PERAMALAN MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK
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The purpose of the study is to forecast the price of rice in the city of Denpasar in 2017 using backpropagation neural network method. Backpropagation neural network is a model of artificial neural network by finding the optimal weight value. Artificial neural networks are information processing systems that have certain performance characteristics similar to that of human neural networks. This analysis uses time series data of rice prices in the city of Denpasar from January 2001 until December 2016. The results of this research, concludes that the lowest rice price is predicted in July 2017 at Rp9791.5 while the highest rice price in April 2017 for Rp9839.4.Keywords:
Backpropagation
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In this paper,five neural network models,such as back-propagation neural network(BPNN),radial basis neural network(RBFNN),generalized regression neural network(GRNN),cascade forward backpropagation neural network(CFNN) and Elman backpropagation neural network(ELMNN),have been evaluated in predicting protein secondary structures.The prediction accuracy of GRNN is better than the others.In addition,some affecting factors(the training sets and the parameters of network) are also discussed.
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The purpose of the study is to forecast the price of rice in the city of Denpasar in 2017 using backpropagation neural network method. Backpropagation neural network is a model of artificial neural network by finding the optimal weight value. Artificial neural networks are information processing systems that have certain performance characteristics similar to that of human neural networks. This analysis uses time series data of rice prices in the city of Denpasar from January 2001 until December 2016. The results of this research, concludes that the lowest rice price is predicted in July 2017 at Rp9791.5 while the highest rice price in April 2017 for Rp9839.4.
Backpropagation
Value (mathematics)
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The authors introduce a novel patient severity measurement model using neural networks. A three layer, fully connected backpropagation neural network was used in the pilot experiment. The results are promising and demonstrate that the backpropagation neural network technique is capable of assessing the severity value by learning from raw data. The neural network is easy to improve and of relatively low cost. It saves the expert's valuable time used in assigning numerical values to variables.< >
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Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and an understanding of its application prospect in animal diseases.
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Machine Learning (ML) is a technology that can revolutionize the world. It is a technology based on AI (Artificial Intelligence) and can predict the outcomes using the previous algorithms without programming it. A subset of artificial intelligence is called machine learning (AI). A machine may automatically learn from data and get better at what it does thanks to machine learning. “If additional data can be gathered to help a machine perform better, it can learn. A developing technology called machine learning allows computers to learn from historical data. Machines can predict the outcomes by machine learning. For Nowadays machine learning is very important for us because it makes our work easy. to many companies are using machine learning in their products, like google is using google its google assistant, which takes our voice command and gives what do we want from it, and google is also using its goggle lens form which we can find anything just by clicking a picture, and Netflix is using machine learning for recommendation of any movies or series, Machine learning has a very deep effect on our life, like nowadays we are using selfdriving car’s.
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Machine learning is often perceived as a sophisticated technology accessible only by highly trained experts. This prevents many physicians and biologists from using this tool in their research. The goal of this paper is to eliminate this out-dated perception. We argue that the recent development of auto machine learning techniques enables biomedical researchers to quickly build competitive machine learning classifiers without requiring in-depth knowledge about the underlying algorithms. We study the case of predicting the risk of cardiovascular diseases. To support our claim, we compare auto machine learning techniques against a graduate student using several important metrics, including the total amounts of time required for building machine learning models and the final classification accuracies on unseen test datasets. In particular, the graduate student manually builds multiple machine learning classifiers and tunes their parameters for one month using scikit-learn library, which is a popular machine learning library to obtain ones that perform best on two given, publicly available datasets. We run an auto machine learning library called auto-sklearn on the same datasets. Our experiments find that automatic machine learning takes 1 h to produce classifiers that perform better than the ones built by the graduate student in one month. More importantly, building this classifier only requires a few lines of standard code. Our findings are expected to change the way physicians see machine learning and encourage wide adoption of Artificial Intelligence (AI) techniques in clinical domains.
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Artificial Neural Network (Artificial Neural Network) is an information processing paradigm inspired by the workings of biological nervous systems, such as the performance of the brain, which processes information. Toko Anugerah Jaya Pancing 99 faces challenges in predicting product sales that fluctuate according to customer demand, and external factors can also affect sales of their products. To determine store sales predictions using the backpropagation method. From the research results with data training experiments it was found that the iterations process stopped when the epoch reached 6, time 0.00.01, performance 0.317, Gradient 0.359, and Validation Checks 6. Keywords: Prediction, Backpropagation, Neural Networks, Sales
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