Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Mukhamad Angga Setiawan
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer

 1 
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
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 4 (2022): April 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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 “Optimal”, “Medium”, and “Bad”. 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%.
Co-Authors Dahnial Syauqy Mochammad Hannats Hanafi Ichsan