https://talenta.usu.ac.id/jormtt/issue/feed 2024-03-10T09:26:51+07:00 Elvina Herawati jormtt@usu.ac.id Open Journal Systems <p class="western" lang="en-US" align="justify">Journal of Research in Mathematics Trends and Technology (JoRMTT) is an international journal, open access which provides advance forum and focused to study in every aspect of pure mathematics and its application. Besides, JoRMTT also publishes real time articles survey, recently trends, new theoretical techniques, new ideas, and mathematical tools in whole branches of mathematics. One of the purpose is to reflect research progress in Indonesia and by providing international forum, to stimulate future progress.</p> <p class="western" lang="en-US" align="justify">Every paper will be published by TALENTA Publisher under management of Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Sumatera Utara. The frequency of the publishing are twice in a year which are on <strong>March</strong> and <strong>September</strong>.</p> <p class="western" lang="en-US" align="justify"><a title="SINTA 4" href="https://sinta3.kemdikbud.go.id/journals/profile/8414" target="_blank" rel="noopener"><img src="https://talenta.usu.ac.id/public/site/images/indra/sinta-4.jpg" alt="" width="190" height="55" /></a></p> https://talenta.usu.ac.id/jormtt/article/view/15864 2024-03-10T09:26:51+07:00 Debora Chrisinta deborachrisinta@unimor.ac.id Justin Eduardo Simarmata justinesimarmata@unimor.ac.id

<p>This study addresses the challenge of sentiment analysis within the Information Technology study program at Universitas Timor, aiming to compare the performance of Support Vector Machines (SVM) and Naive Bayes (NB) through 100 iterations. The dataset, comprising 21 instances of negative sentiment and 18 instances of positive sentiment, is analyzed using both methods, with accuracy and Area Under the ROC Curve (AUC) serving as key metrics. The sample size consists of 39 instances, and the results indicate significant variability in both accuracy and AUC, emphasizing the sensitivity of the models to dataset characteristics and random initialization. On average, SVM outperforms NB, with an accuracy of 0.5846 compared to 0.5075 and an AUC of 0.5916 compared to 0.4607.</p>

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