eprintid: 11071
rev_number: 6
eprint_status: archive
userid: 46
dir: disk0/00/01/10/71
datestamp: 2026-07-29 01:51:17
lastmod: 2026-07-29 01:51:17
status_changed: 2026-07-29 01:51:17
type: thesis
metadata_visibility: show
creators_name: Fadhilah, Anas
creators_NPM: 065121021
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_name: Wihartiko, Fajar Delli
contributors_name: Andriani, Siska
corp_creators: Universitas Pakuan
corp_creators: Fakultas Matematika dan Ilmu Pnegetahuan Alam
corp_creators: Program Studi Ilmu Komputer
title: Analisis Sentimen Pada Hashtag #Kabupaten Aja dulu Menggunakan Metode Support Vector Machine (SVM) Dengan Word2vec Bidirectional Encoder Representations From Transformers (BERT)
ispublished: pub
subjects: QK
divisions: sch_ecs
full_text_status: none
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
date: 2025-06-21
date_type: published
institution: Universitas Pakuan
department: Fakultas Matematika dan Pengetahuan Alam
thesis_type: Skripsi
thesis_name: Sarjana
citation:   Fadhilah, Anas  (2025) Analisis Sentimen Pada Hashtag #Kabupaten Aja dulu Menggunakan Metode Support Vector Machine (SVM) Dengan Word2vec Bidirectional Encoder Representations From Transformers (BERT).  Skripsi thesis, Universitas Pakuan.