Response surface methodology is able to find the setting for input variables that optimize the response. When there is more than one response, the multiresponse surface methodology is used. To optimize these responses simultaneously, especially for quality characteristics, a hybrid method of Fuzzy Goal Programming (FGP) - Genetic Algorithm (GA) can accommodate it. In this research, the tablet's quality characteristics are the level of hardness, the level of friability, and the disintegration time of the tablet. The input variables that are considered to have a significant effect on the quality characteristics of the tablet are levels of binding agents, disintegrants, and the ma-chine pressure on compression process. The use of FGP is based on the reason that this method provides flexibility especially when objective functions and constraints cannot be clearly defined, thus requiring fuzzy numbers as operators. This advantage is not owned by the basic method of common Goal Programming (GP). The use of GA is to find a global optimum solution because it implements a ran-dom system.
|IOP Conference Series: Earth and Environmental Science
|Published - 9 Apr 2019
|1st International Conference on Environmental Geography and Geography Education, ICEGE 2018 - Jember, East Java, Indonesia
Duration: 17 Nov 2018 → 18 Nov 2018