Data Clustering Mining Applying the K-Means Algorithm, Cervical Cancer Behavior Risk - Ridha Maya Faza Lubis; Jen-Peng Huang; Pai-Chou Wang; Kiki Khoifin; Mula Sigiro; Joel Panjaitan (JURNAL MEDIA INFORMATIKA BUDIDARMA, 2023)
Nowadays, cancer is often heard as a topic of conversation for both men and women in Indonesia and even in the world, in addition to the symptoms that are not too significant and also the lack of public awareness to carry out periodic health checks, which has a negative impact on health. This lack of care is also caused by several factors, namely the lack of the community's economy, too busy with work (other matters) and even some people are not ready to know and accept the disease they are suffering from. Based on all the factors causing the reluctance of medical examinations, of course, it requires us to carry out examinations so that we can prevent and treat them early if they are diagnosed with certain diseases. There are several cancers with predominant sufferers and even only suffered by women, one of which is cervical cancer. In 2020 it is estimated that cases of cervical cancer will increase by 3.4% from 6.6% in 2018 to 9% and even cervical cancer will also become the third deadly disease in women after breast cancer and lung cancer. From this it can be seen that the percentage of deaths caused by cervical cancer is always increasing. Therefore, to reduce the high mortality rate, a clustering technique was carried out to group the data into their respective clusters based on the similarity of characteristics between one data and another. The algorithm used is K-Means with the rapid miner tester application. The final result obtained is that cluster 1 has more data and it is stated that out of 72 data on Cervical Cancer only 28 are declared as sufferers of Cervical Cancer and 44 other data are not.