Haviluddin, Taufikurrahman Khosyi, Hario Jati Setyadi, Aji Prasetya Wibawa, Felix Andika Dwiyanto, Andri Pranolo, Fachrul Kurniawan, Muslimin
The price of chili is one of the food commodities that can affect the inflation rate. Its uncertain price and even increasing at certain times will negatively impact society and state the price offer for chili is still highly dependent on the amount of chili produced. At the same time, the amount of chili productivity depends on the harvested area and land productivity. When the supply in the market is lacking, the price will increase far from its average price. Otherwise, when the supply is excessive, the price will fall far below the regular price therefore, it requires a method to estimate this chili's price to support decision making related to price issues. Many forecasting methods have been used to predict data, such as Backpropagation Neural Networks (BPNN) and Single Moving Average (SMA), proven in some cases to provide good forecasting results these two methods will be compared with the lowest error rate and the best method in predicting chili's price the results of this research will help various parties as a consideration in making decisions and planning. © 2021 IEEE.
Universitas Mulawarman, Faculty of Engineering, Samarinda, Indonesia; Universitas Negeri Malang, Jurusan Teknik Elektro, Malang, Indonesia; Universitas Negeri Malang, School, Malang, Indonesia; Hohai University, Nanjing, China; University of Islamic State Maulana Malik Ibrahim, Magister of Informatic Engineering, Malang, Indonesia
