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All Journal Journal of Computer System and Informatics (JoSYC) Bigint Computing Journal
Purba, Ony Hizri Kaifa
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Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Journal : Journal of Computer System and Informatics (JoSYC)

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Sentiment Analysis Study Tour Bus Ban on Twitter Using Support Vector Machine Method Purba, Ony Hizri Kaifa; Zufria, Ilka
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5726

Abstract

Study tour is an activity outside the classroom that has the purpose of learning about the process of something directly. This activity is usually carried out by the school once a year. This activity is not only a learning tool for students, but also a recreational activity.In this activity, there are many things that need to be prepared, such as transportation, lodging, meals, and so on. This is sometimes troublesome, because not all tourists or business people have the time and willingness to prepare it. Therefore, they need services during their trip. Especially now that it is even semester, where every school usually holds a study tour, as well as a final class farewell. As a response to concerns, some parents may choose to find alternative activities that are considered safer for their children, such as joining activities in the city or at school. Based on this need, it makes opportunities for business people engaged in the tour agency industry. SVM (Support Vector Machine) is a machine learning method that works on the principle of Structural Risk Minimization (SRM) with the aim of finding the best hyperlane separating two classes in the input space. Simply put, SVM (Support Vector Machine) has the concept of finding the best hyperlane, which serves as the boundary of two classes The results of sentiment classification on Study Tour Buses using the Support Vector Machine algorithm that matches the actual data amount to 176 data out of a total of 240 test data. It is known that of the 1200 data obtained regarding sentiment towards there are 519 reviews that are positive and 681 reviews that are negative.The accuracy value of the Study Tour Bus sentiment classification using the Support Vector Machine (SVM) algorithm obtained is 73%.
Co-Authors Ilka Zufria Julianti, Miranda Sephia, Putri Aisyah