SERVICES CANCER DETECTION SYSTEM USING K-NEAREST NEIGHBOURS(K-NN) METHOD AND NAÏVE BAYES CLASSIFIER

• Main Authors: Sri Rezeki Candra Nursari, Nanda Mahya Barokatun Nisa
• Format: Article
• Language: English
• Published: STMIK Pringsewu 2020-04-01
• Series: IJISCS (International Journal of Information System and Computer Science)
• Online Access: http://ojs.stmikpringsewu.ac.id/index.php/ijiscs/article/view/893

According to WHO (World Health Organization) data, every 2 minutes a woman dies. In Indonesia alone, 40 - 45 women are diagnosed with cervical cancer every day. Of those diagnosed, around 20-25 die from cervical cancer. About 95% more cervical cancer is caused by infection with the HPV virus or the human papilloma virus and an estimated death rate reaches 270,000 deaths each year. Cervical cancer occupies the third rank type of cancer in the world after breast and lung cancer, because the symptoms are not very visible at an early stage, so it is referred to as "Silent Killer". Based on the data and cases above, the latest technology that is able to detect cervical cancer in order to speed up the detection process for someone to be quickly treated is an artificial intelligence application that serves to detect whether someone should run 4 cervical cancer testing techniques, namely Hinselmann, Schiller, Citology, and biopsy with K-nearest neighbors algorithm and Naive Bayes classifier is one of the latest technologies that can facilitate the work of a doctor and speed up the process of detecting someone whether to run 4 testing techniques or not. The correct amount of data classified by the K-Nearest Neighbors method is 558 data from 858 data. The classification accuracy of the Naïve Bayes method is 84.7%. The correct amount of data classified by the Naïve Bayes method is 558 data from 858 data. The classification accuracy of the Naïve Bayes method is 84%.