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PENDEKATAN EKSPOSISI DALAM PENYUTRADARAAN VIDEO FEATURE “MERAJUT ASA SEPATU LOKAL DI ERA DIGITAL” Adibakti, Hisyam Maulana; Fadilah, Efi; Prahatmaja, Nurmaya
AT-TAWASUL Vol 4 No 2 (2025): At Tawasul
Publisher : Program Studi Komunikasi dan Penyiaran Islam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51192/ja.v4i2.1896

Abstract

The decreasing interest in local footwear brands in the digital era presents a major challenge for creative industries in Indonesia. The feature video "Merajut Asa Sepatu Lokal di Era Digital" was directed using an expository approach to revive the value and potential of local shoe products, especially from Cibaduyut, Bandung. Through voice-over narration, expert interviews, and illustrative footage, this work aims to reshape public perception and raise awareness among Generation Z. This study explores the application of expository directing techniques and the director’s role in constructing an informative and visually compelling narrative. The research uses participatory observation, interviews, and content analysis of the produced video. The findings indicate that the expository approach is effective in delivering social and cultural messages while strengthening visual communication in local brand imaging amidst global brand dominance
Media's Role in Reporting 2024 Indonesian Election Fraud Pratama, Hafiz Gustika; Bajari, Atwar; Fadilah, Efi
WACANA: Jurnal Ilmiah Ilmu Komunikasi Volume 24, No. 2 December 2025
Publisher : Universitas Prof. Dr. Moestopo (Beragama)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32509/wacana.v%vi%i.4937

Abstract

The 2024 Indonesian Presidential Election sparked significant public debate regarding allegations of election fraud. This study investigated public sentiment toward media reporting in response to these fraud allegations on social media platform X. This study employed a quantitative approach with sentiment analysis methods, utilizing three sentiment analysis algorithms: Support Vector Machine (SVM), VADER Sentiment, and Naive Bayes. The research involved collecting tweets related to election fraud, which were then processed using the TF-IDF method to assess the importance of words within the text. Subsequently, the data was classified to identify the sentiment expressed in the tweets. VADER achieved the highest accuracy of 100%, followed by SVM at 92.29%, and Naive Bayes at 90.05%. While most tweets were neutral, negative sentiment was more prevalent in all models. These findings suggested that social media sentiment reflected public opinion on sensitive political issues, providing valuable insights into the discourse on election fraud. The study underscored the need for improving sentiment analysis methods, particularly in addressing data imbalance and the complexities of political sentiment in Indonesia.