Penggunaan Model Klaster K-Means Dan Klasifikasi KNN Untuk Identifikasi Pengetahuan Matematika Mahasiswa
Keywords:
Classification, KNN, Cluster, K-MEANS, Mathematics_knowledgeAbstract
Students' mathematical knowledge is an important factor that needs to be considered in the learning process majoring in Informatics Engineering. Students who have low mathematical knowledge will definitely have difficulty calculating and applying the algorithms used to solve programming and computing problems. The weaknesses that these students have can become obstacles to the future. By knowing students' mathematical knowledge, the learning process does not have to be adjusted to the student's level of knowledge, but further action is needed to increase students' mathematical knowledge. One way to find out students' mathematical knowledge is to classify students' mathematical knowledge. Classification of students' mathematical knowledge can be done using a very popular method, namely the K-Nearest Neighbor (KNN) method. However, in order for classification to be carried out, grouping must be carried out first to form classes from the results of grouping mathematical knowledge data. The grouping of students' mathematical knowledge data was carried out using the K-MEANS method. Data on students' mathematical knowledge was taken based on course grades for calculus 1, applied physics, calculus 2, statistics and probability, and discrete mathematics. The grades from several of these courses will be processed using K-MEANS and form three groups of mathematical knowledge, namely slow, sufficient, and fast. Thus, it was concluded that to determine the classification of slow, sufficient, and fast mathematical knowledge of students majoring in informatics engineering, it was carried out using the KNN method, but first the data was grouped using the KMEAN method.References
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