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Fitri Nur Masruriyah, Anis
Universitas Buana Perjuangan Karawang
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
CLASSIFICATION OF RICE ELIGIBILITY BASED ON INTACT AND NON-INTACT RICE SHAPES USING YOLO V8-BASED CNN ALGORITHM Hastari, Nazwa Putri; Rohana, Tatang; Masruriyah, Anis Fitri Nur; Wahiddin, Deden
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2413

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.
Co-Authors AA Sudharmawan, AA Ahmad Fauzi Ajie, Prasetyo Alvionika, Nindy Amril Siregar ANANDA, MUHAMMAD DWI Anton Junior Anton Romadoni Junior April Lia Hananto Arief Wibowo ARIF, SITI NOVIANTI NURAINI Asep Maulana, Asep Awal, Elsa Elvira dasmadi, dasmadi Deden Wahiddin Deny Maulana Dexi Triadinda Dwi Vina Wijaya Elsa Elvira Awal Elsa Elvira Awal Emilia Sukmawati, Cici Emilia Sukmawati Faisal, Sutan Hanny Handayani Hanny Hikmayanti Handayani Hastari, Nazwa Putri Indra, Jamaludin Juwita, Ayu Ratna MALIK, KARENINA NURMELITA Maman Suherman Mardiah Mardiah Mudzakir, Tohirin Al Muhammad Cesar Afriansyah Arief Nita, yustina Novita, Hilda Yulia Nurmayanti, Trisya Permana, Tedi Prasetyo Ajie Prasetyo, Rizky Tito Rahmat Hidayat Rahmat Rahmat RAMADHAN, ANGGA RAMDA Risky Pratama, Adi Riyandi Aditya Fitrah Rohana, Tatang Ryan Gusti Nugraha Selly Rahmawati Septiany, Eva Senia Siregar, Amril Mutoi Siti Robiah Sukmawati, Cici Emilia Syahrul Azis Tania Puspa Rahayu Sanjaya Tejayanda, Rigger Damaiarta Tiana, Ade Hikma Tri Denda Ulfa Amelia Wahiddin, Deden Widiharto, Banani Yana Cahyana Yogi Alfiansah Yogi Firman Alfiansyah Yulistiawan, Bambang Saras