Faris Robby Zakariya, Mat Syai'in, Ryan Yudha Adhitya
{"title":"mplementation of Extreme Learning Machine for Water Quality Control in Vannamei Shrimp Ponds","authors":"Faris Robby Zakariya, Mat Syai'in, Ryan Yudha Adhitya","doi":"10.52435/complete.v4i1.322","DOIUrl":null,"url":null,"abstract":"The application of science and technology knowledge is crucial in supporting the Indonesian Government's program to increase the production of Litopenaesus Vannamei shrimp. This research collaborates with shrimp pond farmers to develop technology that supports the cultivation of vaname shrimp. The water quality affect the harvest results, and the water parameters such as pH, dissolved oxygen (DO), alkalinity, salinity, and temperature should be monitored and adjusted if the parameters exceed the predetermined limits. We have developed an Extreme Learning Machine-based water quality management system tailored to the geographic conditions of Indonesia. This tool uses sensors to read data from the pond water, which is then processed by a microcontroller and displayed in a web-based information system. This tool helps farmers determine the water conditions and condition it accordingly. Based on experimental result error dari data training adalah 0.0001 dan error pada data testing yaitu sebesar 0.1851, it can be seen the Extreme Learning Machine has good performance for this research.","PeriodicalId":377345,"journal":{"name":"Journal of Computer Electronic and Telecommunication","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Electronic and Telecommunication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52435/complete.v4i1.322","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
The application of science and technology knowledge is crucial in supporting the Indonesian Government's program to increase the production of Litopenaesus Vannamei shrimp. This research collaborates with shrimp pond farmers to develop technology that supports the cultivation of vaname shrimp. The water quality affect the harvest results, and the water parameters such as pH, dissolved oxygen (DO), alkalinity, salinity, and temperature should be monitored and adjusted if the parameters exceed the predetermined limits. We have developed an Extreme Learning Machine-based water quality management system tailored to the geographic conditions of Indonesia. This tool uses sensors to read data from the pond water, which is then processed by a microcontroller and displayed in a web-based information system. This tool helps farmers determine the water conditions and condition it accordingly. Based on experimental result error dari data training adalah 0.0001 dan error pada data testing yaitu sebesar 0.1851, it can be seen the Extreme Learning Machine has good performance for this research.
科学和技术知识的应用对于支持印度尼西亚政府增加凡纳滨对虾产量的方案至关重要。本研究与虾塘养殖户合作,开发支持钒虾养殖的技术。水质影响收获结果,当pH值、溶解氧(DO)、碱度、盐度、温度等水质参数超过预定限值时,应进行监测和调整。我们根据印尼的地理条件开发了一套基于极限学习机的水质管理系统。该工具使用传感器从池塘水中读取数据,然后由微控制器处理并显示在基于网络的信息系统中。这个工具可以帮助农民确定水的状况,并对其进行相应的调节。基于实验结果,误差达里数据训练adalah 0.0001,误差达里数据测试yyitu sebesar 0.1851,可以看出极限学习机对于本研究具有良好的性能。