Analisis Komentar Pada Twitter Terhadap Lapangan Kerja Dengan Metode Naïve Bayes
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Abstract
The Covid pandemic has had an impact on the economy of a country, especially Indonesia. The impact on the economic sector is the employment sector. The issue of employment on Twitter began to emerge when in 2020 last December when the corona began to enter Indonesia so that a wave of layoffs occurred. Indonesian people then use the media channel, namely twitter to comment about their condition. Twitter is one of the internet media channels created for social networking and a means of self-expression so that users who use Twitter will get their own satisfaction. This study tries to provide a perspective on an analysis of comments on Twitter related to employment during the COVID-19 pandemic. The analysis is carried out using the Orange application, the process is carried out through the stages of preprocessing, transformation, filtering, tokenizing, and normalization. The next stage is automatic labeling using the Vader method, classification using the naive Bayes method and weighting using the TF-IDF method and calculations from the orange data mining application which are represented by the results of the extended confusion matrix. The data that the author analyzes according to the orange application is 3929 tweet data from 22-30 August 2021 by utilizing the Twitter Web Crawling API. The results of the orange data mining application show that the accuracy with Naive Bayes is close to the perfect number, which is 99% with the number of sentiments in the community as much as 25% positive, 11% negative and 64% neutral.
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References
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