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Adi, Cyprianus Kuntoro
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Computer Science & IT

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Pengenalan Daun Tanaman Obat Menggunakan Jaringan Syaraf Tiruan Backpropagation Damayanti, Maria; Adi, Cyprianus Kuntoro
MEANS (Media Informasi Analisa dan Sistem) Volume 4 Nomor 2
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (590.902 KB) | DOI: 10.54367/means.v4i2.542

Abstract

Indonesia is a country with a variety of biodiversity. One of the rich types of flora or plants is medicinal plants. Not all types of medicinal plants can be remembered by the community because people have limited memory. In addition, the many types of medicinal plants make an error in the process of introduction of medicinal plant types. This research processes leaf images using image processing. The data used in this study 189 data consisting of 7 types of medicinal plants. Feature extraction used was 21 features which included shape, texture and color. The result of feature extraction will be identified using backpropagation neural network. Classification experiments with backpropagation produce an optimal accuracy of 91%. These results are generated using data normalization and 3 fold. The architecture used is input 21 features, 40 neurons in the hidden layer 1 and 15 neurons in the hidden layer 2. The results are obtained by the trainscg function, the activation function tansig.
Klasifikasi Berbagai Jenis Jamur Layak Konsumsi dengan Metode Backpropagation Hanseliani, Ruth; Adi, Cyprianus Kuntoro
MEANS (Media Informasi Analisa dan Sistem) Volume 4 Nomor 2
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.803 KB) | DOI: 10.54367/means.v4i2.589

Abstract

Mushrooms are a potential source of vegetable food around us. But there are difficulties in recognizing different types of mushrooms worthy of consumption because they have similarities when viewed visually. This study aims to determine the accuracy of the backpropagation method in classifying various types of mushrooms suitable for consumption automatically. The steps taken begin with data capture, initial processing, feature extraction and classification. The data used are images of six types of mushrooms worthy of consumption including button mushrooms, ears, straws, portabella, shitake, and ash oysters. Total data used 222 mushroom data, each type consists of 37 image data. The data sharing is 216 data as training data and testing data, and 6 data are used for single data test. The initial processing stages are grayscalling, adjustment, binaryization, noise reduction and resizing. Feature extraction is done to obtain color characteristics (RGB and HSI) and texture characteristics (texture statistics and gray level co-occurrence matrices). The test results obtained an accuracy of 97%.
Co-Authors Damayanti, Maria Hanseliani, Ruth