Rancang Bangun Pot Cerdas Dengan Mengatur Suhu Ruangan, Kelembapan Tanah, dan Intensitas Cahaya Berbasis Arduino dengan Metode Jaringan Saraf Tiruan Backpropagation - Indera Ulung Mahendra; Hurriyatul Fitriyah; Mochammad Hannats Hanafi Ichsan (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Indonesia is one of the countries where the majority of the population work as farmers. Over time, agricultural land decreases. This encourages people to do farming activities in the home which is often called urban farming. One commodity that is often grown is chili. The growth process of the chili plant itself has several factors that must be considered in order to grow optimally. To support the urban farming activities, an intelligent pot frame was created that could regulate the temperature within the framework, soil moisture in the planting media, and also the intensity of the light in the smart pot frame. All of these functions are supported by using a backpropagation feed-forward neural network classification method. The DHT11 sensor is used to conduct temperature readings with an average reading error rate of 2.57% compared to digital thermometers. YL69 sensor is used to read the soil moisture results from the reading of the soil moisture sensor has a pretty good accuracy compared to the reading from the hygrometer. The LDR sensor is used to read the light intensity with an average error rate of 17.62% compared to digital luxmeter. The reading value of each sensor is then entered into the classification program, where the program takes 548 milliseconds to classify after 20 tests.