The main contribution of this paper is to construct a business intelligence system to improve sugarcane yield for maintaining sugarcane industry production. This intelligence system was required by the sugarcane industry for monitoring the sugar content of the sugarcane yield at the field, decided on how to improve it and further to improving the production sustainability of the sugar industry. This business intelligence design is divided into some parts with the intelligent analysis technique is the main and the core part. This construction applied Relief Technique to identify key performance indicators for controlling sugarcane yield and Support Vector Machine (SVM) for monitored and predicted sugar content and sugarcane yield performance by the time. The result showed that soil pH, Relative Humidity (RH) and sugarcane age are the main key performance indicators that affected the sugar content and yield performance at the field. The SVM model in this research enabled to predicting the sugar content and yield on the field using key performance indicators to decided appropriate harvesting time for milling at the industry. For further research, it required a real-time dashboard to control the sugarcane yield at the field which possible to improve the sustainability of sugar production of the sugar industry.
Volume 12 | 06-Special Issue
Pages: 109-118
DOI: 10.5373/JARDCS/V12SP6/SP20201013