ANALISI PERBANDINGAN METODE SELF ORGANIZING MAP DAN METODE FUZZY C-MEANS PADA PENGELOMPOKKAN PEMINTAAN JURUSAN DI SEKOLAH MENENGAH KEJURUAN

Abstract

Education is a provision to develop self-potential. The role of education is a condition for the progress of a nation. Choosing a department must be adjusted to the skills/expertise possessed, talents, and interests and supported by information to prevent students from choosing majors. The selection of majors in Vocational High Schools still uses a manual system. Information and data from prospective students are processed with a manual system, allowing majors taken by prospective students not in accordance with the desired. Current technological advancements make it easier for computer systems to group data, one method of grouping data is clustering. Experts widely develop clustering or cluster analysis methods. Clustering partitions many data sets into many groups based on their similarity. Clustering methods include K-Means, DBSCAN, Self Organizing Map, and Fuzzy C-Means. This study compared 2 methods, namely Self Organizing Map and Fuzzy C-Means. In this study, the grouping has been done using the Self Organizing Maps and Fuzzy CMeans methods to help prospective students make decisions based on calculating skills, talents, and interests. The grouping is expected to help determine the desired direction. The results of these calculations are in the form of clusters or grouping results as recommendations to prospective students. Accuracy testing on both methods showed that the SOM method had better accuracy with 100% accuracy than the FCM method which has an accuracy value of 80% and 20% is an accuracy data that is not appropriate. Based on the test results it can be explained that testing has succeeded well in classifying majors.

Keywords

• clustering
• vocational high school
• self organizing maps
• fuzzy cmeans method