Analisis Sentimen Pada Opini Masyarakat Terhadap Kasus Magang Tanpa Bayaran Menggunakan Indo Generative Pre-Trained Transformer

Susanti, Aprilia (2022) Analisis Sentimen Pada Opini Masyarakat Terhadap Kasus Magang Tanpa Bayaran Menggunakan Indo Generative Pre-Trained Transformer. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Magang merupakan salah satu program yang sering diikuti oleh mahasiswa untuk memperoleh pengalaman kerja dan mengembangkan keterampilan profesional. Namun, fenomena magang tanpa bayaran atau unpaid internship sering kali menimbulkan perdebatan dan pandangan yang beragam di kalangan masyarakat, terutama di media sosial Twitter. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap opini masyarakat terkait kasus magang tanpa bayaran di Twitter menggunakan model Indo Generative Pre-Trained Transformer (IndoGPT). Data yang digunakan dalam penelitian ini adalah tweet yang dikumpulkan dari Twitter dengan kata kunci terkait magang tanpa bayaran. Setelah melalui tahap pra-pemrosesan data, data tersebut kemudian diklasifikasikan ke dalam sentimen positif, negatif, dan netral menggunakan model IndoGPT. Hasil analisis menunjukkan bahwa opini masyarakat terhadap magang tanpa bayaran cenderung didominasi oleh sentimen negatif. Hal ini mencerminkan adanya ketidakpuasan dan kekhawatiran masyarakat terhadap praktik magang tanpa bayaran yang dianggap tidak adil atau eksploitatif. Penelitian ini memberikan gambaran tentang bagaimana opini publik dapat dipahami melalui analisis sentimen dengan bantuan teknologi deep learning. Hasil penelitian ini dapat menjadi referensi bagi pembuat kebijakan, perusahaan, dan institusi pendidikan dalam memahami pandangan masyarakat terkait isu magang tanpa bayaran.
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Internships are programs often followed by students to gain work experience and develop professional skills. However, the phenomenon of unpaid internships often causes debate and varied views among the public, especially on social media Twitter. This research aims to conduct sentiment analysis on public opinion regarding unpaid internship cases on Twitter using the Indo Generative Pre-Trained Transformer (IndoGPT) model. The data used in this study are tweets collected from Twitter with keywords related to unpaid internships. After going through the data pre-processing stage, the data is then classified into positive, negative, and neutral sentiments using the IndoGPT model. The analysis results show that public opinion on unpaid internships tends to be dominated by negative sentiment. This reflects the public's dissatisfaction and concern regarding unpaid internship practices which are considered unfair or exploitative. This research provides an overview of how public opinion can be understood through sentiment analysis with the help of deep learning technology. The results of this study can be a reference for policymakers, companies, and educational institutions in understanding public views regarding the issue of unpaid internships.

Item Type:	Thesis (Other)
Additional Information:	RSMa 519.53 Sus a-1 2022
Uncontrolled Keywords:	Analisis Sentimen. IndoGPT. Magang Tanpa Bayaran. Twitter. Sentiment Analysis. IndoGPT. Unpaid Internship. Twitter.
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Mathematics and Science > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	05 Jun 2026 05:53
Last Modified:	05 Jun 2026 05:53
URI:	http://repository.its.ac.id/id/eprint/133611

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