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All Journal Teknik dan Ilmu Komputer Prosiding Semnastek IJTB | INTERNATIONAL JOURNAL OF TECHNOLOGY AND BUSINESS I-STATEMENT JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology International Journal of New Media Technology Jurnal ULTIMATICS Zonasi: Jurnal Sistem Informasi Media Aplikom Kalbiscientia Jurnal Sains dan Teknologi Media Abdimas KALBISIANA Jurnal Sains, Bisnis dan Teknologi Jurnal Karya untuk Masyarakat (JKuM)
Ester Lumba
Universitas Bunda Mulia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Public Sentiment Analysis on the Transition from Analog to Digital Television Using the Random Forest Classifier Algorithm Samudera, Elfajar Bintang; Waworuntu, Alexander; Lumba, Ester
ULTIMATICS Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i1.3653

Abstract

Television is one of the most popular media for entertainment and information. Analog television is the most widely used type among the public. However, with technological advancements, analog television is becoming obsolete and is being replaced by digital television, which offers better performance. On November 2, 2022, the Government officially mandated the transition from analog to digital broadcasting. This Analog Switch Off program has elicited various pro and con opinions among the public. Twitter, a widely used social media platform, facilitates rapid communication and information dissemination among users. This study aims to classify public sentiment regarding the Analog Switch Off policy as either positive or negative. The classification model used is the Random Forest algorithm, with the Lexicon Inset for data labeling, Count Vectorizer and TF-IDF Vectorizer for data vectorization and weighting, and various train-test data splits. The study achieved the best classification performance using the Count Vectorizer method, with an 80%:20% train-test data ratio, yielding an accuracy of 88%, precision of 88%, recall of 88%, and an F1-score of 88%. Index Terms”Analog Television; Digital Television; Sentiment; Twitter; Random Forest
Co-Authors Arie Kusumawati Cantiqa Putri Larashati Chiquitha Danuputri Chyquitha Danuputri Danuputri, Chyquitha Destriana Widyaningrum Dyah Kusumawati Farah Alysia Parahita Glenn Nazari Hakim, Lukman Harya Bima Dirgantara Lukman Hakim Machmud Zuhdi Al Munir Maya Cendana, Maya Mochamad Apri Utama Muhammad Reza Khalafi Novia Rahman Nisa Nuraini Purwandari Poltak Pancarian Situmorang Rainner Crasseus Ridha Sefina Samosir Ridha Sefina Samosir Robertus Sanjaya Samudera, Elfajar Bintang Sonia Lassmayda Syahrani, Erwin Waworuntu, Alexander Yansen Yulia Ery Kurniawati Yulius Denny Prabowo Yulius Denny Prabowo