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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Nasibah Husna Mohd Kadir
Universiti Teknologi MARA

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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.
Comparison of convolutional neural network and bag of features for multi-font digit recognition Nasibah Husna Mohd Kadir; Sharifah Nur Syafiqah Mohd Nur Hidayah; Norasiah Mohammad; Zaidah Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i3.pp1322-1328

Abstract

This paper evaluates the recognition performance of Convolutional Neural Network (CNN) and Bag of Features (BoF) for multiple font digit recognition. Font digit recognition is part of character recognition that is used to translate images from many document-input tasks such as handwritten, typewritten and printed text.  BoF is a popular machine learning method while CNN is a popular deep learning method.  Experiments were performed by applying BoF with Speeded-up Robust Feature (SURF) and Support Vector Machine (SVM) classifier and compared with CNN on Chars74K dataset. The recognition accuracy produced by BoF is just slightly lower than CNN where the accuracy of CNN is 0.96 while the accuracy of BoF is 0.94.
Co-Authors Norasiah Mohammad Sharifah Aliman Sharifah Nur Syafiqah Mohd Nur Hidayah Zaidah Ibrahim