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Nfn Bahrawi
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Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based Nfn Bahrawi
Journal of Information Technology and Its Utilization Vol 2, No 2 (2019)
Publisher : Sekolah Tinggi Multi Media (STMM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30818/jitu.2.2.2695

Abstract

Every day billions of data in the form of text flood the internet be it sourced from forums, blogs, social media, or review sites. With the help of sentiment analysis, previously unstructured data can be transformed into more structured data and make this data important information. The data can describe opinions / sentiments from the public, about products, brands, community services, services, politics, or other topics. Sentiment analysis is one of the fields of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions in text form. At the most basic level, the goal is to get emotions or 'feelings' from a collection of texts or sentences. The field of sentiment analysis, or also called 'opinion mining', always involves some form of data mining process to get the text that will later be carried out the learning process in the mechine learning that will be built. this study conducts a sentimental analysis with data sources from Twitter using the Random Forest algorithm approach, we will measure the evaluation results of the algorithm we use in this study. The accuracy of measurements in this study, around 75%. the model is good enough. but we suggest trying other algorithms in further research. 
UTILIZATION OF SMARTPHONE DEVICES AND USE OF SOCIAL MEDIA IN NORTH MALUKU nfn Bahrawi
Journal of Information Technology and Its Utilization Vol 1 No 1 (2018)
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30818/jitu.1.1.1655

Abstract

Basic guidelines. This document is itself an example of the desired layout (inclusive of this abstract) and can be used as a template. It contains information regarding desktop publishing format, type sizes, and typefaces. Style rules are provided that explains how to handle equations, units, figures, tables, abbreviations, and acronyms. Sections are also devoted to the preparation of acknowledgments, references, and authors' biographies. The abstract is limited to 150–200 words and cannot contain equations, figures, tables, or references. It should concisely state what was done, how it was done, principal results, and their significance.
Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based Nfn Bahrawi
Journal of Information Technology and Its Utilization Vol 2 No 2 (2019)
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30818/jitu.2.2.2695

Abstract

Every day billions of data in the form of text flood the internet be it sourced from forums, blogs, social media, or review sites. With the help of sentiment analysis, previously unstructured data can be transformed into more structured data and make this data important information. The data can describe opinions / sentiments from the public, about products, brands, community services, services, politics, or other topics. Sentiment analysis is one of the fields of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions in text form. At the most basic level, the goal is to get emotions or 'feelings' from a collection of texts or sentences. The field of sentiment analysis, or also called 'opinion mining', always involves some form of data mining process to get the text that will later be carried out the learning process in the mechine learning that will be built. this study conducts a sentimental analysis with data sources from Twitter using the Random Forest algorithm approach, we will measure the evaluation results of the algorithm we use in this study. The accuracy of measurements in this study, around 75%. the model is good enough. but we suggest trying other algorithms in further research. 
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