采用假神经网络与PERCEPTRON算法来确定新生的课程

Novhirtamely Kahar, Widya Aritonang
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引用次数: 1

摘要

学习计划的确定对于未来的学生进入州立大学和私立大学是很重要的,这是因为这是一个决定,磨练,探索每个未来学生的能力,减少学生失败或辍学。这项研究的目的是让学生了解适合自己能力的学习计划。输入的数据是未来学生的个人数据、平均值、专业和考试成绩,选择学习项目,即计算机科学学院的学习项目,处理后的数据以选择学习项目的结果形式输出。研究者使用的方法是感知器方法,感知器是人工神经网络训练方法中使用的最简单的第一个训练算法程序。它由具有突触权重的单个神经元组成,该神经元使用硬限制激活函数进行调节。通过Matlab对感知机算法在确定专业方面的人工神经网络仿真进行分析,得到感知机方法的结果,即感知机方法能够确定目标输出和实际输出的准确率高达54.28%。
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IMPLEMENTASI JARINGAN SYARAF TIRUAN DENGAN ALGORITMA PERCEPTRON DALAM PENENTUAN PROGRAM STUDI MAHASISWA BARU
The determination of the study program is important for prospective students to enter state universities and private universities this is because it is a decision to hone, explore the abilities of each prospective student and reduce students who fail or drop out. This research is expected so that students know the appropriate study program based on their abilities. The data inputted is personal data of prospective students, average values, majors and test scores with Alternative study programs, namely study programs at the Faculty of Computer Science and The output of the data processed is in the form of results about selected study programs. The method used by researchers is the perceptron method, Perceptron is one of the simple ANN training methods used the first training algorithm procedure. It consists of a single neuron with synaptic weights that is regulated using the hard limit activation function. After an analysis of the ANN simulation of the perceptron algorithm with Matlab in determining the majors, the results of the perceptron method were obtained, namely the perceptron method was able to determine the target output and actual output as much as54.28%.
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