@article{UNY23322,
         journal = {Proceeding of  International Conference On Research, Implementation And Education  Of Mathematics And Sciences 2015 (ICRIEMS 2015), Yogyakarta State University, 17-19 May 2015},
           title = {BUILDING A MODEL TO PREDICT SCHOOL ACCREDITATION RANK USING BOOSTED CLASSIFICATION TREE},
          author = {. Yesi Nindahayati, Hari Wijayanto and . Bagus Sartono},
       publisher = {Faculty of Mathematics and Sciences Yogyakarta State University},
            year = {2015},
             url = {http://eprints.uny.ac.id/23322/},
        abstract = {Education has a key role to make a better life. The Education for All (EFA) is a global movement led by UNESCO, aiming to provide good basic education for all children, youths and adults. Indonesian government has committed to improve the education quality as stated in law on national education system (Law No. 20/2003). School accreditation rank which is issued by National Accreditation Board for School/Madrasah (BAN S/M) is depiction of education quality provided by school. However the number of accredited school has not met the target yet so that the government faces difficulty in the planning of budget and actions. The prediction of school classification based on accreditation rank to the un-accredited schools, therefore, has important role as reference to improve quality of education. 
In recent years the introduction of aggregation methods led to many new techniques within the field of prediction and classification. Boosting is one of the widely used ensemble for classification with a goal of improving the accuracy of classifier. The objective of this study is to predict school accreditation rank using boosted classification tree compared to single tree utilizing the education database. It is showed that the accuracy of prediction is improved by use of boosting method. Comparisons between the methods are based on misclassification rates as well as criteria that take ordinality into account, like mean absolute error, mean square error and Kendall?s ? association measures. 
Key words: boosting, classification tree, school accreditation rank}
}