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All Journal TEKNIK INFORMATIKA REKAYASA Jurnal Teknologi Elektro ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) Elektrika CYCLOTRON Multitek Indonesia : Jurnal Ilmiah Jurnal Teknik Elektro dan Komputer TRIAC Jambura Journal of Electrical and Electronics Engineering Journal of Robotics and Control (JRC) Jurnal Riset Rekayasa Elektro INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) Journal Zetroem Among : Jurnal Pengabdian Masyarakat JASEE Journal of Application and Science on Electrical Engineering JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Jurnal Fortech SinarFe7 SCIENCE ELECTRO Electrician : Jurnal Rekayasa dan Teknologi Elektro JEECS (Journal of Electrical Engineering and Computer Sciences) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal INFOTEL Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal FORTECH
Achmad Fiqhi Ibadillah
Universitas Trunojoyo Madura
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Pengolahan Citra Digital Dalam Penentuan Panen Jamur Tiram Dedy Ega Saputra; Achmad Fiqhi Ibadillah
Jurnal Teknik Elektro dan Komputer TRIAC Vol 6, No 1 (2019): Mei 2019
Publisher : Jurusan Teknik Elektro Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/triac.v6i1.4356

Abstract

Saat  ini teknologi telah berkembang sangat pesat dan hal itu mulai memodernisasi beberapa bidang kegiatan manusia di era ini. Bidang pertanian pun tak luput dari perkembangan teknologi untuk hal penelitian. Deteksi objek merupakan salah satu teknologi yang terus dikembangkan dan diteliti hingga saat ini. Pada Tugas Akhir ini akan dibahas  Pengolahan Citra Digital Dalam Penentuan Panen Jamur Tiram. Dalam penelitian ini digunakan metode deteksi tepi Canny dan kontur. Hasil penelitian ini akan didapat data penentuan panen dan kualitas jamur tiram. Kata Kunci : teknologi, deteksi, jamur tiram, Canny.
Detection of Rice Diseases: Leaf Blast, Bacterial Leaf Light, and Brown Spot Using Image Enhancement and Faster Region-Based Convolutional Neural Network Fahmi, Monika Faswia; Laksono, Deni Tri; Ibadillah, Achmad Fiqhi; Laksono, Dedi Tri
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.287

Abstract

Rice diseases such as leaf blight, blast, and brown spot remain major constraints on food security and rural livelihoods across Southeast Asia, causing significant yield losses each year. In Indonesia, particularly in Lamongan, East Java, these pathogens threaten smallholder productivity and disrupt national rice supply chains. This study aims to enhance automated rice disease detection under real agricultural conditions by integrating image preprocessing techniques with a deep learning-based detection framework. The main contribution lies in developing a hybrid pipeline that combines RGB-to-grayscale conversion and contrast stretching prior to model training, effectively mitigating low-contrast conditions and noise commonly found in field-acquired image datasets. The enhanced images are subsequently processed using the Faster Region-Based Convolutional Neural Network (Faster R-CNN) with a ResNet-50 backbone to localize and classify disease symptoms. Experiments conducted on a dataset of 1,500 annotated rice leaf images achieved high detection performance, with accuracies of 97.37% for leaf blight, 94.12% for blast, and 95.24% for brown spot. Compared with the baseline Faster R-CNN model, the proposed approach improved classification accuracy from 0.8906 to 0.9297, reduced false negatives from 0.439 to 0.1998, increased foreground classification accuracy from 0.55 to 0.78, and descreased total loss from 0.839 to 0.6493. These results demonstrate that integrating RGB-to-grayscale conversion and contrast stretching significantly enhances feature representation, leading to improved detection accuracy, reduced error rates, and more stable training behavior. Overall, the proposed framework provides a robust and reliable approach for rice disease identification and offers strong potential for practical deployment in precision agriculture systems.
Co-Authors - Haryanto 5Deni Tri Laksono A.A. Ketut Agung Cahyawan W Abd. Wahid Sholeh Abd. Wahid Sholeh Achmad Ubaidillah Achmad Ubaidillah Achmad Ubaidillah MS Achmad Ubaidillah Ms Achmad Zain Nur Adi Kurniawan Adi Kurniawan Saputro Adi Kurniawan Saputro ADI SAPUTRO Aditya Prayoga Ahmad Chaerul Al’ulla Amira Rohadatul Aisy Andre Putra Pratama Anugrah Rachmat Danu Anugrah Rachmat Danu Aries Prianto Ayu Dian Lestari Candra Arif Kurniawan Dafid, Ach Dedi Tri Laksono, Dedi Tri Dedy Ega Saputra Deni Tri Laksono Deni Tri Laksono Desi Anis Anggraini Dian Neipa Purnamasari Diana Rahmawati Diana Rahmawati Diana Rahmawati Diputra, Hamzah Arifianto Dwiky Ariyanto, Kemas Efendi, Akhmad Fahmi, Monika Faswia Faikul Umam Fajar Sidik, Rahmad Farkhan, M. Faswia Fahmi, Monika Febriana, Iftitah Febrianto Hadi Kusuma Firly Abdillah, Fauzan Hadi Kusuma, Febrianto Hanif Pradipta Hanifuddin Sukri Hardiwansyah, Muttaqin Haryanto Haryanto Haryanto Haryanto Haryanto Haryanto Haryanto Hendra Wahyu Aprilyanto Hirvy Nurul Anwar ilyasa, yois Balian Irfan Irianto Ivan Dwi Cahyo Karina Wulandari Khotibul Umam Koko Joni Kunjo Aji Wibisono Kunto Aji Wibisono Kunto Aji Wibisono Kunto Aji Wibowo KURIAWAN, ADI Kurniawan Saputro, Adi Kurniawan, Denni KUSUMA, M.KURNIAWAN HADI Lailatul Riski, Ulfa Laksono, Deni Tri luqman hardianto M. Iqbal Arfiansyah MASLIKAH, SITI Maulana, Ahmad Afan Maulina Safitri MIFTACHUL ULUM Miftachul Ulum, Miftachul Mirza Pramudia Mitfatchul Ulum Moch Fadlian Rasyid Mohammad Dwi Cahyo Mohammad Edi Santoso Mohammad Faizin Zaini Monika Faswia Fahmi Morshed, Md. Monzur Muhammad , Dian Purnomo Muhammad Bahriyan Firdaus Muhammad Mukhlis Febriansyah Nur Muhammad Rinaldi Muttaqin Hardiwansyah Ogik Saputra, Khoirul pradipta, Hanif Prianto, Aries Rasyid, Moch Fadlian Riza Alfita Riza Alfita Riza Alfita Riza Alfita Riza Alfita Riza Alfita, ROSIDA VIVIN NAHARI S. Ida Kholida Saputra, Dedy Ega Sholeh Abdullah, Wahyu Siti Maslikah Siti Maslikah Siti Maslikah Sukri, Hanifudin Syafik Syafik Tania Octavriana Thinakaran, Rajermani Tri Laksono, Deni Tri Sakti Sutrisno Ubaidillah Ms, Achmad Ubaidillah, Achmad Ulum, Miftachul Vivin Nahari Vivin Nahari Vivin Nahari, Vivin Nahari Wibisono, Kunto Aji Yois Balian Ilyasa Zainal Abidin Zainal Abidin