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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi Edu Komputika Journal Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Computatio : Journal of Computer Science and Information Systems JTERA (Jurnal Teknologi Rekayasa) Jurnal Sains dan Informatika INOVTEK Polbeng - Seri Informatika Matrix : Jurnal Manajemen Teknologi dan Informatika SINTECH (Science and Information Technology) Journal Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JTT (Jurnal Teknologi Terapan) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JISKa (Jurnal Informatika Sunan Kalijaga) JTIM : Jurnal Teknologi Informasi dan Multimedia Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) JISA (Jurnal Informatika dan Sains) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Indonesia : Manajemen Informatika dan Komunikasi SmartComp Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Donny Avianto
Universitas Teknologi Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering

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Journal : SINTECH (Science and Information Technology) Journal

 1 
Klasifikasi Penyakit Antraknosa Pada Cabai Merah Teropong ”Inko Hot” Dengan Metode Convolutional Neural Network Donny Avianto; Ilmy Eka Handayani
SINTECH (Science and Information Technology) Journal Vol. 6 No. 2 (2023): SINTECH Journal Edition Agustus 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i2.1377

Abstract

The red chili variety "inko hot" is a type of red chili that has a high economic value. Unfortunately, these red chili plants are often infected with anthracnose disease, which results in significant losses for farmers. Anthracnose is one of the major diseases infecting chili plants, potentially resulting in crop failure and losses of up to 80%. The purpose of this study is to develop a classification system to identify anthracnose disease in red chili fruit, using Convolutional Neural Network (CNN) method. In this experiment, 1500 data were used, of which 80% were used as training data and 20% as validation data. The best results of this experiment produced a model with an accuracy of 97% and a loss rate of 6.45%, by applying the Nadam optimization algorithm and going through 50 iterations (epochs). The model showed good performance with a prediction accuracy rate of 83.33%. The development of this classification system has significant potential in providing efficient solutions to recognize diseases in chili plants. Through continuous development, this system can be a valuable tool for farmers to increase crop productivity and reduce the negative impact of disease attacks on red chili peppers and other crops.
Co-Authors Adityo Permana Wibowo Alfin Syarifuddin Syahab Amalia Rizki Wulandari Aribowo Aribowo Arief Hermawan Arieska Restu Harpian Dwika Aziz Perdana Baiq Nurul Azmi Bowo Hirwono Dwi Ratnawati, Dwi Edi Priyanto Ferdinandus Edwin Penalun Herdy Andriksen Ilmy Eka Handayani Imantoko Imantoko Kusumastuti, Asriana Dyah Muh Arifandi Muhammad Irsyad Indra Fata Nasmah Nur Amiroh Nazar Iqbal Bimantoro Nur Widiastuti Oktavia Hardiyantari Panji Rangga Adzan Fajar Fakharudin Rian Oktafiani Rianto Rianto Rizky Samudra Falasyfa Roy Fasti Rubangi Rubangi Rudi, Rudiono Rudiono Rudi Rusma Eko Fiddy Rizarta Siti Nurazila Soraya Fatmawati Tri Widodo Vivianti