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首页> 外文会议>East Indonesia Conference on Computer and Information Technology >Multivariate Data Model Prediction Analysis Using Backpropagation Neural Network Method
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Multivariate Data Model Prediction Analysis Using Backpropagation Neural Network Method

机译：使用BROWPAGAGAGAGAGAGAGAGAGAGION神经网络方法多变量数据模型预测分析

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摘要

Indonesia has a relatively large population, making health an important necessity. Census data has been widely available in accordance with problems in health. With the available data, researchers have a great opportunity to conduct research. Research on predictions in health is nothing new. By making predictions, it can help in making decisions quickly and accurately decisions that can be determined by the health sector or other fields. Prediction methods have been developed, one of which is an artificial neural network (ANN). In the ANN method, there is a Backpropagation Neural Network (BPNN) model. In general, the BPNN method is implemented in one input data model. Therefore, it requires an analytical study that uses a lot of data models to be implemented into the BPNN method. In this study, five data models were used with the number of variables and the amount of data that were different. Processed with the BPNN method and outputs the accuracy and execution time of all data models. From experiments and analyzes conducted on 5 kinds of data models, data model 4 has the number of variables 19 and the number of data 392 yields accuracy of 98.718% and a relatively slow execution time of 27.798 seconds. The highest execution time is found in the data model 3 with the number of variables 13 and the amount of data of 589 yields an accuracy of 95,385% and execution time of 45,442 seconds.
机译：印度尼西亚人口相对较大，使健康成为重要的必要性。人口普查数据已根据健康问题广泛提供。通过可用数据，研究人员有一个很好的开展研究机会。对健康预测的研究不是新的。通过预测，它可以帮助做出迅速和准确决策，这些决定可以由健康部门或其他领域确定。已经开发了预测方法，其中一个是人工神经网络（ANN）。在ANN方法中，存在反向译，神经网络（BPNN）模型。通常，BPNN方法在一个输入数据模型中实现。因此，它需要一个分析研究，该研究使用许多数据模型来实现到BPNN方法中。在这项研究中，五个数据模型用于变量数量和不同的数据量。使用BPNN方法处理并输出所有数据模型的准确性和执行时间。从5种数据模型进行的实验和分析中，数据模型4具有变量19的数量，并且数据的数量392产生的准确度为98.718％，并且相对缓慢的执行时间为27.798秒。在数据模型3中找到最高执行时间，其中变量13的数量和589的数据量产生95,385％的精度，并且执行时间为45,442秒。

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