RANCANG BANGUN SISTEM IOT UNTUK PETERNAKAN IKAN HIAS KOKI DAN MOLLY

Theresia Herlina Rochadiani, Handri Santoso, W. Widjaja, Ulfah Dzakiyah Nisrina Ariqoh, Regina Angelika Septi Rahayu, Youzy Natasya
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Abstract

As a country rich in fish species diversity, Indonesia was ranked as the fourth largest exporter of ornamental fish in the world from 2016 to 2019. Ornamental fish is one of the potential commodities. However, crop failure due to uncontrolled water quality such as rising ph levels is a problem that needs to be solved. As a solution, this study builds a LoRaWAN-based IoT system to be able to monitor ph, tds, and pool water temperature levels of koki and molly fish pond from anywhere and anytime through a mobile application. The sensor node uses a Heltec Lora Wi-Fi 32 v2 microcontroller that supports LoRa technology and uses the DS18B20 sensor as a temperature sensor, and a DFRobot sensor to measure ph and tds. From the test results, the accuracy of each sensor is quite good with an average error of 0.35 for temperature sensors and 0.37 for ph sensors, as well as an error percentage on tds sensors of 1.5. From the results of the implementation, the ph and temperature values in the koki fish  and molly fish pond are quite good as habitats, but the tds value of pond water is quite high.
为厨师和莫莉的渔场设计了一个系统
作为一个鱼类物种多样性丰富的国家,印度尼西亚在2016年至2019年期间是世界第四大观赏鱼出口国。观赏鱼是潜在的商品之一。然而,由于不受控制的水质(如ph值上升)导致的作物歉收是一个需要解决的问题。作为解决方案,本研究构建了一个基于lorawan的物联网系统,能够通过移动应用程序随时随地监测红木和茉莉鱼塘的ph值、tds和池水温度水平。传感器节点采用支持Lora技术的Heltec Lora Wi-Fi 32 v2微控制器,使用DS18B20传感器作为温度传感器,使用DFRobot传感器测量ph和tds。从测试结果来看,每个传感器的精度都很好,温度传感器的平均误差为0.35,ph传感器的平均误差为0.37,tds传感器的误差百分比为1.5。从实施结果来看,红豆鱼和茉莉鱼塘的ph值和温度都很好,但池水的tds值较高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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