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All Journal PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science
Reza Wahyu Hardian
Universitas Jambi
Computer Science & IT

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Analisis Sentiment Kuliah Daring Di Media Sosial Twitter Selama Pandemi Covid-19 Menggunakan Algoritma Sentistrength: Online Lecture Sentiment Analisys On Twitter Social Media During The Covid-19 Pandamic Using Sentistrength Algorithm Reza Wahyu Hardian; Pradita Eko Prasetyo; Ulfa Khaira; Tri Suratno
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 1 No. 2 (2021): MALCOM October 2021
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (605.908 KB) | DOI: 10.57152/malcom.v1i2.15

Abstract

Analisys sentiment adalah suatu bentuk kegiatan yang digunakan untuk menganalisis opini masyarakat tentang suatu kejadian seperti kuliah daring selama pandemi Covid-19 melalui salah satu media sosial Twitter. Kuliah daring adalah suatu sistem pembelajaran yang dilakukan dengan membutuhkan internet dalam proses belajar- mengajar. Sedangkan Twitter adalah suatu platform microblogging yang digunakan untuk menulis suatu opini atau pendapat tentang suatu peristiwa. Metode SentiStrenth merupakan salah satu metode yang dapat digunakan untuk melakukan analisis sentiment terhadap kebijakan Kuliah Daring yang digunakan untuk mengklasifikasi suatu tweet berdasarkan tingkat emosinya. Hasil proses analisis sentiment menggunakan sentistrength berdasarkan opini masyarakat dalam hal ini pengguna Twitter terhadap kebijakan kuliah daring menghasilkan nilai netral dengan persentase 54%, dan tingkat akurasi 85%, serta tingkat kesalahan 15%.
Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Kebijakan Kemdikbudristek Mengenai Kuota Internet Selama Covid-19 Ulfa Khaira; Reni Aryani; Reza Wahyu Hardian
Jurnal PROCESSOR Vol 18 No 2 (2023): Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2023.18.2.897

Abstract

Sentiment analysis is an activity that is used to analyze public opinion about an incident such as the Ministry of Education and Culture's internet assistance quota during the Covid-19 pandemic through one of the Twitter social media. Twitter is a microblogging platform that is used to write an opinion or opinion about an event that can be used as a source of data used. The Naïve Bayes method and Support Vector Machine (SVM) are methods with a Machine Learning approach that can be used to perform sentiment analysis on Kemdikbudristek policies regarding MoEC Quotas in the process of classifying a tweet based on its emotional level and knowing the accuracy comparison between the Naïve Bayes method and the Support Vector Machine ( SVM). The results of the sentiment analysis process using the Naïve Bayes Algorithm and Support Vector Machine (SVM) based on public opinion, in this case Twitter users regarding the Ministry of Education and Culture Quota policies, resulted in a higher level of accuracy for the Support Vector Machine (SVM) than Naïve Bayes with an accuracy of 80%, for an average -the average precision value is 80.3%, recall is 80.3% and f1-score is 80.3%.
Co-Authors Khaira, Ulfa Pradita Eko Prasetyo Reni Aryani Tri Suratno