Open AccessK-Means and Fuzzy C-Means Optimization using Genetic Algorithm for Clustering Questions
Author(s) -
Siti Sendari,
Agung Bella Putra Utama,
Nastiti Susetyo Fanany Putri,
Prasetya Widiharso,
Rizki Jumadil Putra
Publication year - 2021
Publication title -
international journal of advanced science and computer applications
Language(s) - English
Resource type - Journals
eISSN - 2809-7599
pISSN - 2809-7467
DOI - 10.47679/ijasca.v1i1.2
Subject(s) - cluster analysis , computer science , similarity (geometry) , fuzzy clustering , data mining , process (computing) , fuzzy logic , genetic algorithm , artificial intelligence , machine learning , image (mathematics) , operating system
The grouping of data can be used in the development strategy of an educational game application. The process of grouping data that initially behaved differently into several groups that now behaved more uniformly. As well as grouping the data on the difficulty level of the questions on the educational game question board. This grouping of questions is needed to get the dominant values that will be the characteristics of each group of questions that exist. The clustering method is quite widely used to overcome problems related to data grouping. This clustering is a method of grouping based on the size of the proximity, the more accurate the cluster formed, the clearer the similarity of the difficulty level of the questions. Thus, educational game developers can determine the strategy for placing the existing questions more precisely. Many clustering methods can be used to group the data on this question, including K-Means and Fuzzy C-Means (FCM) which are then optimized using the Algorithm Genetics. From the results of the research conducted, optimization gives better results for clustering questions.