Predictive Analysis Pendidikan Menggunakan Machine Learning di Sumatera Barat

Fitri Rahmah Ul Hasanah, Muhammad Kivlan Reftreka Nugraha
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Abstract

This study uses big data, namely the socio-economic 2019 data. The focus of this research is Education. Education is an important factor in life, but in West Sumatra using the socio-economic 2019 data it is still in the low category and far from the national average. In this study will predict the factors that influence education in West Sumatra with the data classification method. Methods for classifying large amounts of data have been developed, including Machine Learning. Machine Learning is a field of technology that is currently being widely used to create algorithms with large data (big data). The machine learning method used is Naive Bayes and Bagging. Furthermore, the two models were tested and showed that the Naive Bayes model gave the best performance compared to the Bagging model based on the values of accuracy, sensitivity and specificity. So the Naive Bayes model is the best machine learning model for predicting the factors that affect education, namely household members, gender and regional classification.
本研究使用大数据,即2019年社会经济数据。本研究的重点是教育。教育是生活中的一个重要因素,但根据2019年的社会经济数据,在西苏门答腊,教育仍处于低水平,远低于全国平均水平。本研究将采用数据分类的方法对影响西苏门答腊教育的因素进行预测。已经开发了对大量数据进行分类的方法,包括机器学习。机器学习是目前被广泛用于创建具有大数据(大数据)的算法的技术领域。使用的机器学习方法是朴素贝叶斯和Bagging。此外,对两种模型进行了测试,结果表明,基于准确性、灵敏度和特异性值,朴素贝叶斯模型与Bagging模型相比具有最佳性能。因此,朴素贝叶斯模型是预测影响教育的因素(即家庭成员、性别和地区分类)的最佳机器学习模型。
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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