PENGIRIMAN DATA SENSOR SUHU DAN ASAP MENGGUNAKAN LONG RANGE (LoRa)

Abstract

Lo-Ra (Long Range) is a process of changing a certain periodic wave so that it becomes a signal capable of carrying information. LoRa technology offers a very attractive long term. Such as using low power and data transfer that is guaranteed security. Public networks that use LoRa networks will be able to provide a wider signal coverage. Compared to cellular networks in general, LoRa is a network that covers a wider range. Besides that, it is also easy to fit into the needs we need. This need is like any other electronic device that uses wireless installation. The making of this tool is a fire detector to reduce the impact of forest fires and has advantages over other tools, namely that many fire detection tools used do not meet criteria such as sending information that cannot reach long distances so that a tool that can solve this problem appears, namely Long Range ( LoRa), LoRa can anticipate lost GSM signal coverage. The purpose of this research is to detect and monitor the level of vulnerability to fire and provide information to officers in real time which will be displayed on the blynk. The results of this study obtained that this tool is able to detect forest fires up to a distance of 700 meters, if it exceeds that distance the reading will be error, this tool detects forest fires by reading the three sensors, if the three sensors have reached the threshold point, namely with a temperature sensor greater than 40 degrees Celsius, the humidity sensor value is less than 61%, and the smoke sensor value exceeds 2670 ppm, the tool will give a fire warning that will appear on the Blynk notification. at a distance of up to 700 m, the average delay for node 1 reaches 23.2 seconds and the average delay for node 2 reaches 54.5 seconds. And it can also be seen that the average percentage of temperature error obtained at node 1 reaches 4%, and the average error in humidity for node 1 reaches 6.4%, then the average percentage error temperature obtained at node 2 reaches 8.5%, and the average error at node 2 humidity reaches 18%.