Pengembangan Smart Trash sebagai Media Monitoring Lingkungan Hidup Larva Lalat Tentara Hitam (Hermetia Illucens) menggunakan Metode Gaussian Naive Bayes - Mukhamad Angga Setiawan; Dahnial Syauqy; Mochammad Hannats Hanafi Ichsan (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2022)
Indonesia is one of the country with the most waste in the worlds. Most of the waste come from organic waste. One way to reduce the organic waste is using help from microorganism such as black soldier fly larvae (Hermetia Illucens). The living environment of the black soldier fly larvae greatly influences the amount of organic waste consumption, such as temperature, height of the media, humidity, and water content of organic waste. Using smart trash can help to monitoring the environment of the larvae so the condition of black soldier fly larvae is maintained well. This system using DS18B0 as temperature sensor, VL53L0X as height sensor, DHT-22 as humidity sensor, and YL-69 as moisture content are connected to the nodeMCU microcontroller. The data from system is continuous data so it must implement gaussian naive bayes first before implementing naive bayes. There are three classes of system output classification, namely â€œOptimalâ€, â€œMediumâ€, and â€œBadâ€. Sensors data and classification result are sent through the firebase server which is then sent it to the android application and displayed on the lcd screen. By using 32 training data and 17 test data, the accuracy of the gaussian naive bayes classification is 82,3%. The average computional speed of gaussian naive bayes classification is 5,2 ms by performing 10 times tests. Meanwhile, the accuracy of sending data to the firebase is 100%.