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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Tampubolon, Fenny Chintya
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Computer Science & IT

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ANALISIS SENTIMEN TIK TOK PADA MEDIA SOSIAL DENGAN ALGORITMA NAIVE BAYES CLASSIFIER Rahmadani, Putri Suci; Tampubolon, Fenny Chintya; Jannah, Adelia Nurfattul; Hutabarat, Novia Lucky Halen; Simarmata, Allwin M.
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11579

Abstract

Social media is a computer application designed to make it simpler to communicate with others without having to do it face-to-face, as well as a tool for having fun and reducing feelings of isolation. Existing social media applications include games, music, and media for communicating with distant individuals, among others. These social media are utilized by parents, adolescents, and even young children. The application Tik-Tok is frequently used by children as a social networking platform. Tik-Tok has succeeded in grabbing the interest of youngsters, such that children are curious about creating short movies on the platform. Due to the fact that this application is used by children, the researcher seeks to use the Naïve Bayes Classifier Algorithm to recognize and differentiate unfavorable remarks on TikTok's social media. The rising number of negative remarks in the TikTok comments column can hinder the mental development of youngsters, and it is hoped that this algorithm would encourage users to post positive comments on this application. Based on the data gathering until the results of classification are obtained. There are 600 comments data randomly collected from TikTok users, gathered through the export comments website. After evaluating, the accuracy of the application of the Naïve Bayes Classifier algorithm in conducting sentiment analysis is 80% while the result of the AUC is 46%
Customer Classification Using Naive Bayes Classifier With Genetic Algorithm Feature Selection Tanjung, Juliansyah Putra; Tampubolon, Fenny Chintya; Panggabean, Ari Wahyuda; Nandrawan, M. Anjas Asmara
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.12182

Abstract

There is a tendency to decrease the number of speedy customers in the operational area of ​​North Sumatra due to customer dissatisfaction. Termination of employment is carried out by the customer against PT. Telekomunikasi Indonesia, Tbk in North Sumatra. There is no management of customer data classification so that classification information based on certain product purchases cannot be known. Naïve Bayes is a classification algorithm that is easy to use but has weaknesses which result in poor performance, therefore feature selection is needed, the genetic algorithm is an algorithm that is able to select attributes in research, will be selected based on the highest weight so that the accuracy of the prediction results is more optimal. The steps taken in the measurement model using the Naive Bayes Classifier (NBC) approach and the model using the GA-NBC approach obtained accurate results from cross validation measurements, Confusion Matrix, ROC curves for the classification of existing and speedy telephone subscribers. The stages of the Naive Bayes process are: data collection, data preprocessing, processing of the Naive Bayes Classifier algorithm. Then the results are validated and evaluated using the Text Mining Algorithm, and calculating the parameters based on the genetic algorithm. The accuracy produced by the Naive Bayes Classifier model is 85.08%. The accuracy produced by the Naive Bayes Classifier model with the selection of Genetic Algorithm features increased to 89.31%.
Co-Authors Hutabarat, Novia Lucky Halen Jannah, Adelia Nurfattul Juliansyah Putra Tanjung Nandrawan, M. Anjas Asmara Panggabean, Ari Wahyuda Rahmadani, Putri Suci Simarmata, Allwin M.