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All Journal SINTECH (Science and Information Technology) Journal STRING (Satuan Tulisan Riset dan Inovasi Teknologi)
Nur Arifin
Universitas Singaperbangsa Karawang
Computer Science & IT Mathematics

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PERBANDINGAN KERNEL SUPPORT VECTOR MACHINE (SVM) DALAM PENERAPAN ANALISIS SENTIMEN VAKSINISASI COVID-19 Thalita Meisya Permata Aulia; Nur Arifin; Rini Mayasari
SINTECH (Science and Information Technology) Journal Vol. 4 No. 2 (2021): SINTECH Journal Edition Oktober 2021
Publisher : LPPM STMIK STIKOM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v4i2.762

Abstract

In early 2020, the first recorded death from the COVID-19 virus in China [3]. Followed by WHO which later stated that the COVID-19 virus caused a pandemic. Various efforts were made to minimize the transmission of COVID-19, such as physical distancing and large-scale social circulation. However, this resulted in a paralyzed economy, many factories or business shops closed, eliminating the livelihoods of many people. Vaccines may be a solution, various International Research Communities have conducted research on the COVID-19 vaccine. In early 2021 the Sinovac vaccine from China arrived in Indonesia and was declared a BPOM clinical trial, but the existence of the vaccine still raises pros and cons, some have responded well and others have not. For this reason, a sentiment analysis of the COVID-19 vaccine will be carried out by taking data from Twitter, then classified using the Support Vector Machine algorithm. The research data is nonlinear data so it requires a kernel space for the text mining process, while there has been no specific research regarding which kernel is good for sentiment analysis, so a test will be carried out to find the best kernel among linear, sigmoid, polynomial, and RBF kernels. The result is that sigmoid and linear kernels have a better value, namely 0.87 compared to RBF and polynomial, namely 0.86
Penerapan Algoritma Support Vector Machine (SVM) dengan TF-IDF N-Gram untuk Text Classification Nur Arifin; Ultach Enri; Nina Sulistiyowati
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 6, No 2 (2021)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1003.319 KB) | DOI: 10.30998/string.v6i2.10133

Abstract

Syntax Journal of Informatics is an information system that contains a collection of scientific articles managed by the Informatics Study Program of Singaperbangsa Karawang University. Currently, Syntax Journal of Informatics does not have a feature for categorizing scientific articles based on their focus and scope. The research is conducted to classify scientific articles into categories according to focus and scope contained on Syntax Journal of Informatics’ page automatically by utilizing the text mining process. Text mining is a process that aims to get important information from the text. The method used in the research is Knowledge Discovery in Database (KDD) with stages of data selection, preprocessing, transformation, modeling and evaluation. This study will compare the classifications based on the title of the article. The algorithm used is the Support Vector Machine (SVM) using four SVM kernels, including the linear kernel, polynomial kernel, sigmoid kernel and RBF kernel. Data are divided into four scenarios by using traintestsplit, namely 60:40, 70:30, 80:30 and 90:10. The results of the study after testing the model are measured by of Accuracy, Precision, Recall and F-measure. The best results are accuracy of 70%, precision of 75%, recall of 69% and f-measure of 71% in the 90:10 comparison scenario and linear kernel.
Co-Authors Nina Sulistiyowati Rini Mayasari Thalita Meisya Permata Aulia Ultach Enri