Detecting Corn Plant Disease with Expert System Using Bayes Theorem Method
Frans Ikorasaki, Muhammad Barkah Akbar · 2018 6th International Conference on Cyber and IT Service Management (CITSM) · 2018
Corn is one of the food commodities in Indonesia. The need for corn commodity continues to increase from year to year both as main food ingredient, animal feed and as raw material of large scale industry until small scale. Various efforts have been made to increase the national corn production, among others, with research of improved varieties, expansion of planting areas, and extension. But in the process of planting corn there are some obstacles that is the intensity of pest and disease attacks, and the lack of agricultural extension workers. In overcoming the problem of disease attack on corn plants, corn farmers as the parties directly related to corn planting need to know the information quickly and accurately related types of attacking diseases. So after the information obtained the disease it can be immediately known solution to overcome the attack of the disease. With the development of information technology, much information can be accessed quickly through internet service. Ease of access to information is one of which can be used to provide information to corn farmers about disease identification. Therefore researchers try to give one solution that can be done to help corn farmers in identifying corn plant disease. In this study the researchers applied Bayes's theorem to calculate the probability value of corn plant identification. In the sample test the data of the symptoms of the disease showed that yielded an accuracy of 90%.