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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika JTAM (Jurnal Teori dan Aplikasi Matematika) SINTECH (Science and Information Technology) Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Technology and Informatics (JoTI) Jurnal Pengabdian Kepada Masyarakat Abdi Nusa ABDINE Jurnal Pengabdian Masyarakat Bulletin of Social Informatics Theory and Application Jurnal Medika: Medika Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif
Arsi, Primandani
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Bulletin of Social Informatics Theory and Application

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Sentiment analysis of Indonesian government policy in the era of social commerce: public perception and reaction Sugiarti, Sugiarti; Arsi, Primandani; Subarkah, Pungkas; V, Jay
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.710

Abstract

This research explores public sentiment towards the Indonesian government’s policies in the era of social commerce, based on Minister of Trade Regulation No. 31 of 2023. Sentiment analysis was conducted on a dataset comprising 1013 tweets on Twitter, employing various machine learning algorithms, including Naïve Bayes, Logistic Regression, Random Forest, SVM, and KNN. The results reveal that the Support Vector Machine (SVM) algorithm achieved the highest accuracy rate of 87%, outperforming other algorithms. Analyzing public sentiment towards the mentioned government policies, positive sentiment accounted for 20.2%, while negative sentiment reached 79.8%. This suggests that the policies, as outlined in the regulation, did not elicit a positive response from the public. Recommendations for future research include expanding the dataset and incorporating diverse data sources beyond Twitter for enhanced accuracy. This study contributes valuable insights into public sentiment analysis, particularly in the context of social commerce policies, providing a foundation for further investigations and policy adjustments.
Co-Authors Adam Prayogo Kuncoro Aditya Permana, Reza Agung Prasetyo Astrida, Deuis Nur Asyari, Muhammad Hasyim Azzahra, Delia Oktaviana Bimawan, Zaynuri Ilham Busyro, Muhammad Dedi Purwanto, Dedi Dhanar Intan Surya Saputra Firmanda, Reza Arief Hendra Marcos Isnaini, Khairunnisak Nur Junianto, Haris Khofiyah, Salma Ngarifatul Kholifah Dwi Prasetyo Kartika, Nur Kisma, Atmaja Jalu Narendra Kusuma, Bagus Adhi Lestari, Vika Febri Nandang Hermanto Nasar Ghanim, Nadif Nur Ikhsan, Ali Prasetyo Kartika, Nur Kholifah Dwi Prayoga, Iphang Riandini, Dini Rofiqoh, Dayana Salsabiela, Ayuni Sandhi Bhakti, Dwi Saputri, Inka Sari, Rida Purnama Septiana Putri, Refida Subarkah, Pungkas Sugiarti Sugiarti Suhaman, Jali Tarwoto, Tarwoto Tri Astuti V, Jay Waluyo, Retno