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All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Jurnal Esensi Infokom : Jurnal Esensi Sistem Komputer dan Informasi
Andry Winata
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Computer Science & IT Mathematics Other

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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)
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 Laksono Trisnantoro Manatap Dolok Lauro, Manatap Dolok Matthew Oni Teny Handhayani Teny Handhayani@