Skripsi

ANALISIS SENTIMEN PENGGUNA MEDIA SOSIAL X TERHADAP FILM SORE: ISTRI DARI MASA DEPAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)

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Indonesia’s film industry is rapidly expanding with diverse genres and new titles, creating a need for fast, data-driven mapping of audience sentiment. This study maps social media X users’ sentiment toward the film “Sore: Istri dari Masa Depan,” which carries an unconventional theme, while addressing limited manual labels on short and noise tweets. Support Vector Machine (SVM) is selected for its robustness in high-dimensional feature spaces and effectiveness on short texts. A pseudo-labeling scheme replaces large-scale manual annotation by delegating label assignment to the model through calibrated zero-shot predictions combined with lexicon scores as an agreement filter and an abstain mechanism. The workflow covers deduplication, language filtering, preprocessing, TF-IDF feature extraction on word and character n-grams with meta-features, chi-square feature selection, and SVM training with an 80:20 stratified split and K-Fold Cross-Validation. Evaluation on the test set yields 82.36% accuracy and a macro F1 of 70.06%. The three-class confusion matrix shows dominant positive opinions and more confusion between neutral and negative classes. These findings position pseudo-labeling as a pragmatic trade-off between label quality and annotation efficiency while indicating generally positive public reception.

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Detail Information

Series Title

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Call Number

T1893632025

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvi, 131 hlm.; ilus.; tab, 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.350 7

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Analisis sentimen
Prodi Sistem Informasi
Algoritma Support Vector Machine

Specific Detail Info

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Statement of Responsibility

MI

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