SOFTWARE DESIGN AND IMPLEMENTATION OF RICE QUALITY IDENTIFICATION SYSTEM

• Main Author: Fadhilah Fithriah, Naila
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/51050
• Institution: Institut Teknologi Bandung

According to data from the Indonesian Ministry of Agriculture, rice is the staple food for more than 95% of Indonesia's population. However, based on data from the Central Bureau of Statistics, the quality of rice produced in Indonesia is still not good enough and does not match the price offered, so the rice produced by Indonesia is less competitive at the global level. So far, most of the quality of rice in Indonesia is still detected by physiological and physicochemical methods. This method was unable to determine some of the rice quality factors which had a direct relationship with the characteristics of the rice DNA. This can hamper rice production, especially for good quality rice. Therefore, the use of biomolecular technology developments to identify the quality of rice at the genetic level needs to be applied in Indonesia. Based on the description above, a solution is proposed in the form of a system called "DNA Detection System for Rice Quality Identifier" or RIKUIDEN. One part of RIKUIDEN is a device that performs the DNA identification process. The software system of this tool is made on the Raspberry Pi 4 B + Mini PC and consists of several main subsystems, namely the User Interface subsystem, Image Acquisition and Storage subsystem, and Image Processing subsystem. The user interface subsystem is implemented using a local server web application with HTML / CSS and Javascript for front-end side and Python / Flask for back-end side. On the back-end side, this device is connected to the high voltage converter and UV illuminator so that the user can provide input for these two devices through the interface. Image acquisition and storage subsystem is implemented through the user interfice subsystem by utilizing the built-in Javascript library. Meanwhile, the image processing subsystem is implemented in Python to identify DNA in electrophoretic images. In general, functional testing for each software systems on the identification device has been successfully carried out.