Skripsi

ANALISIS SENTIMEN WARGANET INDONESIA TERHADAP VASEKTOMI DI X MENGGUNAKAN MODEL INDOBERT

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Vasectomy-related conversations on X (Twitter) frequently generate polarized pro–contra debates that can shape public understanding of male contraception, yet evidence on Indonesian netizens’ sentiment remains limited. This study maps and classifies sentiment toward vasectomy during April–June 2025 using a descriptive quantitative text-mining and NLP pipeline. After preprocessing (cleaning and deduplication), 9,817 posts were analyzed. Semisupervised labeling was performed using the teacher model taufiqdp/indonesiansentiment with confidence-based refinement, support ed by a rule-based sarcasm_flag that identified 330 potentially sarcastic texts. A 20% manually verified GOLD subset (1,963 samples) served as ground truth, and IndoBERT (indolem/indobert-base-uncased) was fine-tuned with weighted cross-entropy and early stopping. Evaluation on the GOLD test set (n = 393) showed strong performance (accuracy = 0.8168; macro F1 = 0.8141), with most errors concentrated in short, ambiguous, or humor/sarcasm-leaning posts. Full-corpus predictions produced 3,957 negative, 3,520 positive, and 2,340 neutral texts, indicating a contested and polarized discourse with a slightly higher negative share. These findings support the need for evidence-based digital communication strategies to address misconceptions and stigma surrounding male contraception.

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Availability

Detail Information

Series Title

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Call Number

T1947292026

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

xviii, 56 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.350 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Informasi

Specific Detail Info

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Statement of Responsibility

KA

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