often change. Factors that affect price changes occur because the amount of demand is high while the supply is small. Making predictions about price changes will be very helpful to get an idea of the pattern of price changes. Therefore making predictions from price patterns is useful for providing information to the public. Predictions regarding price changes can be made using many methods. Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) are several methods that can be used to predict time series data, these two methods can provide an overview of the predictions made. The results of the study show that both algorithms have good results in terms of the the evaluation value. The evaluation results showed no significant difference between the two algorithms. The evaluation value of the rice commodity showed that ELM tended to be better with MAE values of 6,721, MAPE 0.061%, MSE 115,281, RMSE 10,737 and CV 3,699%, while LSTM with MAE 31,707, MAPE 0.286%, MSE 1927.633, RMSE 43.905 and CV 3.655%. However, for other commodities, LSTM can produce a better evaluation value.
Analysis And Forecasting of Foodstuffs Prices in Bandung Using Gated Recurrent Unit Matthew Oni; Manatap Dolok Lauro; Andry Winata; Teny Handhayani
Jurnal Esensi Infokom : Jurnal Esensi Sistem Informasi dan Sistem Komputer Vol 7 No 2 (2023): Jurnal Esensi Infokom : Jurnal esensi sistem informasi dan sistem komputer
Publisher : Lembaga Riset dan Pengabdian Masyarakat Institut Bisnis Nusantara

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

Abstract

Bandung is a city in West Java province, Indonesia. Bandung becomes one of the most densely populated cities in Indonesia. Therefore, predicting and analyzing the prices of foodstuffs based on historical data is necessary to provide useful information for society and government. This paper developed models implementing a gated recurrent unit or GRU which is a specific version of recurrent neural networks (RNN) for forecasting the price of rice, chicken meat, chicken egg, shallot, and garlic in a Bandung traditional market. The GRU models are trained using a dataset from the Information Center for National Strategic Food Price. The data are recorded from January 2018 – February 2023. The experimental results show that GRU was successfully implemented for forecasting the price of rice, chicken meat, chicken egg, shallot, and garlic. The best models produce Mean Absolute Error (MAE) as 4.3, 133.1, 118.3, 341.8, and 338.1 for rice, chicken meat, chicken egg, shallot, and garlic, respectively.
Co-Authors Laksono Trisnantoro Manatap Dolok Lauro, Manatap Dolok Matthew Oni Teny Handhayani