ANALISIS SENTIMEN DI MEDIA SOSIAL TWITTER DENGAN STUDI KASUS KARTU PRAKERJA

I. Subekti, Muhammad Habibi, Aris Wahyudi Murdiyanto, Alfun Roehatul Jannah
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Abstract

Kartu Prakerja is one of the government's flagship programs in providing training to the workforce. In its implementation there is a lot of information scattered, especially on social media Twitter both in the pros and cons of Kartu Prakerja program. Based on information in the form of tweets that have not been analyzed in depth, it is necessary to analyze sentiment on the Kartu Prakerja in order to obtain appropriate information based on the opinions of netizen   s on Twitter. This study discusses sentiment analysis of tweet data with the keyword “Kartu Prakerja” which uses data as many as 6658 tweet data taken in the period May 27 - August 5, 2021. This research uses the Naive Bayes Classification method which has several stages, namely data retrieval, data preprocessing, manual labeling, data training and testing. The solution offered in this study is to create an analysis model that can be used to perform sentiment analysis about Kartu Prakerja on Twitter. Based on the results of this study obtained that the calculation of accuracy obtained a value of 86% for training data and 87% for data testing. This study concluded that the Kartu Prakerja has a positive sentiment by Twitter netizens based on the results of Classification that discusses many positive sentiments such as the benefits, effectiveness and addition of the Kartu Prakerja budget.
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社交媒体 twitter 上的情感分析与职前卡案例研究
Kartu Prakerja 是政府为劳动力提供培训的旗舰项目之一。在该计划的实施过程中,有许多信息散落在各处,尤其是在社交媒体 Twitter 上,其中既有对 Kartu Prakerja 计划有利的信息,也有对其不利的信息。基于尚未深入分析的推文形式的信息,有必要对 Kartu Prakerja 进行情感分析,以便根据推特上网民的意见获得适当的信息。本研究讨论了对以 "Kartu Prakerja "为关键词的推特数据的情感分析,使用的数据多达 6658 条,时间跨度为 2021 年 5 月 27 日至 8 月 5 日。本研究使用了 Naive Bayes 分类方法,该方法分为几个阶段,即数据检索、数据预处理、人工标注、数据训练和测试。本研究提供的解决方案是创建一个分析模型,用于在 Twitter 上对 Kartu Prakerja 进行情感分析。根据本研究得出的结果,对训练数据和测试数据的计算准确率分别为 86% 和 87%。本研究的结论是,根据分类结果,Twitter 网民对 Kartu Prakerja 有积极的情感,如 Kartu Prakerja 的好处、有效性和增加预算等。
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