Penentuan Kualitas Beras Menggunakan Sensor Gas Dan Neural Network

Rasyidi, Hifzhan (2024) Penentuan Kualitas Beras Menggunakan Sensor Gas Dan Neural Network. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Beras merupakan sumber karbohidrat utama bagi penduduk Indonesia. Namun, penyimpanan beras di gudang atau lumbung masih belum termonitor dengan baik. Berita mengenai ribuan ton beras yang busuk dalam gudang sering ditemukan. Karena itu, pada studi ini telah mengembangkan alat untuk memonitor dan menentukan kualitas beras dalam tempat penyimpanan. Kualitas beras dapat diprediksi melalui kadar CO2 yang diukur oleh sensor gas CCS811. Mikrokontroler Arduino Uno mengambil data dari sensor gas melalui komunikasi I2C dan mengirimnya ke komputer. Penentuan kualitas beras ditangani oleh algoritme neural network. Hasil percobaan menunjukkan bahwa sistem ini dapat mengenali kualitas beras yang meliputi baik, sedang dan rusak dengan akurasi sebesar 90,91%. Sistem ini diharapkan dapat mengurangi jumlah beras yang telah membusuk dalam gudang
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Rice is the main source of carbohydrates for the Indonesian population. However, rice storage in warehouses or granaries is still not properly monitored. News about thousands of tons of rotten rice in warehouses is often found. Therefore, this study has developed a tool to monitor and determine the quality of rice in storage. Rice quality can be predicted through CO2 levels measured by the CCS811 gas sensor. The Arduino Uno microcontroller takes data from the gas sensor via I2C communication and sends it to the computer. Determining rice quality is handled by a neural network algorithm. Experimental results show that this system can recognize rice quality including good, medium and damaged with an accuracy of 90.91%. This system is expected to reduce the amount of rice that has rotted in the warehouse

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Kualitas Beras, Mikrokontroler Arduino Uno, Neural Network, Sensor Gas; Rice Quality, Arduino Uno Microcontroller, Neural Network, Gas Sensor
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7871.674 Detectors. Sensors T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7882.P3 Pattern recognition systems
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Hifzhan Rasyidi
Date Deposited:	01 Feb 2024 07:49
Last Modified:	01 Feb 2024 07:49
URI:	http://repository.its.ac.id/id/eprint/105886

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