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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Sharifah Aliman
Universiti Teknologi MARA
Electrical & Electronics Engineering

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Herbal plant recognition using deep convolutional neural network Izwan Asraf Md Zin; Zaidah Ibrahim; Dino Isa; Sharifah Aliman; Nurbaity Sabri; Nur Nabilah Abu Mangshor
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (552.13 KB) | DOI: 10.11591/eei.v9i5.2250

Abstract

This paper investigates the application of deep convolutional neural network (CNN) for herbal plant recognition through leaf identification. Traditional plant identification is often time-consuming due to varieties as well as similarities possessed within the plant species. This study shows that a deep CNN model can be created and enhanced using multiple parameters to boost recognition accuracy performance. This study also shows the significant effects of the multi-layer model on small sample sizes to achieve reasonable performance. Furthermore, data augmentation provides more significant benefits on the overall performance. Simple augmentations such as resize, flip and rotate will increase accuracy significantly by creating invariance and preventing the model from learning irrelevant features. A new dataset of the leaves of various herbal plants found in Malaysia has been constructed and the experimental results achieved 99% accuracy.
Text analysis on health product reviews using r approach Nasibah Husna Mohd Kadir; Sharifah Aliman
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i3.pp1303-1310

Abstract

In the social media, product reviews contain of text, emoticon, numbers and symbols that hard to identify the text summarization. Text analytics is one of the key techniques in exploring the unstructured data. The purpose of this study is solving the unstructured data by sort and summarizes the review data through a Web-Based Text Analytics using R approach. According to the comparative table between studies in Natural Language Processing (NLP) features, it was observed that Web-Based Text Analytics using R approach can analyze the unstructured data by using the data processing package in R. It combines all the NLP features in the menu part of the text analytics process in steps and it is labeled to make it easier for users to view all the text summarization. This study uses health product review from Shaklee as the data set. The proposed approach shows the acceptable performance in terms of system features execution compared with the baseline model system.
Co-Authors Dino Isa Izwan Asraf Md Zin Nasibah Husna Mohd Kadir Nur Nabilah Abu Mangshor Nurbaity Sabri Zaidah Ibrahim