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All Journal Jurnal Ilmiah Informatika Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Jurnal Transformatika Jurnas Nasional Teknologi dan Sistem Informasi E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal CoreIT JPM (Jurnal Pemberdayaan Masyarakat) Jurnal BALIRESO Dinamisia: Jurnal Pengabdian Kepada Masyarakat ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer Journal of Information System, Applied, Management, Accounting and Research Celebes Abdimas: Jurnal Pengabdian Kepada Masyarakat Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Data and Science Indian Journal of Forensic Medicine & Toxicology International Journal of Engineering, Science and Information Technology Jurnal PkM (Pengabdian kepada Masyarakat) Caraka : Indonesia Journal of Communication Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Celebes Journal of Community Services KOPEMAS Jurnal Balireso: Jurnal Pengabdian pada Masyarakat Journal of Information Technology and its Utilization Linier: Literatur Informatika dan Komputer
Dolly Indra
Universitas Muslim Indonesia
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Journal : Bulletin of Electrical Engineering and Informatics

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Classification of good and damaged rice using convolutional neural network Dolly Indra; Hadyan Mardhi Fadlillah; Kasman Kasman; Lutfi Budi Ilmawan; Harlinda Lahuddin
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3385

Abstract

Rice production is massive in Indonesia, therefore maintaining the quality of the product is necessary. Detection and classification of objects have become a very important part in image processing. We performed object detection namely rice. After the object is found, it can be classified into two categories, namely good and damaged rice. We conducted a new study on rice which was carried out per group not per grain to obtain or classify good and damaged rice where we had carried out several steps, namely segmentation process using HSV (hue, saturation, value) color space. HSV is used because of its excellence in representing brightness of the image. We considered evaluating brightness because the tendency of damaged rice is darker or paler compared to good rice. To accomodate environment lighting ambiguity we perform the image acquisition in a controlled environment, so that all the images have the same light intensity. Here we use only channel V of HSV to be used in feature extraction using the gray-level co-occurrence matrix (GLCM) and finally convolutional neural network (CNN) is used for classification. From the test experiments that we have done, we have produced 83% prediction accuracy. Considering how similar the good rice is to the spoiled rice, the results are quite impressive.
Co-Authors Abdul Rachman Manga’ Abdul Rauf Tuasikal Agung, Riski Dewa Ahmad Toni, Ahmad Alda, A. Nurul Aisya Aldri Frinaldi Amir, Nur Hikmah Andi Apryan Mallarangen andi Widya Mufila Gafar Anik Nur Handayani Anwar, Faudiah Ardhiansya Yusuf Arfan Zainuddin Armind Mauldi Kurniawan As'ad, Ihwana Astuti, Wistiani Damanhuri, Nor Salwa Daris, Mega Asfirawati Djamereng, Asdar Erick Irawadi Alwi Erick Irawadi Alwi Erick Irawadi Alwi, Erick Irawadi Erick, Erick Irawadi Alwi Fadly Achmad Farniwati Fattah Fauzy Fathrurahman Fery Setyo Aji Firman Shantya Budi, Firman Shantya Fitriyani Umar Hadyan Mardhi Fadlillah Haerdiansyah Syahnur, Muhammad Harlinda Lahuddin Hayudin Hasnanda Maila Herdianti Herdianti Darwis Herman Herman Hi. Talib, Juraiz Hidayat, Muh Wahyu Huzain Azis Ihwana As’ad Irawati Irawati Irawati Irawati Irja, Mulianty Cipta jabir, sitti rahmah Jufriadif Na`am, Jufriadif Juita Mandasari Julius Santony Kadri Rahmat Suat, Wahyu Kasman Kasman L, Harlinda Lilis Nur Hayati lilis nurhayati Lisna Ariani Lukman Syafie Lutfi Budi Ilmawan Lutfi Budi Ilmawan, Lutfi Budi Manga, Abdul Rachman Manga, Abdul Rachman Mansyur, St. Hajrah Mardiyyah Hasnawi Muh Fadlan Risqullah Dwitama N Muh Yeyen Dwi Suherman Muh. Fachrisyam Muh. Ridwan Rahim Muhammad Al Mubarak Muhammad Arfah Asis Muhammad Farhan Hermansyah Mukarramah, Rifqatul Musdalifah Musdalifah Mustika, Mustika Octavia Novanto, Achmad Nur Hayati, Lilis Nurhalima Nurhalima Prihandani, St. Nadya Kurnia Purnawansyah Purnawansyah Putri Nur Rezky Rahma, Dewi Ernita Rahmat Suat, Wahyu Kadri Rahmayani, Nurul Ramadan, Syahril Ramdan Satra Ramdaniah Ramdaniah Rezky Anugrah Rifky Saputra Scania, Muhammad Rosa Andrie Asmara Salsa, Salsabila Aurelia Saputra Scania, Muhammad Rifky Satma, Satma St. Hajrah Mansyur Subhan Ardhiman Sugiarti, Sugiarti Sukur, Widianti Syahnur, Muh. Haerdiansyah Syahrul Mubarak Abdullah Tanaka Kazuaki Tasmil Tasmil Tasrif Hasanuddin Tasrif Hasanuddin Taufik, Muhammad Asrai Tenri Sa'nah Umar Mansyur Umar, Fitriyani Veithzal Rivai Zainal Wahyu Sakti Gunawan Irianto Yuhandri Yuhandri, Yuhandri Yundari, Yundari Zahra, Andi Fathimatuz Zahra