[vc_empty_space][vc_empty_space]
Design of Istitaah Classification System Based on Machine Learning Using Imbalanced Dataset
Huda N.M.a, Albardaa
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.Hajj is an obligatory activity for every Muslim who fulfills the requirements. One of the capable (istitaah) criteria is the health of a prospective pilgrim. Consequently, a health assessment is critical to ensure such a pilgrim can worship without any obstacles and comply with the sharia law. This study aims to design a classification system for istitaah based on machine learning technique by utilizing Electronic Health Records (EHR) of the prospective pilgrims. The EHR data was collected from prospective pilgrims in Purworejo District and used for training and testing purposes. The supervised machine learning method was leveraged for data analysis. This study describes how to handle the preprocessing stage of data imbalance among the classes to improve the accuracy of results. Predictive analysis from imbalanced data might generate bias decisions on majority or minority classes during the learning process if data are not properly handled. This paper studied two approaches to handling imbalanced data distribution including Synthetic Minority Over-sampling Technique (SMOTE) based on data approach and algorithmic approach by comparing AdaBoost, Neural Network, Random Forest, and Decision Tree algorithm. The combination of SMOTE and Neural Network showed the result more accurate in this case.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Classification system,Decision-tree algorithm,Electronic health record,Imbalanced data,istitaah,SMOTE,Supervised machine learning,Synthetic minority over-sampling techniques[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]classification,imbalanced data,istitaah,machine learning,SMOTE[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/CITSM47753.2019.8965414[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]