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All Journal SENTRI: Jurnal Riset Ilmiah
Kristi V. Mokodompit
Universitas Sam Ratulangi Manado
Aerospace Engineering Humanities Computer Science & IT Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice

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KLASIFIKASI INTENSI KOMUNIKATIF PADA GAMBAR REAKSI DALAM KOMENTAR KONTEN KOMEDI TIKTOK MENGGUNAKAN CNN BERBASIS TRANSFER LEARNING EFFICIENTNETB0 Kristi V. Mokodompit; Christian A. J. Soewoeh; Tohap Manurung
SENTRI: Jurnal Riset Ilmiah Vol. 5 No. 10 (2026): SENTRI : Jurnal Riset Ilmiah, Oktober 2026
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v5i10.7257

Abstract

Reaction images are a form of visual communication widely used in social media comments, including TikTok. They can convey direct responses or indirect communicative intentions, such as sarcasm, irony, evaluation, symbolism, and implicit humor. This study aimed to develop and validate a classification of communicative intentions into Literal and Figurative Intentions and implement a Convolutional Neural Network (CNN) using EfficientNetB0 transfer learning. A total of 339 images were annotated by three annotators using an expert-judged guideline. After revising the guideline and relabeling 124 disagreements, the average descriptive Cohen’s Kappa increased from 0.389 to 0.886. Following duplicate removal, 334 images remained, comprising 245 Literal and 89 Figurative Intentions. The data were stratified into training, validation, and test sets. The baseline achieved 76.47% accuracy, 67.31% macro F1-score, 75.45% weighted F1-score, and 0.6933 AUC. Figurative Intentions remained more difficult to classify, indicating limitations of unimodal visual approaches for context-dependent communication.
Co-Authors Christian Alderi Jeffta Soewoeh Tohap Manurung