<![CDATA[Cervical cancer is the second most common cancer in Indonesia after breast cancer. The number of deaths from cervical cancer in Indonesia continues to increase every year due to delays in making diagnoses and examinations. To detect cervical cancer, a laboratory examination using Visual Inspection with Acetic Acid (IVA) or pap smears is needed which requires specialist internal medicine and several considerations of features to get accurate diagnosis. Sometimes, how to analyze features by doctor with one another produces different results. Therefore, a classification process is needed to make a diagnosis of cervical cancer with high accuracy results so that it is expected to be able to match the diagnosis results of medical personnel. This study uses cervical cancer risk classification data with feature selection based on expert interviews. This study uses the Extreme Learning Machine algorithm to carry out the classification process and measure the results of algorithm performance with accuracy values ​​from the calculation of confusion matrix. Based on the test results obtained the optimal parameters are as many as 11 hidden neurons, the activation function is binary sigmoid, and the fold on training and testing data is fold 1st which produces an accuracy of 91.76%.]]>
Copyrights © 2019
