SUBRATA, BULAN KIRANA (2026) Analisis Sentimen Masyarakat terhadap Profesionalisme Generasi Z di Dunia Kerja Menggunakan Support Vector Machine (SVM) Melalui Platform X. S1 thesis, Universitas Mercu Buana Jakarta - Menteng.
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Abstract
Generasi Z yang lahir pada rentang tahun 1997 hingga 2012 saat ini telah memasuki dunia kerja dengan sifat yang berbeda dibandingkan generasi sebelumnya, sehingga menimbulkan berbagai tanggapan masyarakat terkait profesionalisme mereka yang banyak disampaikan melalui media sosial. Analisis sentimen digunakan untuk melihat kecenderungan penilaian atau opini masyarakat terhadap suatu isu berdasarkan data teks, seperti komentar atau unggahan pengguna. Penelitian ini dilakukan untuk mengetahui bagaimana sentimen masyarakat Indonesia terhadap profesionalisme Generasi Z di dunia kerja berdasarkan data yang diperoleh dari platform X. Data yang digunakan berjumlah 2.095 tweet hasil crawling, dengan pelabelan manual terdiri dari 1.092 tweet negatif, 855 tweet positif, dan 145 tweet netral. Data kemudian diproses melalui tahapan preprocessing dan ekstraksi fitur menggunakan metode TF-IDF, selanjutnya diklasifikasikan menggunakan metode Support Vector Machine (SVM) kernel linear dengan tiga skenario pembagian data, yaitu 70:30, 80:20, serta 90:10. Hasil pengujian menunjukkan bahwa metode SVM memberikan performa terbaik pada rasio 90:10 dengan nilai akurasi sebesar 69,52%, presisi 66%, dan recall 70%. Generation Z, defined as individuals born between 1997 and 2012, has begun to enter the workforce and shows characteristics that differ from previous generations. These differences have led to various public responses regarding their professionalism, which are widely expressed on social media platforms. Sentiment analysis is applied to identify the tendency of public opinions or evaluations toward a particular issue based on textual data, such as user comments or posts. This study aims to examine public sentiment in Indonesia toward the professionalism of Generation Z in the workplace using data collected from platform X. The dataset consists of 2,095 tweets obtained through a crawling process, with manual labeling resulting in 1,092 negative tweets, 855 positive tweets, and 145 neutral tweets. The data were processed through preprocessing stages and feature extraction using the TF-IDF method, then classified using the Support Vector Machine (SVM) algorithm with a linear kernel under three data split scenarios: 70:30, 80:20, and 90:10. The results indicate that the SVM method achieved its best performance at the 90:10 ratio, with an accuracy of 69.52%, precision of 66%, and recall of 70%.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41822010058 |
| Uncontrolled Keywords: | Analisis Sentimen, Generasi Z, Profesionalisme, Support Vector Machine, Platform X. Sentiment Analysis, Generation Z, Professionalism, Support Vector Machine, Platform X. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 003 Systems/Sistem-sistem |
| Divisions: | Fakultas Ilmu Komputer > Sistem Informasi |
| Depositing User: | ARDIFTA DWI AFRIANI |
| Date Deposited: | 12 Feb 2026 02:12 |
| Last Modified: | 12 Feb 2026 02:12 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/100912 |
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