Classification of Student Work Readiness Using the Decision Tree and KNN Methods

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI:10.1109/ICETSIS61505.2024.10459355

Rifqy Muhammad Alfian, K. Lhaksmana

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Abstract

Machine learning methods have been implemented to perform prediction and classification tasks across various domain due to their superior time and cost efficiency compared to human expertise. This research employs such methods to predict student work readiness, which result is beneficial to assist universities to profile students and design career preparation programs tailored to their readiness level. The methods utilized in this research include Decision Tree and KNearest Neighborhood (KNN) classifiers. The confusion matrix demonstrates the applicability of these methods in predicting student work readiness. The KNN model, with k = 9, achieves accuracy of 97.50%, 96.90%, 96.80%, 97.60%, 95.80%, 97.00%, and 97.20%. On the other hand, the Decision Tree model achieves 98.60%, 98.80%, 98.90%, 98.70%, 98.60%, 98.70%, and 99.50%. Therefore, based on the given dataset of 6823 students, the Decision Tree model slightly outperforms KNN in predicting student work readiness.

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使用决策树和 KNN 方法对学生作业准备情况进行分类

由于机器学习方法与人类专业知识相比具有更高的时间和成本效率，因此已被用于执行各种领域的预测和分类任务。本研究采用这些方法来预测学生的工作准备程度，其结果有利于帮助大学对学生进行分析，并根据学生的准备程度设计职业准备计划。本研究采用的方法包括决策树和最近邻域（KNN）分类器。混淆矩阵显示了这些方法在预测学生工作准备程度方面的适用性。KNN 模型（k = 9）的准确率分别为 97.50%、96.90%、96.80%、97.60%、95.80%、97.00% 和 97.20%。另一方面，决策树模型的准确率分别为 98.60%、98.80%、98.90%、98.70%、98.60%、98.70% 和 99.50%。因此，基于 6823 名学生的给定数据集，决策树模型在预测学生工作准备度方面略胜于 KNN。

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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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)

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