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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Scientific Journal of Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika Data Science: Journal of Computing and Applied Informatics Journal of Information System, Applied, Management, Accounting and Research Indonesian Journal of Electrical Engineering and Computer Science Journal of Research in Mathematics Trends and Technology Jurnal EduHealth
Mahyuddin K. M. Nasution, Mahyuddin K. M.
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Mathematics Public Health Veterinary

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

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The feature extraction for classifying words on social media with the Naïve Bayes algorithm Arif Ridho Lubis; Mahyuddin Khairuddin Matyuso Nasution; Opim Salim Sitompul; Elviawaty Muisa Zamzami
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1041-1048

Abstract

To classify Naïve Bayes classification (NBC), however, it is necessary to have a previous pre-processing and feature extraction. Generally, pre-processing eliminates unnecessary words while feature extraction processes these words. This paper focuses on feature extraction in which calculations and searches are used by applying word2vec while in frequency using term frequency-Inverse document frequency (TF-IDF). The process of classifying words on Twitter with 1734 tweets which are defined as a document to weight the calculation of frequency with TF-IDF with words that often come out in tweet, the value of TF-IDF decreases and vice versa. Following the achievement of the weight value of the word in the tweet, the classification is carried out using Naïve Bayes with 1734 test data, yielding an accuracy of 88.8% in the Slack word category tweet and while in the tweet category of verb 78.79%. It can be concluded that the data in the form of words available on twitter can be classified and those that refer to slack words and verbs with a fairly good level of accuracy. so that it manifests from the habit of twitter social media user.
Co-Authors Al Khowarizmi Alexander F.K. Sibero Arif Ridho Lubis Azmi, Zulfian - Elviawaty Muisa Zamzami Herman Mawengkang Marischa Elveny Muhammad Zarlis Muhammad Zarlis, Muhammad Opim Salim Sitompul Rahmad Syah Rahman Aulia, Rahman Verdi Yasin Zarlis, M Zarlis, M Zulfian Azmi