Penerapan Metode Clustering dengan Algoritma K-Means pada Pengelompokkan Peminatan Mata Kuliah
DOI:
https://doi.org/10.54066/jikma.v1i6.1028Keywords:
Data Mining, Cluetering, K-MensAbstract
Choosing a concentration in student academic activities is not an easy thing because it depends on interests, talents and desires, therefore careful consideration is needed so that students do not make a mistake in choosing the desired concentration. This often happens when final semester students do their final assignment but it does not match their field of ability. Choosing a concentration haphazardly without careful consideration can have a negative impact on students, namely difficulty in absorbing lecture material. Therefore, a special method is needed that students can use to determine student concentration. One of the methods used is the K-Means method. The K-Means algorithm is a non-hierarchical method that initially takes a number of population components to become the initial cluster center. At this stage the cluster center is selected randomly from a set of data populations. Next, K-Means tests each component in the data population and marks the component to one of the cluster centers that has been defined depending on the minimum distance between components and each cluster. with a total of 100 data records, using cluster centers C1 70, 82.5, 85, C2 70, 75, 80 and C3 80, 85, 80 produces 6 iterations with the results of Cluster 1. Students are recommended to enter the Expert Systems Concentration. In the calculation above, there are 3 students who are included in cluster 1. Cluster 2 Students are recommended to enter the multimedia programming concentration. In the calculation above, there are 20 students included in cluster 2. Cluster 3 Students are recommended to enter the Cisci and Network Concentration. In the calculation above, there are 34 students included in cluster 3. From validation testing it is obtained: initial and final centroid of the first attribute: 5.83%, second attribute: 31.44%, third attribute: 35.89%. It is hoped to develop concentration clustering for Information Systems majors using other methods, not only the K-Means method, and determining concentration majors using variables other than academic grades, such as non-academic achievement scores which are linear with the study program. In the future, the concentration determination system will be carried out in the information systems study program.
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