Penerapan Metode Klustering Fuzzy C-Means Untuk Penentuan Peminatan Pemilihan Jurusan Pada Sekolah Menengah Tingkat Atas

Abstract

Determination of majors for students in accordance with the weight of knowledge conditions or commonly referred to as determining student majors in the high school environment is determined by the understanding of learning in understanding knowledge which is supported by elements of specialization, because the scientific character is proportional to the same properties of the object pursuing the science. With this assumption, someone who studies a science will be able to deepen it if he has pleasure in what he is studying. Pleasure with something being learned can affect the quality of student learning outcomes in certain scientific fields of study. It can be said that the focus on a material being studied, students will learn more comfortably and achieve a better understanding so that the weight of the knowledge learned will be achieved [1]. The process of determining the majors is held to select and classify the same students' abilities in one education department according to the field taken. This is done so that there is an adjustment to the students' interests and abilities in the desired field of majors, so that it is expected to provide comfort in learning and affect the achievement of understanding and student learning achievement. The formation of data grouping is one of the methods or methods used in parsing uniform patterns in data. Grouping analysis is the process of sorting data in a group into several groups whose data similarity in one group is greater than the similarity of the data to data in other groups [4]. A method that is often used in data set grouping is the use of the clustering algorithm [5]. There are many data clustering algorithms, one of which is frequently used, namely Fuzzy C-Means. The K-Means clustering method is not appropriate to use to classify students based on the similarity of academic abilities in the process of determining majors according to the provisions of the Ministry of National Education. Of the 42 examples, some of the data are 10 Banjarmasin Public Senior High Schools 13 which will be grouped into 3 groups based on the similarity in the value of 10 core majors. The difference between the results of clustering student data manually (based on the provisions used in SMA 13 Banjarmasin) with the results of the K-Means algorithm clustering is due to (a) the K-Means algorithm performs student data clustering based on similar data patterns (scores) in groups that are which has been set, and is not tied to a certain rule or variable values. (b) The student clustering method used in SMA 13 Banjarmasin in determining majors is grouping students based on the similarity of values ​​in predetermined groups, but tied to a certain rule or variable value, namely the minimum standard value (minimum completeness criteria value / KKM). ) to belong to a certain group.