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All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Bilawa, Firdho Akbar
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Computer Science & IT Decision Sciences, Operations Research & Management

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Prediksi Harga Beras Medium Di Indonesia Dengan Membandingkan Metode Regresi Linear Dan Regresi Polinomial Bilawa, Firdho Akbar; Hikmayanti, Hanny; Rahmat, R
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 2 (2024): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i2.810

Abstract

Availability of sufficient and equitable food is one of the pillars of realizing national  food security. Rice, as an important aspect of Indonesian food, has a strategic role and its availability must always be ensured. The majority of Indonesian people’s needs are medium types of rice. The price of medium rice fluctuates, but tends to increase over time. Changes in rice prices have a significant impact on people’s lives and can threaten household food security. Predicting the price of medium rice is very important for the Indonesian government to maintain economic stability. It is hoped that the accurate prediction results can be taken into consideration by the Indonesian government in controlling and determining medium rice price policies in Indonesia. The data used is medium rice price data in Indonesia from January 2013 to February 2024, totaling 134 data. The method used to predict rice prices is the linear regression and polynomial regression methods. This research focuses on the applying and comparing the effectiveness of the two methods by considering their accuracy and error rates.  The accuracy of the prediction results is assessed by calculating the MAPE value.  The research result show that both methods have accurate prediction model performance because the MAPE value is less than 10%. The linear regression method can predict the medium rice price more accurately because it has a smaller MAPE value of 6,29%, compared to the polynomial regression method of  6,88%.
Co-Authors Hanny Hikmayanti Handayani Rahmat, R