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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Syukron Abdul Aziz
UIN Sunan Ampel Surabaya
Computer Science & IT Engineering

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ANALISIS SENTIMEN FENOMENA PENGIBARAN BENDERA STRAW HAT PIRATES DI MOMEN HUT RI KE-80 DENGAN MENGGUNAKAN SUPPORT VECTOR MACHINE DAN EKSTRAKSI FITUR GLOVE: SENTIMENT ANALYSIS OF THE STRAW HAT PIRATE FLAG HOISTING PHENOMENON DURING THE 80TH ANNIVERSARY OF INDONESIAN INDEPENDENCE DAY USING SUPPORT VECTOR MACHINE Syukron Abdul Aziz; Dian Candra Rini Novitasari; Maunah Setyawati; Jiphie Gilia Indrayani
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7621

Abstract

The phenomenon of hoisting the Straw Hat Pirate flag during the commemoration of the 80th Anniversary of Indonesian Independence Day has sparked diverse public reactions on social media platform X. Some members of society interpret it as a symbol of freedom of expression and social criticism, while others consider the action inappropriate as it is perceived to reduce the meaning of national symbols. This study aims to analyze public sentiment toward this phenomenon by classifying public opinion into positive and negative sentiments and to determine the performance of the classification method used. The method employed is Support Vector Machine (SVM) with text feature extraction based on Global Vectors for Word Representation (GloVe). The research data consists of 2,660 tweets collected during 1 August 2025 into 31 August 2025 through a crawling process using keywords related to the flag-hoisting phenomenon. The research stages include text pre-processing, word vector formation using GloVe, and sentiment classification using SVM. Model validation was conducted using the K-Fold Cross Validation method and evaluated using Confusion Matrix based on accuracy, precision, recall, and F1-score metrics. The research results demonstrate that the SVM model with GloVe features is capable of classifying public sentiment, achieving an accuracy value of 0.9708, precision of 0.9733, recall of 0.9698, and F1-score of 0.97154, while providing a mapping where positive sentiment regards the phenomenon as a form of freedom of expression and negative sentiment reflects views that consider the action inappropriate.
Co-Authors Jiphie Gilia Indrayani Maunah Setyawati Sari, Dian Candra Rini Novita