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Mimi Hamidah
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Implementasi Decision Tree pada Penentuan Kondisi Ruang Berasap Menggunakan Multi-Sensor Berbasis Arduino Uno Mimi Hamidah; Hurriyatul Fitriyah; Issa Arwani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In this day and age many facilities are designed automatically to help human activities in regulating the level of comfort and safety in the room, one of the technologies that has been widely used, namely fire alarms that are used to provide automatic warnings about fires that occur. However, due to several reasons and certain factors, often the fire alarm does not work properly and actually sends a false alarm. In this study there are 3 sensors, namely MQ-2 sensor, DHT22 sensor, and flame sensor that is connected to the Arduino Uno microcontroller. Arduino Uno microcontroller implements the decision tree method as the output decision maker based on the calculation of C4.5. There are 3 processes, namely the process of determining datasets, decision tree formation and rule formation. In this system, there are 3 attributes that are used to detect the status of smoky space conditions, namely temperature, fire intensity and smoke content. From the results of several tests conducted, it is known that the error percentage reading of the DHT22 temperature sensor is 1.58% and the MQ22 gas sensor can read the gas content in the room well, where the sensor reading value is directly proportional to the output voltage which is the higher the smoke level detected the higher the value of the sensor output voltage. From the results of testing the fire sensor YG1006 can perform ADC readings detected by the sensor against the fire source based on the distance of the sensor with the fire source. Furthermore, in testing the system using the Decision Tree method with the amount of training data as many as 800 data and test data as many as 40 data, obtained an accuracy of 97%. The average system execution time is ± 1389.9 ms
Co-Authors Fitriyah, Hurriyatul Issa Arwani