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Lukman Hakim
Universitas Yudharta Pasuruan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Other

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Classification of Coffee Leaf Diseases using the Convolutional Neural Network (CNN) EfficientNet Model Muhammad Imron Rosadi; Lukman Hakim; M. Faishol A.
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.627

Abstract

Coffee leaf disease is a problem that needs attention because it affects the quality and productivity of the coffee harvest and is detrimental to farmers. Therefore, a system is needed to identify types of coffee leaf diseases using artificial intelligence. There are four types of coffee leaf diseases, namely Miner leaf, Phoma leaf, Rust leaf, and Nodisease leaf. The research used the EfficientNet Architecture Convolutional Neural Network (CNN) method to detect types of disease on coffee leaves. This method was chosen because it is capable and reliable in processing digital images for pattern recognition. The dataset used is 1,464 images with dimensions of 2048 x 1024 pixels with RGB type which are divided into 1,264 training data and 400 testing data. Several architectures used in EfficientNet are EfficientNet B0, EfficientNet B1, EfficientNet B2, EfficientNet B3, EfficientNet B4. Parameters used are Lanczos resampling, Epoch 25, Learning Rate 0.0001, Loss Function Sparse Categorical Cross Entropy, Optimizer Adam. The results of training data testing, namely the CNN EfficientNet B1 Architecture Model method, got the best accuracy of 97% and a loss of 0.1328 and testing data testing got an accuracy of 0.97% and a loss of 0.1328. The architecture of the EfficientNet B1 model is better than other architectural models, namely VGG16, ResNet50, MobileNetv2, EfficientNet B0, EfficientNet B2, EfficientNet B3, EfficientNet B4, EfficientNet B5, EfficientNet B6, EfficientNet B7.
Co-Authors Achmad Afifuddin Adrianto, Dian Agus Prianto Ahfa, Mokhamad Isna Marzuki Ahwan, Zainul Ainiyah, Roisatul Aliansyah, Zulhaj Amang Fathurrohman Amelia, Resti Aminatuz Zuhriyah Aminatuz Zuhriyah As'idah, Fizriyatul Awang Rivaldo Bachtiar, Mochammad Alvin Tri Bagus Hari Sugiharto Bagus Hari Sugiharto Baroah, Hamida Maulana Lailatal Cahya Bagus Sanjaya Candra Adi Lesmana Deby Rizky Prihatini E Ernawati, E Ediyanto Ernawati Evy Kamilah Ratnasari Fafit Rahmat Aji Firda Nur Aisyah Firdaus, Lukman Fitrianawati, Meita Hapsari Titi Palupi Imam Syafi’i Imam Wahyudi Farid Irwan Abdullah Khoilil Fitria Kholid Murtadlo Lilis Trisnawati M Dayat M. Daimul Abror M. Ikmal Farih Miftahul Huda Miftahul Huda Misbach Munir Moch. Lutfi Mochamad Hasyim Mohamad Zein Saleh Muh. Aniar Hari Swasono Muhammad Fahmy Mayadi Muhammad Faishol Amrulloh Muhammad Imron Rosadi Muhammad Imron Rosadi Mulyono Wobisono Murtiasih Murtiasih Mu’awanah, Cahyatul Nabilah, Lailatun Novitasari, Tri Wahyu Nur Aisyah, Firda Nur Robiah Nofikusumawati Peni Nurus Sa'adah Okariawan, I.D.K. Prayoga, Resdi Hadi Prayoga, Resdi Hadi Rahayu, Silvia Indah Ramli Ramli Resdi Hadi Prayoga Ridha, Faishal Ananta Roisatul Ainiyah Siti Mutrofin Sugiyarto Sugiyarto Sujita, Sujita Sulhan, M. Syarwani, Muhammad Syukur Sugeng Apriwiyanto Tiara Meylinda S Trisna Seviya Widodo S Pranowo Yusebla, Agil