TY  - JOUR
AU  - Nurhafifah Matondang
AU  - Nico Surantha
TI  - Effects of Oversampling SMOTE in the Classification of Hypertensive Dataset
JO  - Advances in Science, Technology and Engineering Systems Journal
PY  - 2020
VL  - 5
IS  - 4
SP  - 432
EP  - 437
DO  - 10.25046/aj050451
UR  - https://www.astesj.com/v05/i04/p51/
L1  - https://www.astesj.com/?sdm_process_download=1&download_id=14288
AB  - <div class="wpb_text_column wpb_content_element"> <div class="wpb_wrapper"> <p style="text-align: justify;">Hypertensive or high blood pressure is a medical condition that can be driven by several factors. These factors or variables are needed to build a classification model of the hypertension dataset. In the construction of classification models, class imbalance problems are often found due to oversampling. This research aims to obtain the best classification model by implementing the Support Vector Machine (SVM) method to get the optimal level of accuracy. The dataset consists of 8 features and a label with two classes: hypertensive and non-hypertensive. Overall test result performance is then compared to assess between SVM combined with SMOTE and not. The results show that SMOTE can improve the accuracy of the model for unbalanced data into 98% accuracy compared to 91% accuracy without SMOTE.</p> </div> </div> <div class="vc_separator wpb_content_element vc_separator_align_center vc_sep_width_100 vc_sep_pos_align_center vc_separator_no_text vc_sep_color_grey wpb_content_element wpb_content_element"></div>
KW  - Synthetic Minority Oversampling Technique
KW  - Support Vector Machine
KW  - Hypertensive
ER  -