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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Pendidikan dan Pengajaran Indonesian Journal of Artificial Intelligence and Data Mining JSiI (Jurnal Sistem Informasi) Jurnal Ilmiah Media Sisfo Jurnal Nasional Komputasi dan Teknologi Informasi Indonesian Journal on Learning and Advanced Education (IJOLAE) Journal of Information System,Graphics, Hospitality and Technology Jurnal Nasional Ilmu Komputer JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Indonesian Journal of Informatic Research and Software Engineering Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Karya Abdi Masyarakat Journal of Business Studies and Management Review J-Icon : Jurnal Komputer dan Informatika Jurnal Pengabdian Masyarakat (JPM) Pinang Masak Jurnal Sistem Informasi dan Ilmu Komputer
Utomo, Pradita Eko Prasetyo
Univeristas Jambi
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Journal : J-Icon : Jurnal Komputer dan Informatika

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ANALISIS SENTIMEN TAGAR #INDONESIATERSERAH DI MASA COVID-19 MENGGUNAKAN METODE SENTISTRENGTH Bisma Aulia; Pradita Eko Prasetyo Utomo; Ulfa Khaira; Tri Suratno
J-Icon : Jurnal Komputer dan Informatika Vol 9 No 2 (2021): Oktober 2021
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v9i2.4275

Abstract

The use of social media during the current pandemic is the choice of the community in expressing their thoughts, one of which is Twitter. With the hashtag feature in the Twitter application, people can find out the latest trending information. With the current pandemic condition that raises many social, political, economic problems and so on, making Twitter a place for people to express their emotions. Not long ago, the hashtag #IndonesiaTerserah became a byword in the community because it described the public's disappointment with the handling of the Corona virus (COVID-19) in Indonesia. This study aims to see how the sentiments of the Indonesian people through the hashtag #IndonesiaTerserah. The sentiments were analyzed through the sent-strength algorithm, and classified into 3 classes, namely positive, neutral, and negative. This algorithm uses the lexicon as the basis for calculating the weight of the sentiment strength. The stages carried out in this study are the data crawling stage, data preprocessing and word weighting. The results of this study obtained 236 tweet data with 41.5% neutral sentiment, 32.2% negative sentiment, and 26.3% positive sentiment. This research is expected to be a benchmark for stakeholders in making a decision.
Co-Authors Agung Mahadewa Arsa, Daniel Aulia Dwiza Puteri Ayu Indryani Azhari SN Benedika Ferdian Hutabarat Benedika Ferdian Hutabarat Bisma Aulia Cepi Ramdan Dedy Setiawan Desi Musfiroh Dwi Agus Kurniawan Dwi Agus Kurniawan Edi Saputra Elisa, Edi Ghaitsa Althafah Wandira Imelda Raudati Indra Weni Jefri Marzal Jefri Marzal Khaira, Ulfa Lucky Enggraini Fitri Made Gitra Aryawan Mauladi Mauladi Mauladi Mauladi Muhammad Iqbal Muksin Alfalah Ragil Johanda Reni Aryani Repaldi Handi Saputra Riyan Mahmudin Rizka Octavia Sandra Rizqa Raaiqa Bintana Salmah Nur Zahra Septika Sari Sari Sulfiyandi Sulfiyandi Suwannit Chareen Chit Sylvia Kartika Wulan Bhayangkari Tasia Maidi Saputri Tri Suratno Tri Suratno Tri Ulfa Khaira Ulfa