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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Rekayasa: Jurnal Penerapan Teknologi dan Pembelajaran SemanTIK : Teknik Informasi INTECOMS: Journal of Information Technology and Computer Science JURNAL PENDIDIKAN TAMBUSAI J-SAKTI (Jurnal Sains Komputer dan Informatika) Juriti Prima (Jurnal Ilmiah Teknik Industri Prima) JTIM : Jurnal Teknologi Informasi dan Multimedia JISA (Jurnal Informatika dan Sains) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Algoritma Jurnal Pemberdayaan Masyarakat Jurnal Indonesia Sosial Teknologi Indonesian Journal of Applied Technology Jurnal Indonesia : Manajemen Informatika dan Komunikasi Ulil Albab
Sutopo, Joko
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Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences

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Journal : J-SAKTI (Jurnal Sains Komputer dan Informatika)

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Pemanfaatan Artificial Neural Network Terhadap Klasifikasi Penyakit Tanaman Padi Menggunakan Citra Daun Sunandar, Nandi; Sutopo, Joko
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 8, No 1 (2024): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v8i1.761

Abstract

Rice is a type of plant that is very essential for humans. Rice is used as a staple food by a large portion of people, especially in Asia. Therefore, proper care and management of rice plants are crucial to maximize crop yields and meet the food needs in Indonesia, particularly for the staple diet. One of the common factors that hinder rice production is diseases that affect rice plants. If left untreated, it can have a detrimental impact on the much-needed rice production in Indonesia. Some common rice diseases include Bacterial Leaf Blight, Brown Spot, and Leaf Smut. Hence, early detection is required as a preventive measure against these diseases. One way to address this issue is by applying artificial intelligence, where AI can play a significant role in disease identification in rice plants. This allows farmers to take clear actions in disease management. Identification is performed using leaf images and an Artificial Neural Network (ANN) algorithm with an accuracy of 83%.
Co-Authors Abimanyu, Silvan Azindha, Fillah Syakur Charles De Haan Erwan Bali Loko, Hermanus Fadillah, Naufal Syafiq Faizy, Akmal Hartono, Ardan Dwi Hidayati, Anindya Nurindah Ichsan, Muhammad Ichsan Asy'ari Islamey, Daffa Sholah Juhansar Andi Latief Kuswantoro, RB. Hendri Mustaqim Pabbajah Nugroho, Agung Yuliyanto Nurahdika, Ni Putu Irene Pasca Nuraini, Fina Oktafiana, Oni Pramono, Owen Denpas Prasetio, Sixtus Albern Joand Rachman, Annisa Rasyid, Guilliano Santoso, Muhamad Agung Sinaga, Thomas Sunandar, Nandi Sunardi Sunardi Supriyo, Sarwo Susilo, Tarsisius Syamaidzar, Fata Nurlaudza' 'aza Syukor Mohamad Jaya, Abdul Taalungan, Anggita Taufiq Rohman, Ahmad Wahyanti, C. Murni Wisnu, Aditya Indra Zaman, Komaruz