WEB-BASED EXPERT SYSTEM TO DIAGNOSE OVARIAL CYST DISEASE USING CERTAINTY FACTOR METHOD - Ayu Sundari; Riki Andri Yusda; Tika Christy (Informatika, Universitas Jenderal Soedirman, 2022)
Ovarian cysts are one of the most common forms of reproductive disease affecting women. Cyst or tumor is a form of abnormality that can be regarded as a benign growth of smooth muscle cells in the ovaries. Cysts are usually harmless but do not rule out the possibility of a malignant cyst or even turn into cancer. The thing that makes ovarian cysts dangerous is when they burst, are very large, or block the blood supply to the ovaries. Lack of knowledge of the general public about the symptoms that cause ovarian cyst disease makes it too late to detect this disease early so it is slow in handling, there are even some cysts or tumors which when they become malignant are only detected as having ovarian cysts, as well as unhealthy lifestyles of today's society such as consuming alcohol, fast food, causing the body to produce more chemicals. To overcome this problem, the design of a web-based expert system to diagnose ovarian cyst disease using the certainty factor method is made to assist the public or users in diagnosing through the symptoms they feel. The method used to diagnose ovarian cyst disease is the Certainty Factor method. From the calculations that have been inputted by the user, the results obtained are 97% confidence that the patient is likely to be diagnosed with cystadenoma ovarii mucinosum. With this web-based expert system program, it is hoped that the general public or users can diagnose ovarian cyst disease through the symptoms felt so as to minimize the possibility of the cyst becoming malignant.