Hepatitis is considered a liver disease that is difficult to diagnose at an early stage. The number of infected people exceeds two billion, with one million deaths and more than four million infected people registered per year. Therefore, there is a great need for a system to diagnose this disease. Hepatitis is a critical inflammatory liver disease with different causes, including viral infection, alcohol, and the autoimmune system. Several systems were proposed to diagnose and classify this disease, using numerical, rigid, and low level methods such as color histogram, standard deviation, and entropy. In our research, we leveraged these to linguistic, flexible, and high level by applying Fuzzy Logic theory using a Fuzzy Inference System (FIS). In this paper, a model is implemented through many stages where 3D-Discrete Wavelet is applied to remove noise from liver biopsy images. Then the Normalized Mean Color Histogram (NMCH) is extracted as a visual feature, and a FIS is built for diagnosing the class of hepatitis using 45 fuzzy IF-THEN rules. The system is evaluated by calculating precision and accuracy, and the results were both very accurate and interesting. Diagnosis accuracy reaches 96%, with the corresponding approximated time ranging between 0.10 – 0.15 seconds.
Simulasi Perangkat Lunak Alat Pengeruk Sampah Otomatis Menggunakan Logika Fuzzy Mamdani
