Perbandingan Fuzzy Time Series Lee untuk Meramalkan Nilai Tukar Petani di Provinsi Gorontalo

Alvitha Habibie, Lailany Yahya, Isran K. Hasan
{"title":"Perbandingan Fuzzy Time Series Lee untuk Meramalkan Nilai Tukar Petani di Provinsi Gorontalo","authors":"Alvitha Habibie, Lailany Yahya, Isran K. Hasan","doi":"10.34312/jjps.v4i1.17453","DOIUrl":null,"url":null,"abstract":"Gorontalo Province is one of the provinces in Indonesia where 60% of the population are farmers and fishermen. As much as 28,66% of PDRB in Gorontalo Province in 2020 was contributed by the agricultural sector. Farmer's Exchange Rate is a measurement capability of agricultural products in producing goods or services. Therefore, NTP forecasting is needed so that it becomes a reference in the future in making a decision to increase the agricultural sector. In this study, a comparison was made of the Holt Winters Exponential Smoothing method with Lee's Fuzzy Time Series to find out which is the best forecasting method for predicting NTP in Gorontalo Province. Based on the forecasting results, the accuracy value obtained from FTS Lee has a mape value of 0,65557% for FTS Lee order 1 and 0,55607%. While the accuracy value obtained by the multiplicative Holt Winters Exponential Smoothing is 5.92509% and the additive Holt Winters Exponential Smoothing is 6,14574%. From the forecasting results obtained, it can be concluded that the best method for predicting NTP in Gorontalo Province is the FTS Lee Order 2 method. ","PeriodicalId":315674,"journal":{"name":"Jambura Journal of Probability and Statistics","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jambura Journal of Probability and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34312/jjps.v4i1.17453","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Gorontalo Province is one of the provinces in Indonesia where 60% of the population are farmers and fishermen. As much as 28,66% of PDRB in Gorontalo Province in 2020 was contributed by the agricultural sector. Farmer's Exchange Rate is a measurement capability of agricultural products in producing goods or services. Therefore, NTP forecasting is needed so that it becomes a reference in the future in making a decision to increase the agricultural sector. In this study, a comparison was made of the Holt Winters Exponential Smoothing method with Lee's Fuzzy Time Series to find out which is the best forecasting method for predicting NTP in Gorontalo Province. Based on the forecasting results, the accuracy value obtained from FTS Lee has a mape value of 0,65557% for FTS Lee order 1 and 0,55607%. While the accuracy value obtained by the multiplicative Holt Winters Exponential Smoothing is 5.92509% and the additive Holt Winters Exponential Smoothing is 6,14574%. From the forecasting results obtained, it can be concluded that the best method for predicting NTP in Gorontalo Province is the FTS Lee Order 2 method. 
哥龙塔洛省是印度尼西亚的一个省,其中60%的人口是农民和渔民。到2020年,哥伦塔洛省高达28.66%的PDRB由农业部门贡献。农民汇率是衡量农产品生产商品或服务的能力。因此,国家毒理学规划预测是必要的,以便将来在作出增加农业部门的决定时成为参考。本研究将Holt Winters指数平滑法与Lee的模糊时间序列法进行比较,以找出哪种方法是预测Gorontalo省NTP的最佳预测方法。根据预测结果,FTS Lee得到的精度值对于FTS Lee阶1和阶1的映射值分别为0,65557%和0,55607%。而乘式Holt Winters指数平滑得到的精度值为5.92509%,加式Holt Winters指数平滑得到的精度值为614574%。从预测结果可以看出,预测Gorontalo省NTP的最佳方法是FTS Lee阶2方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信