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Asnidar Asnidar
Universitas Negeri Medan
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
CLASSIFICATION OF RICE QUALITY LEVELS BASED ON COLOR AND SHAPE FEATURES USING ARTIFICIAL NEURAL NETWORK BASED ON DIGITAL IMAGE PROCESSING Asnidar, Asnidar; Perdana, Am Akbar Mabrur; Ilham, Muhammad Ryan; Kaswar, Andi Baso; Andayani, Dyah Darma
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.734

Abstract

Rice is the staple food of most Indonesians. In identifying the quality of rice, it can be seen from physical characteristics such as the color and shape of rice, because these characteristics can make an object can be identified properly and clearly. In general, what is done in determining the quality of rice by looking at its color and shape. But usually the human eye in identifying objects is sometimes less accurate which is influenced by several factors, such as age. So, several studies were conducted that tried to solve the problem by using digital image processing. However, the accuracy results obtained are still not accurate, because the datasets used in the previous study were relatively small, namely around 80 images, although the average level of accuracy obtained was quite high, but the number of datasets used was very small so that the level of accuracy was still inaccurate. Therefore, in this study, it is proposed that the title of classification of rice quality levels using JST based on digital image processing which divides rice into 3 classifications, namely, good, good enough, and not good where in this study using 330 digital images to produce a more accurate level of accuracy. In this study, there are several stages, namely, image retrieval, preprocessing, segmentation, morphological, feature extraction, and classification using artificial neural networks. Based on the research conducted, training accuracy was produced with an average accuracy of 98,75% while the test accuracy was produced with an average accuracy of 98,89%.
Co-Authors A.A. Ketut Agung Cahyawan W Achmad R. Muttaqien Al-Maidin Ahmad Ridha Ahmad Ridha Ahmad, Arfandi Akbar Akbar Alamsyah - Alber, Alber Ali Nurman Alwi, Muhammad Khidri Amirullah, Sufyan Andi Alim, Andi Andi Baso Kaswar Andi Baso Kaswar Andi Sukainah Andiny, Puti AP, Andi Rizki Amelia Aprillia, Sri Rahayu Arfanti, Yulia Arni Rizqiani Rusydi Arwan, Yayudia Setriawati M. Asnawi Asnawi Asnidar Asrul Fole Asyura, Asyura Aulia , Iin Chaira, Teuku Muhammad Iqbal Desinta, Runy Eka Safitri Sayuti, Ella Erni Erni Faisalrahmat, Muh. Fajriah, Agustina Nurul Farida Catur Wahyu Anggriyani, Farida Catur Wahyu Halimatus Sakdiah, Halimatus Hanum, Nurlaila Hardianti Manik Hidayani, Hidayani hidayatullah, Hidayatullah hidayatullah Ilham, Muhammad Ryan Ilyas, Herlina Isdaryanti Isdaryanti Isdaryanti, Isdaryanti Iskandar Iskandar Islawati Ismanidar, Nur Khusnul Fahima, Aufiq Kumalasari, Reni Kurniadewi, Meinasari Lahming, Lahming Lilis Handayani Mardiani, Risti Mukhlis, Muhammad Murni, Mayang Mustikawati, Deswy Nirwan Ilyas Novita Sari Novitasari, Eni Nur Rahmah Nurlaila Hanum Nurlina Nurlina Nurmala Berutu Perangin-Angin, Mochammad Iswan Perdana, Am Akbar Mabrur Priyadi Priyadi Puspa Sari Aceh, Adinda Qalbi, Aulia Rahayu, Julia Rahma, Fathia Rahmah, Nur Rahmaniah Malik Raupong, Raupong Renilda Ridwan , Asriyani Rizky Nafaida Romansyah Sahabuddin Rufaida, Erty Rospyana Safriana Safriana Safuridar Saifuddin Muhammad Jalil Salami Mahmud Samsualam, Samsualam Sari, Sri Wulan Sawiyah, Mudiyatun Setriawati, Yayudia Shomary, Sudirman Sri Suyanta Sutarsa, I Ketut Suyadi Suyadi Syahminan Syahminan Syahri Ramdhaniah, Anggun Syarifah Rita Zahara Tambun, Laila Hasyim Tengku Putri Lindung Bulan, Tengku Putri Lindung Widiastuti, Intan Widiyanti, Tri Yuliawan, Tri Zahrawati, Zahrawati Zuhairah, Ismi Zulkarnen Mora, Zulkarnen