CLASSIFICATION OF RICE ELIGIBILITY BASED ON INTACT AND NON-INTACT RICE SHAPES USING YOLO V8-BASED CNN ALGORITHM

Nazwa Putri Hastari; Tatang Rohana; Anis Fitri Nur Masruriyah; Deden Wahiddin

doi:10.52436/1.jutif.2024.5.5.2413

Nazwa Putri Hastari Faculty of Computer Science, Informatics Engineering Study Program, Universitas Buana Perjuangan Karawang, Indonesia
Tatang Rohana Faculty of Computer Science, Informatics Engineering Study Program, Universitas Buana Perjuangan Karawang, Indonesia
Anis Fitri Nur Masruriyah Faculty of Computer Science, Informatics Engineering Study Program, Universitas Buana Perjuangan Karawang, Indonesia
Deden Wahiddin Faculty of Computer Science, Informatics Engineering Study Program, Universitas Buana Perjuangan Karawang, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.5.2413

Keywords: Social Assistance, Rice, classification, YoloV8

Abstract

The large amount of unfit rice has an impact on the quality of rice provided to the community. This is due to the lack of supervision of the quality of existing rice, so that the quality of rice distributed to the community has a lot of unfit quality. Rice production for public consumption reached 21.69 million tons in 2021, according to data from the Central Statistics Agency (BPS). Rice is the main food of the Indonesian people because most Indonesians are farmers and the vast amount of agricultural land makes Indonesia one of the largest rice producing countries in Southeast Asia, this has a huge impact on people's habits in consuming rice as the main food provider. The Government of the Republic of Indonesia started a Social Assistance rice distribution program through the Ministry of Social Affairs in 2018. This program is named Prosperous Rice Social Assistance (Bansos Rastra). Classification of rice eligibility can be the first step to ensure that the rice received from the government is of high quality and can meet the daily needs of households in Indonesia. CNN algorithm based on YOLOv8 system can automatically recognize the form of rice given by the government whether it is feasible or not. In the research stages there are dataset collection, preprocessing, training models to evaluation. Based on the results obtained in this study, the accuracy achieved is 79% for the Eligible class and 79% for the Ineligible class with Confidence score reaching a value of 1.00. The results of this study can be used as a decent and unfit rice classification detection model by looking at the shape of the rice. So that the rice distributed to the community has decent rice quality.

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