Septiani, Wianda Ayudia (2026) ANALISIS SENTIMEN BERBASIS ASPEK PADA SERIES ORIGINAL NETFLIX: ARCANE MENGGUNAKAN SVM. Skripsi thesis, Universitas Pakuan.
Full text not available from this repository.Abstract
ANALISIS SENTIMEN BERBASIS ASPEK PADA SERIES ORIGINAL NETFLIX: ARCANE MENGGUNAKAN SVM Boldson Situmorang1 ; Wianda Ayudia Septiani2 ; Dini Suhartini3 Program Studi Ilmu Komputer2 Universitas Pakuan2 Abstract— The rapid growth of social media has increased public participation in expressing opinions on entertainment content, including television series. Arcane, as a Netflix original series, has gained wide attention and generated various audience responses on platforms such as X and Youtube. This study aims to conduct Aspect-Based Sentiment Analysis (ABSA) on Arcane audience comments using the Support Vector Machine (SVM) method. The dataset was collected from X with 3,193 comments and from Youtube with 2.863 comments, resulting in a total of 6.056 raw comments. After splitting multiaspect comments, the data were transformed into an aspect-oriented dataset of 8,309 documents. Aspect identification was performed using Latent Dirichlet Allocation (LDA), which produced four main aspects: Animation, Soundtrack, Character, and Story, with an additional General aspect for comments that could not be assigned to the four main aspects. The research workflow included text preprocessing, initial sentiment labeling using VADER, feature weighting using Term Frequency–Inverse Document Frequency (TF-IDF), class balancing using Synthetic Minority Oversampling Technique (SMOTE), and sentiment classification using a linear-kernel SVM. The evaluation results showed that the highest accuracy was achieved on the Soundtrack aspect (82%), followed by Story (80%), Animation (77%), Character (76%), and General (75%). These findings indicate that SVM is effective for aspect-based sentiment classification and can provide more detailed insights into audience perceptions of Arcane across different aspects.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Subjects: | Fakultas Ilmu Pengetahuan Alam dan Matematika > Ilmu Komputer |
| Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam > Ilmu Komputer |
| Depositing User: | PERPUSTAKAAN FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM UNPAK |
| Date Deposited: | 29 Jul 2026 01:49 |
| Last Modified: | 29 Jul 2026 01:49 |
| URI: | http://eprints.unpak.ac.id/id/eprint/11062 |
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