分布式信息系统中商业智能的发展对登革热出血热的可视化预测和处理提供建议

Radityo Prasetianto Wibowo, Wiwik Anggraeni, Tresnaning Arifiyah, Edwin Riksakomara, F. Samopa, Pujiadi Pujiadi, Siti Aminatus Zehroh, Nur Aini Lestari
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引用次数: 1

摘要

背景:从2017年到2020年,印度尼西亚每月有150例登革热病例,每天有1人以上死亡。登革出血热(DHF)患者死亡的因素之一是由于医院或诊所对患者的处理较晚。玛琅县卫生办公室记录了2016年发生的1114例登革出血热病例,可用的病房数量有限。因此,本研究以玛琅摄政为个案研究对象。目的:本研究旨在制作一个仪表板来显示预测结果,可视化DHF患者的分布,并为玛琅卫生局处理DHF患者提供缓解建议。方法:本研究采用了商业智能(BI)开发方法,该方法包括两个主要阶段,即商业智能的制作和商业智能的使用。本研究采用了BI阶段的制定,包括BI开发策略、数据源识别与准备、BI工具选择、BI设计与实施四个阶段。在提取、加载和转换过程中,本研究使用了本质转换和预测。结果:BI方法成功构建了仪表板。仪表板显示登革热出血热预测结果的可视化,登革热患者人数的详细信息,每年登革热患者趋势和预测2个月的患者,以及每个社区卫生办公室的缓解建议。结论:我们使用BI开发方法构建了BI Dashboard。它需要一些治疗来获得更好的表现。其中包括使用数据虚拟化技术提高ETL性能,考虑使用云计算技术,通过了解关键成功因素来进行进一步评估,以确定成功和弱点的水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Business Intelligence Development in Distributed Information Systems to Visualized Predicting and Give Recommendation for Handling Dengue Hemorrhagic Fever
Background: Indonesia has 150 dengue cases every month, and more than one person dies every day from 2017 to 2020. One of the factors of Dengue Hemorrhagic Fever (DHF) patients dying is due to the late handling of patients in hospitals or clinics. Health Office of Malang Regency recorded 1,114 cases of DHF that occurred during 2016, and the number of patients room available is limited. Therefore, Malang Regency is used as a case study in this research. Objective: This study aims to make a dashboard to display the predictions, visualize the distribution of DHF patients, and give mitigation recommendations for handling DHF patients in Malang Health Office. Methods: This study used the Business Intelligence (BI) Development method, which consists of two main phases, namely the making of Business Intelligence and the use of Business Intelligence. This research used the making of the BI phase, which consists of four stages, which are BI development strategies, identification and preparation of data sources, selecting BI tools, and designing and implementing BI. In the Extract, Load, and Transform process, this study used essential transformation and forecast. Results: BI method has succeeded in building the dashboard. The dashboard displays the visualization of Dengue Hemorrhagic Fever predicted results, detail of Dengue Fever Patient number, Dengue Fever patient trends per year and predictions 2 Monthly patient, and mitigation recommendation for each Community Health Office. Conclusion: We have built the BI Dashboard using the BI development method. It needs some treatment to get better performance. These are improving ETL performance using data virtualization technology, considering the use of cloud computing technology, conducting further evaluations by understanding the critical success factors to determine the level of success and weaknesses.
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