ANALISIS SENTIMEN PUBLIK TERHADAP FENOMENA JUDI ONLINE DI MEDIA SOSIAL X MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)

Andika Dwi Prastiko; ade davy wiranata;

ABSTRAK

Analisis Sentimen Publik terhadap Fenomena Judi Online di Media Sosial X

Menggunakan Metode Support Vector Machine (SVM)

Andika Dwi Prastiko

Fenomena judi online yang marak di media sosial X (Twitter) menimbulkan kekhawatiran sosial
karena melibatkan berbagai kalangan dan tetap berkembang meski dilarang secara hukum.
Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap fenomena judi online serta
menilai efektivitas algoritma Support Vector Machine (SVM) dalam mengklasifikasikan opini
publik menjadi sentimen positif dan negatif. Data diperoleh melalui web scraping sebanyak 1.006
tweet berbahasa Indonesia dan diproses melalui tahapan cleaning, tokenizing, TF-IDF, serta
klasifikasi menggunakan SVM. Hasil penelitian menunjukkan bahwa 81,31% tweet bersentimen
negatif dan 18,69% bersentimen positif, dengan akurasi model mencapai 90,59%. Temuan ini
menunjukkan bahwa mayoritas masyarakat menolak praktik judi online karena dianggap
meresahkan, serta membuktikan bahwa SVM efektif digunakan untuk analisis sentimen publik dan
dapat menjadi dasar bagi penyusunan kebijakan pencegahan penyebaran konten judi daring.
Kata Kunci : Analisis Sentimen, Judi Online, Media Sosial X, Support Vector Machine (SVM)

Public Sentiment Analysis on the Phenomenon of Online Gambling on Social
Media X Using the Support Vector Machine (SVM) Method

Andika Dwi Prastiko

The phenomenon of online gambling on social media platform X (formerly Twitter) has raised
significant social concern in Indonesia due to its widespread reach and easy accessibility despite
being legally prohibited. This study aims to analyze public sentiment toward online gambling and
evaluate the effectiveness of the Support Vector Machine (SVM) algorithm in classifying public
opinions into positive and negative categories. The research employs a text mining–based sentiment
analysis approach using web scraping to collect 1,006 Indonesian-language tweets posted between
July 15, 2024, and July 15, 2025. Data were preprocessed through cleaning, tokenizing,
normalization, TF-IDF weighting, and classification using SVM with an 80:20 training-testing split.
The results show that 81.31% of tweets expressed negative sentiment, while 18.69% expressed
positive sentiment, with an accuracy rate of 90.59%. These findings indicate that the majority of
users oppose online gambling activities, and the SVM algorithm proves effective for public opinion
analysis, providing valuable insights for policymakers in preventing the spread of online gambling
content.
Keywords: Sentiment Analysis, Online Gambling, Social Media X, Support Vector Machine (SVM)