The design of website-based sugarcane forecasting information system

Sugarcane (Saccharum officianarum) is widely used as raw material for sugar and MSG. Data on sugarcane production has not been used optimally, except for administrative purposes. The production data can be used to predict the yield of sugar cane of which can be utilized by cooperatives and farmers. This research was conducted to design an information system that can be used to forecast sugarcane yields in the working area of KUD Subur Malang, Indonesia. The information system design process is carried out by implementing Machine Learning. The results of sugarcane yield forecasting using machine learning implementation in KUD Subur showed the best results using the gradient boosting algorithm with 68% model accuracy. Web-based yield forecasting information system can be used as a production forecasting tool for KUD Subur to improve its business processes. Sugarcane forecasting information system can be well received by users.