The growth in the number of educational institutions creates competition which encourages each educational institution to have a special strategy to deal with it. The challenge that must also be faced in the world of education is the uneven quality of education in various regions. One way to face the challenge of equal distribution of school quality is to create programs that suit the needs of each school. In this study, solving the problem of mapping schools owned by the XYZ educational foundation was carried out. The results of this study obtained a design of a school mapping indicator instrument to assess the quality of each school. Then obtained a simulation of school mapping design from the results of unsupervised learning with the K-Means and K-Medoids Clustering methods, as well as a simulation of predicting school mapping patterns from the results of supervised learning with the Decision Tree C4.5 method. The results of K-Medoids were selected for the proposed school mapping with a Davies Bouldin index value of 0.112. The model cluster owned by K-Medoids, namely Excellent School, has 44 schools; at Good School, there are 36 schools; and in the Improvement School, there are 20 schools. Meanwhile, the prediction pattern with Decision Tree C4.5 obtained rules with the dominant indicator attributes (IND) 1, 2, 3, and 4. Also, the prediction simulation results using the 80:20 ratio decision tree model show the new testing data with the assumption that the 101st school goes to cluster_1 with an accuracy rate of 95%.Keywords - Decision tree, K-means, K-medoids, School mapping indicator instrument.