BibTex Citation Data :
@article{J.Gauss27522, author = {Johanes Prabowo and Rukun Santoso and hasbi Yasin}, title = {IMPLEMENTASI JARINGAN SYARAF TIRUAN BACKPROPAGATION DENGAN ALGORITMA CONJUGATE GRADIENT UNTUK KLASIFIKASI KONDISI RUMAH (Studi Kasus di Kabupaten Cilacap Tahun 2018)}, journal = {Jurnal Gaussian}, volume = {9}, number = {1}, year = {2020}, keywords = {House, Classification, Artificial Neural Networks, Conjugate Gradient}, abstract = { House is one aspect of the welfare of society that must be met, because house is the main need for human life besides clothing and food. The condition of the house as a good shelter can be known from the structure and facilities of buildings. This research aims to analyze the classification of house conditions is livable or not livable. The method used is artificial neural networks (ANN). ANN is a system information processing that has characteristics similar to biological neural networks. In this research the optimization method used is the conjugate gradient algorithm. The data used are data of Survei Sosial Ekonomi Nasional (Susenas) March 2018 Kor Keterangan Perumahan for Cilacap Regency. The data is divided into training data and testing data with the proportion that gives the highest average accuracy is 90% for training data and 10% for testing data. The best architecture obtained a model consisting of 8 neurons in input layer, 10 neurons in hidden layer and 1 neuron in output layer. The activation function used are bipolar sigmoid in the hidden layer and binary sigmoid in the output layer. The results of the analysis showed that ANN works very well for classification on house conditions in Cilacap Regency with an average accuracy of 98.96% at the training stage and 97.58% at the testing stage. Keywords: House, Classification, Artificial Neural Networks, Conjugate Gradient }, issn = {2339-2541}, pages = {41--49} doi = {10.14710/j.gauss.9.1.41-49}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/27522} }
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House is one aspect of the welfare of society that must be met, because house is the main need for human life besides clothing and food. The condition of the house as a good shelter can be known from the structure and facilities of buildings. This research aims to analyze the classification of house conditions is livable or not livable. The method used is artificial neural networks (ANN). ANN is a system information processing that has characteristics similar to biological neural networks. In this research the optimization method used is the conjugate gradient algorithm. The data used are data of Survei Sosial Ekonomi Nasional (Susenas) March 2018 Kor Keterangan Perumahan for Cilacap Regency. The data is divided into training data and testing data with the proportion that gives the highest average accuracy is 90% for training data and 10% for testing data. The best architecture obtained a model consisting of 8 neurons in input layer, 10 neurons in hidden layer and 1 neuron in output layer. The activation function used are bipolar sigmoid in the hidden layer and binary sigmoid in the output layer. The results of the analysis showed that ANN works very well for classification on house conditions in Cilacap Regency with an average accuracy of 98.96% at the training stage and 97.58% at the testing stage.
Keywords: House, Classification, Artificial Neural Networks, Conjugate Gradient
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