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primary topic: Analisis Sentimen Pada Hashtag #Kabupaten Aja dulu Menggunakan Metode Support Vector Machine (SVM) Dengan Word2vec Bidirectional Encoder Representations From Transformers (BERT)
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HTML Summary of #11071 Analisis Sentimen Pada Hashtag #Kabupaten Aja dulu Menggunakan Metode Support Vector Machine (SVM) Dengan Word2vec Bidirectional Encoder Representations From Transformers (BERT)
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primary topic: Analisis Sentimen Pada Hashtag #Kabupaten Aja dulu Menggunakan Metode Support Vector Machine (SVM) Dengan Word2vec Bidirectional Encoder Representations From Transformers (BERT)

Analisis Sentimen Pada Hashtag #Kabupaten Aja dulu Menggunakan Metode Support Vector Machine (SVM) Dengan Word2vec Bidirectional Encoder Representations From Transformers (BERT)
abstract: Social media has become a major platform for expressing public opinion regarding social, political, and economic issues. One of the viral phenomena currently discussed in Indonesia is the hashtag #KaburAjaDulu, which reflects public concerns regarding employment opportunities, economic instability, and social conditions. This study aims to compare the performance of Support Vector Machine (SVM) combined with Word2Vec and Bidirectional Encoder Representations from Transformers (BERT) in sentiment analysis of tweets containing the #KaburAjaDulu hashtag on X (Twitter). The dataset was collected using web scraping techniques and processed through several preprocessing stages, including cleaning text, case folding, slang normalization, tokenization, stopword removal, and sentence reconstruction. The research adopted the Cross Industry Standard Process for Data Mining (CRISP-DM) framework consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment stages. In the first approach, Word2Vec with the Skip-gram architecture was used to generate word embeddings which were classified using SVM with a Radial Basis Function (RBF) kernel. The second approach employed IndoBERT for contextual sentiment classification. Model evaluation was conducted using accuracy, precision, recall, F1- score, and confusion matrix metrics. Experimental results demonstrated that BERT achieved superior classification performance compared with SVM combined with Word2Vec due to its contextual understanding capability. The findings indicate that transformer-based models provide more accurate sentiment classification for Indonesian social media text, especially in detecting contextual and implicit sentiment expressions. Keywords: Sentiment Analysis; Support Vector Machine; Word2Vec; BERT; Twitter; Natural Language Processing; CRISP-DM; #KaburAjaDulu
author list: authors
status: published
date: 2025-06-21
is part of: repository
subject: Ilmu Komputer
type: Article
type: Thesis
type: EPrint
type: ThesisEPrint

Ilmu Komputer
type: Concept

Fakultas Matematika dan Ilmu Pnegetahuan Alam
type: Organization

Universitas Pakuan
type: Organization

Fakultas Matematika dan Pengetahuan Alam, Universitas Pakuan
type: Organization

Program Studi Ilmu Komputer
type: Organization

Anas Fadhilah
family name: Fadhilah
given name: Anas
type: Person

Siska Andriani
family name: Andriani
given name: Siska
type: Person

Fajar Delli Wihartiko
family name: Wihartiko
given name: Fajar Delli
type: Person