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All Journal Syntax Literate: Jurnal Ilmiah Indonesia IJIES (International Journal of Innovation in Enterprise System)
Kuswandi, Brillian Adhiyaksa
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Humanities Computer Science & IT Control & Systems Engineering Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Analyzing Twitter Sentiments on Booster Vaccination with Support Vector Machine (SVM) Method Fauzi, Rahmat; Hamami, Faqih; Maulana, Fakhri Hassan; Kuswandi, Brillian Adhiyaksa; Ramli, Muhammad Ayyub
International Journal of Innovation in Enterprise System Vol. 8 No. 2 (2024): International Journal of Innovation in Enterprise System
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijies.v8i02.648

Abstract

The Indonesian government has implemented various measures to prevent the spread of the COVID-19 virus, one of which is through a vaccination program with two doses (the first dose and the second dose). However, new variants of the virus have emerged, reducing the effectiveness of the initial vaccinations. To address this, the government introduced a booster vaccination program aimed at enhancing immunity by up to 80%. The government's plan for booster vaccination has received both positive and negative opinions from the public through various media platforms, including Twitter. This study analyzes public opinions on the booster vaccination plan into three classes: positive, negative, and neutral. SVM is a classification method in machine learning categorized as supervised learning, which involves finding an optimal line (hyperplane) as a separator for two different data classes. The stages of this research include data collection, data cleaning, data transformation, and data classification using the Support Vector Machine (SVM) method. The results of this study indicate that the accuracy of the SVM model reaches 80.42%.
Optimizing Traffic Congestion in Route Planning Using a Simple Path Algorithm Kuswandi, Brillian Adhiyaksa; Hamami, Faqih; Fa’rifah, Riska Yanu
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i4.15352

Abstract

This essay examines the various learning styles that students can choose from, depending on their preferences. In the COVID-19 era, lectures have been discontinued in classrooms all across the world, but the teaching and learning process is still possible through online platforms. There are learning types with unique characteristics that like to work alone or in groups, as well as visual, auditory, tactile, and kinesthetic learning styles. While some students will adjust to the lecturers' teaching approach, it can be challenging for lecturers to accommodate each student's unique learning preferences. In order to accommodate various student learning styles, lecturers must create their instructional materials in this manner. This article's goals are to: 1) describe and classify the idea of learning styles; 2) emphasize the significance of determining the research participants' preferred learning styles; and 3) emphasize that if a lecturer's teaching style reflects the preferences of the student's preferred learning style, the student's learning outcomes will be enhanced. In this study, a survey, a mix of quantitative and qualitative approaches, as well as questionnaires, are used to gather data on the four preferred learning styles. As a consequence, the majority of participants favored the kinesthetic learning strategy in both solo and group work. In this study, a survey, a mix of quantitative and qualitative approaches, as well as questionnaires, are used to gather data on the four preferred learning styles. As a consequence, the majority of participants favored the kinesthetic learning strategy in both solo and group work.
Co-Authors Fa'rifah, Riska Yanu Faqih Hamami Maulana, Fakhri Hassan Rahmat Fauzi Ramli, Muhammad Ayyub