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All Journal Simtek : Jurnal Sistem Informasi dan Teknik Komputer Journal of Computer Networks, Architecture and High Performance Computing
Chairisni Lubis
Universitas Tarumanagara
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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KLASIFIKASI PENYAKIT KULIT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) DENGAN ARSITEKTUR VGG16 Supirman Supirman; Chairisni Lubis; Danu Yuliarto; Novario Jaya Perdana
Simtek : jurnal sistem informasi dan teknik komputer Vol. 8 No. 1 (2023): April 2023
Publisher : STMIK Catur Sakti Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51876/simtek.v8i1.217

Abstract

Pada penelitian ini, fokus utama adalah mengklasifikasikan penyakit kulit menjadi 7 jenis yaitu dermatitis, campak, herpes, psoriasis, cacar air, kurap dan kutil. Metode yang digunakan adalah deep learning Convolutional Neural Network (CNN) yang merupakan bagian dari Artificial Intelligence (AI). Deep learning merupakan ilmu berbasis jaringan saraf tiruan yang mengajarkan komputer untuk melakukan tindakan yang dianggap alami oleh manusia. Data yang digunakan dalam perancangan ini adalah data citra 7 jenis penyakit kulit yaitu cacar air, campak, dermatitis, herpes, kurap, kutil dan psoriasis. Setelah seorang pakar memeriksa keseluruhan data, jumlah yang dapat digunakan dalam melatih kedua model arsitektur VGG16 dan MobileNet dengan 1.410 data citra. Data citra penyakit kulit berbasis mobile pada epoch 100 dengan tingkat Akurasi 82,14%, Precision 83%, Recall 82% dan F1-Score 82%, metode algoritma CNN memberikan hasil yang bagus dan dapat digunakan dalam pengujian data citra klasifikasi penyakit kulit berbasis mobile
Classification of diseases in snake plants using convolutional neural network Kensa Athalia; Tiffany; Kevin Adhi Dhamma Setiawan; Bertrand Ferrari; Chairisni Lubis
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3201

Abstract

snake plant has an important role in human life, as well as in increasing the aesthetic value of the environment. Limited knowledge about diseases in snake plants has a crucial result in improper handling and control when the plant is attacked by disease. Advances in deep learning technology and Convolutional Neural Network (CNN) have presented high opportunities with their advantages in recognizing patterns and features from image data. This research will use a CNN model with VGG-19 architecture to classify diseases in the leaves of the snake plant. It is expected that by using the pre-trained VGG-19 model, the model can recognize complex visual patterns in snake plants. Diseases to be classified include several types of diseases that often attack snake plants such as anthracnose, rust, water soaked lesion, and healthy plants for comparison. The highest value of training accuracy reached a value of 98.08%, validation accuracy of 94.02%, and testing accuracy reached 94%.
Co-Authors Bertrand Ferrari Danu Yuliarto Kensa Athalia Kevin Adhi Dhamma Setiawan Novario Jaya Perdana Supirman Supirman Tiffany