Abstrak
This study aims to conduct mining using webtechnology to collect information data collected from Webof Science and SINTA as many as 20 each collected from 2018 to 2019. The Cross-Industrial Standard Process Methodology for data mining (CRISP-DM) is used as standard data mining processes and research methods. This research addresses research topics in the journal Webof Science and research which is accompanied by research trends and the most emerging are learning, networking, analysis, systems, control, data, images, optimization, systems, and nerves. The classification process with Generalized Linear Model and Decision Tree models has an accuracy of 94.3%, while Gradient Boosted Trees has an accuracy percentage of 93.8%. Naive Bayes shows an accuracy rate of 91.4%, followed by Fast Large Margins, Deep Learning, Random Forest, and Support Vector Machine models that have an accuracy of 91.4%.