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All Journal Journal of Development Research
Adhe Irham Thoriq
Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Education Social Sciences

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Classification of Banana Maturity Levels Based on Skin Image with HSI Color Space Transformation Features Using the K-NN Method Adhe Irham Thoriq; Muhamad Haris Zuhri; Purwanto Purwanto; Pujiono Pujiono; Heru Agus Santoso
Journal of Development Research Vol. 6 No. 1 (2022): Volume 6, Number 1, May 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v6i1.200

Abstract

Banana or Musa Paradisiaca is one type of fruit that is often found in Southeast Asia. The most popular is the Raja banana (Musa paradisiaca L.). The advantage of the plantain is that it has a fragrant aroma and is of medium size and has a very sweet taste that is appetizing when it is fully ripe. While the drawback of plantains is that they ripen quickly, if not handled properly, it can change the nutritional value and nutrients contained in plantains. In this study, the author focuses on identifying the level of ripeness of bananas using the image of a plantain fruit that is still intact and its skin. Processing of the image of the plantain fruit using HSI (Hue Saturation Intensity) color space transformation feature extraction. The tool used to extract the HSI (Hue Saturation Intensity) color space transformation feature is Matlab. The attribute values obtained from the extraction are the Red, Green, Blue values obtained from the RGB values. Hue, saturation and intensity attributes were obtained from HSI extraction. Classification of the level of ripeness of plantain fruit is done with the help of the rapidminer tool. The method used is K-NN. The results obtained from this test are the accuracy value of 91.33% with a standard deviation value of+/- 4.52% with a value of k=4. The RMSE value obtained is 0.276.
Classification of Guarantee Types Using Leaf Feature Extraction with Minutiae and GLCM Using K-NN Method Muhammad Haris Zuhri; Adhe Irham Thoriq; Abdul Syukur; Affandy Affandy; Muslih Muslih; Moch Arief Soeleman
Journal of Development Research Vol. 6 No. 1 (2022): Volume 6, Number 1, May 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v6i1.201

Abstract

Indonesia is a fertile area that has a sub-tropical climate that makes plants grow well in various parts of Indonesia. There are various variants of guava in Indonesia. Of the several types have differences including the structure of the fruit, tree and leaves. The focus of this research is to classify guava species based on leaf bone image using GLCM feature extraction, minutiae and shape extraction using the K-NN method. In this study using a dataset of 4 types of guava as many as 300 images, where each type of as many as 75 images. In the extraction process to get the leaf bone image in this study, there are several processes, namely preprocessing, grayscale image, binary image and morphology then only get the leaf bone image. After getting the extracted value, then the data is processed using the K-NN method. The highest accuracy in the K-NN method is at k1 = 92.42% with a standard deviation of 6.05% (micro average: 92.45%). Thus GLCM feature extraction, minutiae and shape extraction can potentially increase the level of accuracy in guava classification based on leaf bone images.
Co-Authors Abdul Syukur Affandy Affandy Heru Agus Santoso Moch Arief Soeleman Muhamad Haris Zuhri Muhammad Haris Zuhri Muslih Muslih Pujiono Pujiono Purwanto Purwanto