Sentiment Analysis in Platform X with the Support Vector Machine Method for Generation Z

Apriandini Sri Dewi; Sarjon  Defit; Gunadi Widi  Nurcahyo

doi:10.35134/komtekinfo.v12i4.659

Authors

Apriandini Sri Dewi Universitas Putra Indonesia YPTK
Sarjon Defit Universitas Putra Indonesia YPTK
Gunadi Widi Nurcahyo Universitas Putra Indonesia YPTK

DOI:

https://doi.org/10.35134/komtekinfo.v12i4.659

Keywords:

Sentiment Analysis, Generation Z, Support Vector Machine, Social Media, Machine Learning

Abstract

Advances in information technology and the increasing use of social media have significantly influenced the behavior of Generation Z. The generation born between 1997 and 2012 is known to be very familiar with the digital world, but also faces challenges such as lack of in-person social interaction and the risk of mental health disorders. This study aims to identify and classify public sentiment towards Generation Z on social media, especially on platform X (formerly Twitter). The method used is the Support Vector Machine (SVM). This research was carried out through several stages, namely the collection of 1607 data in the form of text using crawling techniques, pre-processing of text (tokenization, case folding, removal of stopwords, stemming, and normalization), and feature extraction using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The processed data is then classified into three sentiment categories: positive, negative, and neutral using SVM. Evaluation was carried out by measuring accuracy, recall value, and F1-score value through a confusion matrix. The results showed that the measurement of an accuracy value of 85%, a precision value of 85%, a value of recall of 95% and an F1-score value of 90% that SVM was able to classify sentiment with high accuracy and stability. In addition, SVM has been shown to be more effective than other methods studied in previous studies. The data analyzed shows that most sentiment towards Generation Z is negative, reflecting public concern about the behavior and mindset of this generation. This research is expected to be a reference for academics, practitioners, and policymakers in understanding public opinion and designing targeted policies for the younger generation.

Keywords: Sentiment Analysis, Generation Z, Support Vector Machine, Social Media, Machine Learning.

References

Khairi, M., Rianto, B., Chrismondari, Yolnasdi, Jalil, M., Juita, H., & Sudeska, E. (2025). Pengaruh teknologi dalam transformasi ekonomi dan bisnis di era digital. Jurnal Perangkat Lunak.

Mawaaddah. (2025). Analisis opini publik tagar #JanganJadiDosen pada media sosial X (Twitter).

Anisti, A., Sidarta, V., Imran, M., & Syatir, S. (2024). Tantangan literasi digital generasi Z: Kajian systematic literature review.

Pandya, T. (2024). Analisis fenomena filter bubble media sosial X dalam membentuk partisipasi politik Gen-Z pada masa kampanye Pilpres 2024.

Badriyah, L. (2024). Analisis sentimen media sosial: Pengaruh opini publik terhadap produktivitas Generasi Z dengan pendekatan kuesioner.

Aspar, & Said, N. M. (2025). Peran komunikasi terhadap kesehatan mental spiritual Generasi Z di era modern.

Aisah, I. S., Irawan, B., & Suprapti, T. (2023). Algoritma Support Vector Machine (SVM) untuk Analisis Sentimen Ulasan Aplikasi Al Qur'an Digital.

Sulistilawati, I., Musyafa, A., & Zain, R. M. (2024). Penerapan Data Mining Dalam Menentukan Pelajaran yang Diminati Dengan Metode Support Vector Machine (SVM).

Arum, L. S., Zahrani, A., & Duha, N. A. (2023). Karakteristik Generasi Z dan Kesiapannya Dalam Menghadapi Bonus Demografi 2030.

Winarto, W., Musdar, I. A., & Hasniati. (2024). Analisis Sentimen Bakal Calon Presiden 2024 Menggunakan Algoritma Support Vector Machine pada Twitter.

Rosyida, T., Putro, H. P., & Wahyono, H. (2023). Analisis Sentimen Terhadap Pilpres 2024 Berdasarkan Opini dari Twitter Menggunakan Naïve Bayes dan SVM.

Komara, D. A., & Widjaya, S. N. (2024). Memahami Perilaku Informasi Gen-Z dan Strategi Melawan Disinformasi: Sebuah Tinjauan Literatur Penggunaan Media Sosial

Muaviah, E., Lathifaturrahmah, & Dewi, A. A. (2023). Generasi Z: Melangkah di Era Digital dengan Bijak dan Terencana.

Fahlevvi, M. R. (2022). Analisis Sentimen Terhadap Ulasan Aplikasi Pejabat Pengelola Informasi dan Dokumentasi Kementerian Dalam Negeri Republik Indonesia di Google Playstore Menggunakan Metode Support Vector Machine.

Hokijuliandy, E., Napitupulu, H., & Firdaniza, F. (2023). Analisis Sentimen Menggunakan Metode Klasifikasi Support Vector Machine (SVM) dan Seleksi Fitur Chi-Square.

Khatami, M. K. (2024). Analisis Sentimen Twitter Menggunakan Naive Bayes dan Support Vector Machine Terhadap KPU pada Pemilihan Umum Presiden 2024.

Muaviah, E., Lathifaturrahmah, & Dewi, A. A. (2023). Generasi Z: Melangkah di Era Digital dengan Bijak dan Terencana.

Oktafiani, R., & Rianto, R. (2023). Perbandingan Algoritma Support Vector Machine (SVM) dan Decision Tree untuk Sistem Rekomendasi Tempat Wisata.

Peni, N., Suarna, N., & Prihartono, W. (2024). Analisis Sentimen Generasi Z terhadap Pengetahuan tentang Kehidupan Gay Menggunakan Algoritma Naïve Bayes.

Wulandari, Y. F., Rahastine, M. P., Afianto, H., Bastian, Y., & Murtiadi. (2023). Tantangan Komunikasi di Era Digital: Memahami Generasi Z.