Prediction Of Drug Sales Using Methods Forecasting Double Exponential Smoothing (Case Study : Hospital Pharmacy of Condong Catur)

Annesa Maya Sabarina, Heru Cahya Rustamaji, Hidayatulah Himawan
{"title":"Prediction Of Drug Sales Using Methods Forecasting Double Exponential Smoothing (Case Study : Hospital Pharmacy of Condong Catur)","authors":"Annesa Maya Sabarina, Heru Cahya Rustamaji, Hidayatulah Himawan","doi":"10.31315/TELEMATIKA.V18I1.4586","DOIUrl":null,"url":null,"abstract":"Informasi Artikel Abstract Received: 12 December 2020 Revised: 12 January 2021 Accepted: 30 January 2021 Published: 28 February 2021 Purpose: Knowing the best alpha value from the data for each type of drug with various alpha parameters in the Double Exponential Smoothing Method and knowing the prediction results on each type of drug data at the Condong Catur Hospital pharmacy. Design/methodology/approach: Applying the Double Exponential Smoothing method with alpha parameters 0.1; 0.2; 0.3; 0.4; 0.5; 0.6; 0.7; 0.8; 0.9 Findings/result: The test results on a system built using test data show that the double exponential smoothing method provides accuracy below 20% by producing a different Alpha (α) for each type of drug because the trend patterns in each drug sale are different at the Pharmacy at the Condong Catur Hospital. . Originality/value/state of the art: Based on previous research, this study has similar characteristics such as themes, parameters and methods used. Previous researchers used smoothing methods such as Double Exponential Smoothing in predicting stock / sales of goods","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"163 1","pages":"106"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Telematika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31315/TELEMATIKA.V18I1.4586","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

Informasi Artikel Abstract Received: 12 December 2020 Revised: 12 January 2021 Accepted: 30 January 2021 Published: 28 February 2021 Purpose: Knowing the best alpha value from the data for each type of drug with various alpha parameters in the Double Exponential Smoothing Method and knowing the prediction results on each type of drug data at the Condong Catur Hospital pharmacy. Design/methodology/approach: Applying the Double Exponential Smoothing method with alpha parameters 0.1; 0.2; 0.3; 0.4; 0.5; 0.6; 0.7; 0.8; 0.9 Findings/result: The test results on a system built using test data show that the double exponential smoothing method provides accuracy below 20% by producing a different Alpha (α) for each type of drug because the trend patterns in each drug sale are different at the Pharmacy at the Condong Catur Hospital. . Originality/value/state of the art: Based on previous research, this study has similar characteristics such as themes, parameters and methods used. Previous researchers used smoothing methods such as Double Exponential Smoothing in predicting stock / sales of goods
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于双指数平滑预测方法的药品销售预测(以广东医院药房为例)
摘要收稿日期:2020年12月12日修稿日期:2021年1月12日接受日期:2021年1月30日发布日期:2021年2月28日目的:利用双指数平滑法对不同alpha参数的各类药物数据求出最佳alpha值,了解Condong Catur医院药房各类药物数据的预测结果。设计/方法/途径:采用alpha参数为0.1的双指数平滑法;0.2;0.3;0.4;0.5;0.6;0.7;0.8;0.9发现/结果:在使用测试数据构建的系统上的测试结果表明,由于Condong Catur医院药房每种药物销售的趋势模式不同,双指数平滑法对每种药物产生不同的Alpha (α),准确度低于20%。原创性/价值/技术水平:在前人研究的基础上,本研究在主题、参数、方法等方面具有相似的特点。以前的研究人员使用平滑方法,如双指数平滑来预测商品的库存/销售
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 去求助
来源期刊
自引率
0.00%
发文量
7
审稿时长
24 weeks
期刊最新文献
Identification of Social Media Posts Containing Self-reported COVID-19 Symptoms using Triple Word Embeddings and Long Short-Term Memory Deep Learning for Histopathological Image Analysis: A Convolutional Neural Network Approach to Colon Cancer Classification Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization Optimizing Clustering of Indonesian Text Data Using Particle Swarm Optimization Algorithm: A Case Study of the Quran Translation Monitoring Development Board based on InfluxDB and Grafana
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1