基于CART算法的电信大学大学生求职等待期预测

Rani Gusti Angesti, A. Kurniawati, Hilman Dwi Anggana
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引用次数: 0

摘要

竞争越来越激烈,大学必须培养能够在职场竞争的毕业生。毕业生档案可以作为考核的标准是等待时间。理想的等待期是少于或等于三个月。毕业生在工作中竞争的能力成为对一所大学质量的评估。影响等待时间的几个因素是平均绩点(GPA)、学习时间和学生的组织活动。本研究以影响等待期的因素为基础,利用决策树建立等待期预测模型。对等待期预测结果进行分析,根据准确率、灵敏度和特异性对模型和决策树模型的准确性进行评价。决策树是一种可用于决策的数据挖掘技术。在本研究中,我们将使用CART (Classification and Regression Tree)算法。在数据挖掘分类过程中,首先进行数据预处理,然后对数据(训练数据和测试数据)进行拆分。根据分类树的结果,树的大小为10,有10条规则。该分类树模型的准确率为66.67%,灵敏度为72.97%,特异性为61.36%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of the Telkom University's Undergraduates Waiting Period for Getting a Job using the CART Algorithm
Competition is getting tougher, universities must prepare graduates who can compete in the world of work. The standard of graduates’ profiles that can be used as an assessment is the waiting period. The ideal target of a waiting period is less than or equal to three months. The competence of graduates who can compete in the world of work becomes an assessment of the quality of a university. Several factors that affect the waiting period are the Grade Point Average (GPA), study period, and students’ organization activity. This research was conducted to create a waiting period prediction model using a decision tree based on the factors that affect it. To analyze the waiting period prediction results, the accuracy of the model and the decision tree model is good or not based on the accuracy, sensitivity, and specificity. The decision tree is one of the data mining techniques that can be used for decision-making. In this research, we will use the CART (Classification and Regression Tree) algorithm. In the data mining classification process, data pre-processing will be carried out first, after that the splitting data (training and testing data) will be carried out. Based on the results of the classification tree, the tree size is 10 and has 10 rules. The accuracy of the classification tree's model is 66.67%, 72.97% of sensitivity, and 61.36% of specificity.
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