Desi Nurnaningsih
Universitas Muhammadiyah Tangerang, Tangerang

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

Identifikasi Citra Tanaman Obat Jenis Rimpang dengan Euclidean Distance Berdasarkan Ciri Bentuk dan Tekstur Desi Nurnaningsih; Dedy Alamsyah; Arief Herdiansah; Alfry Aristo Jansen Sinlae
Building of Informatics, Technology and Science (BITS) Vol 3 No 3 (2021): Desember 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (427.459 KB) | DOI: 10.47065/bits.v3i3.1019

Abstract

In the midst of the Covid-19 pandemic, increasing the body's immunity is very important. Some experts suggest consuming medicinal plants or herbs to boost immunity. In addition to being used as a cooking spice, this rhizome type plant turns out to have properties and benefits for health, especially to increase immunity. However, many people do not know and it is difficult to distinguish the type of rhizome plant. This type of rhizome plant can be identified based on the characteristics seen from the shape and texture. However, most people judge the type of rhizome has a shape that is difficult to distinguish. This study aims to determine the type of medicinal plant rhizome with Euclidean distance and extraction of shape and texture. Extraction of shape features using metric and eccentricity parameters. This parameter is considered to be able to recognize shape objects and can distinguish them from other objects. Meanwhile, texture feature extraction uses Gray Level Co-occurence Matrix (GLCM) with contrast, correlation, energy, and homogeneity parameters. For the identification process, Euclidean distance is used which serves to represent the level of two images that consider the distance value from Euclidean. From the results of the evaluation using a confusion matrix by calculating precision, recall and accuracy, it gets a precision value of 83%, recal 87% and an accuracy of 85%. These results indicate that the Euclidean distance and extraction of shape and texture features can identify the object image of medicinal plants with rhizome types well
Implementasi Metode Linear Discriminant Analysis (LDA) Pada Klasifikasi Tingkat Kematangan Buah Nanas Rachmat Destriana; Desi Nurnaningsih; Dedy Alamsyah; Alfry Aristo Jansen Sinlae
Building of Informatics, Technology and Science (BITS) Vol 3 No 1 (2021): Juni 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (588.903 KB) | DOI: 10.47065/bits.v3i1.1007

Abstract

Pineapple is a fruit commodity that is Indonesia's flagship. This is because pineapple is a fruit that has the highest export volume in Indonesia. To obtain pineapples with perfect ripeness, generally manually selected, this becomes inefficient if large numbers of pineapples are selected. So, in this study, an image processing system will be developed that can classify pineapple ripeness based on its image. In this study, the color feature extraction used is feature extraction based on hue and saturation values. Color feature extraction with hue and saturation is used to obtain various information from the colors in the image so as to facilitate the identification process. Furthermore, Linear Discriminator Analysis will obtain optimal projections to be able to enter spaces with smaller dimensions by performing pattern recognition that can be separated so that they can be grouped based on boundary lines obtained from linear equations. Based on the results of the accuracy test, the accuracy rate reaches 83%, it is in the good category
Klasifikasi Citra Daun Herbal Dengan Menggunakan Backpropagation Neural Networks Berdasarkan Ekstraksi Ciri Bentuk Arief Herdiansah; Rohmat Indra Borman; Desi Nurnaningsih; Alfry Aristo J Sinlae; Rosyid Ridlo Al Hakim
JURIKOM (Jurnal Riset Komputer) Vol 9, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i2.4066

Abstract

Since ancient times until now herbal plants have been used for treatment and have been applied in the world of health to this day. All parts of the plant can be used as medicine, one of which is the leaves. However, there are still many people who are not familiar with the medicinal leaves. This is because the leaves at first glance look almost the same, making it difficult to tell them apart. Actually, if you look closely, the leaves have characteristics that can be distinguished from one leaf to another. The purpose of this study is to classify images of herbal leaf species using the Backpropagation Neural Network (BNN) algorithm with shape feature extraction utilizing metric and eccentricity parameters. BNN is a type of supervised learning algorithm that consists of several layers and uses an error output as a modifier of the weight value backwards. In this study, the extraction of shape features that become input for the BNN algorithm will go through morphological operations to improve the segmentation results so that the classification results are more optimal. The test results show an accuracy of 88.75%, this shows the developed model can classify herbal leaves well