Abstract
One of the problems that often occurs in school administration is the late payment of tuition fees. Therefore, it is necessary to evaluate the education payment process so that in the future the payment process can run in an orderly and disciplined manner. This study aims to create a cluster model for grouping student administration payments. This type of research is quantitative using the K-Means Clustering method to classify payment data based on 2 variables, namely the time of payment and the income of students' guardians carried out in private elementary schools in Semarang. The data used in this study is payment data for the 2019/2020 academic year, which totals 1,933 records, covering transactions from 419 students. Determining the number of clusters is calculated using the elbow method, the best clusters obtained from the data used are 3 clusters, namely clusters 0, 1 and 2. Our findings show that cluster 2 has the largest percentage of early monthly administration payments, namely 52.5%, the percentage is on time the highest was in cluster 1, namely 74.8%, and the highest percentage of late payments was in cluster 0, namely 28.6%. The results of the analysis show that the main factor for late payments is not the guardian's income but other external factors, as evidenced by the highest percentage of late payments in cluster 0, where the average income of student guardians is = 10,000,000.
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