基于粒子群优化的神经网络方法预测教育部网络数据配额援助接受者满意度

Annahl Riadi, Irvan Muzakkir, M. H. Botutihe
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引用次数: 0

摘要

对学生和讲师的免费配额支援是教育文化部提供的支援项目。自2019冠状病毒病大流行在印度尼西亚所有地区蔓延以来,该方案一直在实施。预计这一援助将帮助学生和讲师在covid-19大流行期间进行在线学习。本研究旨在预测使用者的满意度,以协助政府推进教育。数据处理采用快速挖掘应用和粒子群优化的神经网络方法。从数据处理结果来看,神经网络算法模型的精度值为42.44%,基于pso的神经网络算法模型的精度值为91.86%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Particle Swarm optimization-based Neural Network method for predicting satisfaction of recipients of internet data quota assistance from the ministry of education and culture
The free quota assistance program for students and lecturers is an assistance program provided by The Ministry of Education and Culture. This program has been implemented since the spread of the covid-19 pandemic in all regions of Indonesia. This assistance is expected to help students and lecturers carry out online learning caused by the pandemic covid-19. This study aims to predict the satisfaction level of the users so that it can help the government in advancing education. The data processing is carried out using the rapid miner application and the neural network method with particle swarm optimization. From the results of data processing, the accuracy value for the neural network algorithm model is 42.44%, and the accuracy value for the PSO-based neural network algorithm model is 91.86%.
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