{"title":"印度尼西亚橡胶生产采用了模糊时间系列两种基于区间孔径的长期兼容性因素","authors":"Etna Vianita, Heru Tjahjana, Titi Udjiani","doi":"10.19184/mims.v22i1.30414","DOIUrl":null,"url":null,"abstract":"The fuzzy time series method for forecasting continues to develop over time. This research discusses fuzzy time series, which considers two factors for high order using interval partitioning based on interval ratio with long relation construction for getting different accuracy in forecasting between combination method and existing method. The first step is the formation of the universe of speech. Second, divide the universe of discourse into several intervals using interval ratios. Third, fuzzification. Fourth, build fuzzy logic relations and fuzzy logic relation groups, and fifth, defuzzification. The previous methods would be compared with the fuzzy logic relation construction result. The simulation used Indonesian rubber production data for 2000-2020. The results and errors were tested using the average forecasting error rate (AFER). AFER value of the forecasting method is 1.863% obtained.Keywords: Forecasting, fuzzy time series, long relationMSC2020: 62M10, 62M20, 62M86, 03E72","PeriodicalId":264607,"journal":{"name":"Majalah Ilmiah Matematika dan Statistika","volume":"162 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PERAMALAN PRODUKSI KARET INDONESIA MENGGUNAKAN FUZZY TIME SERIES DUA FAKTOR ORDE TINGGI RELASI PANJANG BERDASARKAN RASIO INTERVAL\",\"authors\":\"Etna Vianita, Heru Tjahjana, Titi Udjiani\",\"doi\":\"10.19184/mims.v22i1.30414\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The fuzzy time series method for forecasting continues to develop over time. This research discusses fuzzy time series, which considers two factors for high order using interval partitioning based on interval ratio with long relation construction for getting different accuracy in forecasting between combination method and existing method. The first step is the formation of the universe of speech. Second, divide the universe of discourse into several intervals using interval ratios. Third, fuzzification. Fourth, build fuzzy logic relations and fuzzy logic relation groups, and fifth, defuzzification. The previous methods would be compared with the fuzzy logic relation construction result. The simulation used Indonesian rubber production data for 2000-2020. The results and errors were tested using the average forecasting error rate (AFER). AFER value of the forecasting method is 1.863% obtained.Keywords: Forecasting, fuzzy time series, long relationMSC2020: 62M10, 62M20, 62M86, 03E72\",\"PeriodicalId\":264607,\"journal\":{\"name\":\"Majalah Ilmiah Matematika dan Statistika\",\"volume\":\"162 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Majalah Ilmiah Matematika dan Statistika\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.19184/mims.v22i1.30414\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Majalah Ilmiah Matematika dan Statistika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.19184/mims.v22i1.30414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PERAMALAN PRODUKSI KARET INDONESIA MENGGUNAKAN FUZZY TIME SERIES DUA FAKTOR ORDE TINGGI RELASI PANJANG BERDASARKAN RASIO INTERVAL
The fuzzy time series method for forecasting continues to develop over time. This research discusses fuzzy time series, which considers two factors for high order using interval partitioning based on interval ratio with long relation construction for getting different accuracy in forecasting between combination method and existing method. The first step is the formation of the universe of speech. Second, divide the universe of discourse into several intervals using interval ratios. Third, fuzzification. Fourth, build fuzzy logic relations and fuzzy logic relation groups, and fifth, defuzzification. The previous methods would be compared with the fuzzy logic relation construction result. The simulation used Indonesian rubber production data for 2000-2020. The results and errors were tested using the average forecasting error rate (AFER). AFER value of the forecasting method is 1.863% obtained.Keywords: Forecasting, fuzzy time series, long relationMSC2020: 62M10, 62M20, 62M86, 03E72
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