IMPLEMENTASI METODE BROWNIS DOUBLE EXPONENTIAL SMOOTHING UNTUK FORECASTING HASIL PRODUKSI DAN PRODUKTIVITAS TANAMAN JAGUNG

Abstract

Corn is a key commodity in Indonesia's agricultural sector, serving as a food source, a raw material for animal feed, and an ingredient for the processed food industry. Corn production and productivity are influenced by various factors—such as climatic conditions, the availability of production inputs, seed quality, and cultivation techniques—leading to fluctuations over time. Consequently, there is a need for a method to predict corn production and productivity. This research aims to design and develop a forecasting application for corn production and productivity using Brown’s Double Exponential Smoothing method. The study utilizes historical corn production and productivity data sourced from Statistics Indonesia (BPS), implemented using the PHP programming language and a MySQL database. Brown’s Double Exponential Smoothing was selected because it is suitable for forecasting data that exhibits a linear trend and assigns greater weight to recent data. The research is expected to yield an intuitive forecasting application that provides predictive information on corn production and productivity, serving as a reference to support agricultural planning and decision-making.