{"title":"Ambon市消费者价格指数预测(IHK)使用Elman Recurrent Neural Network (ERNN)","authors":"Jefri Radjabaycolle","doi":"10.30598/tensorvol1iss2pp65-75","DOIUrl":null,"url":null,"abstract":"Indeks Harga Konsumen (IHK) is an economic indicator that can provide information on developments and changes in the prices of goods and services that are predominantly consumed by the public within a certain period of time. In this study the method to be used is the Elman Recurrent Neural Network (ERNN). The research data uses Ambon City IHK data from 2016 to 2019. The data used as research objects are: Food, Beverages, Cigarettes and Tobacco, Housing, Water, Electricity, Gas and Fuel, Clothing, Health, Education, Recreation, and Sport, Transportation, Communication and Financial Services as input variables. The results of training with 5 hidden layers at a maximum epoch of 100,000 obtained the smallest MAPE value of 1.1773. Then the results of testing using the parameters in the experiment on the number of hidden layer neurons 20 obtained the smallest MAPE value of 0.461823.","PeriodicalId":294430,"journal":{"name":"Tensor: Pure and Applied Mathematics Journal","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Prediksi Indeks Harga Konsumen (IHK) Kota Ambon Menggunakan Elman Recurrent Neural Network (ERNN)\",\"authors\":\"Jefri Radjabaycolle\",\"doi\":\"10.30598/tensorvol1iss2pp65-75\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Indeks Harga Konsumen (IHK) is an economic indicator that can provide information on developments and changes in the prices of goods and services that are predominantly consumed by the public within a certain period of time. In this study the method to be used is the Elman Recurrent Neural Network (ERNN). The research data uses Ambon City IHK data from 2016 to 2019. The data used as research objects are: Food, Beverages, Cigarettes and Tobacco, Housing, Water, Electricity, Gas and Fuel, Clothing, Health, Education, Recreation, and Sport, Transportation, Communication and Financial Services as input variables. The results of training with 5 hidden layers at a maximum epoch of 100,000 obtained the smallest MAPE value of 1.1773. Then the results of testing using the parameters in the experiment on the number of hidden layer neurons 20 obtained the smallest MAPE value of 0.461823.\",\"PeriodicalId\":294430,\"journal\":{\"name\":\"Tensor: Pure and Applied Mathematics Journal\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tensor: Pure and Applied Mathematics Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.30598/tensorvol1iss2pp65-75\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tensor: Pure and Applied Mathematics Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30598/tensorvol1iss2pp65-75","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
Harga Konsumen指数(IHK)是一项经济指标,可以提供有关在一段时间内主要由公众消费的商品和服务价格的发展和变化的信息。在本研究中使用的方法是Elman递归神经网络(ERNN)。研究数据使用安邦城市IHK 2016年至2019年的数据。作为研究对象的数据有:食品、饮料、香烟和烟草、住房、水、电、气和燃料、服装、健康、教育、娱乐和体育、交通、通信和金融服务作为输入变量。在最大历元为100,000时,5个隐藏层的训练结果得到最小的MAPE值为1.1773。然后利用实验中的参数对隐藏层神经元个数20进行测试,得到最小的MAPE值为0.461823。
Prediksi Indeks Harga Konsumen (IHK) Kota Ambon Menggunakan Elman Recurrent Neural Network (ERNN)
Indeks Harga Konsumen (IHK) is an economic indicator that can provide information on developments and changes in the prices of goods and services that are predominantly consumed by the public within a certain period of time. In this study the method to be used is the Elman Recurrent Neural Network (ERNN). The research data uses Ambon City IHK data from 2016 to 2019. The data used as research objects are: Food, Beverages, Cigarettes and Tobacco, Housing, Water, Electricity, Gas and Fuel, Clothing, Health, Education, Recreation, and Sport, Transportation, Communication and Financial Services as input variables. The results of training with 5 hidden layers at a maximum epoch of 100,000 obtained the smallest MAPE value of 1.1773. Then the results of testing using the parameters in the experiment on the number of hidden layer neurons 20 obtained the smallest MAPE value of 0.461823.