KLASIFIKASI AKUN BUZZER PADA TWITTER MENGGUNAKAN ALGORITMA NAIVE BAYES

Perkasa, Catur Arpal (2023) KLASIFIKASI AKUN BUZZER PADA TWITTER MENGGUNAKAN ALGORITMA NAIVE BAYES. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

Amid Indonesia's e-commerce IPO, there is a suspicious movement of the dissemination of tweets with positive sentiments that involve the use of fabricated content spread by buzzers. In this study, the patterns of accounts involved will be investigated. A classifier will be developed based on the patterns of these accounts to accurately identify and distinguish between buzzer and non-buzzer accounts. The buzzer classifier will have four attributes: the number of followings, the number of followers, the sentiment value of recent tweets, and the age of the account. The data will be processed and cleaned before sentiment analysis is performed in order to provide weight to the data. Then, the data will be labeled according to predetermined characteristics. The Gaussian variant of Naive Bayes is used to classify accounts with and without stamping. The results show that the accuracy of the model is 80% and the buzzer classifier that implements the model predicts 63 true out of 70 isolated dataset, hence, the buzzer classifier is 90% accurate.

Item Type:

Thesis (Undergraduate)

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