Implementasi Metode Particle Swarm Optimization-Dempster Shafer untuk Diagnosa Indikasi Penyakit pada Budidaya Ikan Gurami - Faris Dinar Wahyu Gunawan; Edy Santoso; Lailil Muflikhah (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Knowledge of fish breeders of the type of disease that can attack on gouramy fish at the time of cultivation is very small. Prediction indication of disease on gourami fish is an important thing to the succes of cultivation. Prediction of disease obtained from the facts that exist in the cultivation process. Dempster shafer is one of the techniques of artificial intelligence used to predict based on interrelated facts. Dempster shafer method is often used because it is quite easy to implement algorithm. However, the performance of dempster shafer is very dependent on the girlfriend who has a connection with the problem. So, if there is a new fact must first consult to experts. In addition, Dempster shafer does not guarantee specific prediction results because interrelated facts are often general. One approach that can be used to overcome this problem is to apply Particle Swarm Optmization method. Particle Swarm Optimization explores the search space to find initial density values based on particle cost values. . Where the Particle Swarm Optimization method is used to generate density values, and Dempster Shafer as a conclusion of disease indication. In this study using hybrid Particle Swarm Optmization-Dempster Shafer for diagnosis of disease indication on gouramy fish culture. The results obtained from the output of the system with experts achieve 86,5% results.