Model prediction for accreditation of public junior high school in Bogor using spatial decision tree

Endang Purnama Giri, A. M. Arymurthy
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引用次数: 1

Abstract

Indonesia has a large geographic area with large variance of quality service of education. This paper will analyze whether it has a correlation or not, between the quality level of school and its characteristic of geographic area. This paper describes performance of two kind of decision tree method in predicting level of accreditation for public junior high school in Bogor, West Java. With three scenarios (training set as testing set, leave one out, and 3-fold cross validation) using conventional decision tree (ID3) the accuracy level reach 97.14%, 40%, and 51.51% respectively. On the other hand, using spatial decision tree (SDT) the accuracy reach level 97.14%, 54.29%, and 54.28%. Based on the accuracy level and the structure of tree that was constructed, SDT produce better result. These facts will imply that quality level of public junior high schools on Bogor have correlation with characteristics of geographic area. However, SDT need much more computation time to construct decision tree rather than ID3, so for big number of data and big number of attributes, SDT will not appropriate.
基于空间决策树的茂物公立初中认证模型预测
印尼幅员辽阔,教育服务质量参差不齐。本文将分析学校质量水平与其地理区域特征之间是否存在相关性。本文介绍了两种决策树方法在预测西爪哇茂物市公立初中认证水平中的应用效果。采用传统决策树(ID3),在训练集作为测试集,留一个,三重交叉验证三种场景下,准确率分别达到97.14%,40%,51.51%。空间决策树(SDT)的准确率分别达到97.14%、54.29%和54.28%。基于精度水平和所构建的树的结构,SDT取得了较好的效果。这些事实表明,茂物公立初中的质量水平与地理区域特征存在相关性。但是,SDT在构建决策树时需要比ID3更多的计算时间,因此对于数据量大、属性量大的情况,SDT将不适合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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