Analisis Perbandingan Pengukuran Jarak Algoritma K-Nearest Neighbor Dengan Menggunakan Data Breast Cancer Dan Data Heart Disease

Herdiesel Santoso; Linda Pratiwi

doi:10.30989/ijds.v1i2.1200

Herdiesel Santoso STMIK El Rahma
Linda Pratiwi STMIK El Rahma

DOI: https://doi.org/10.30989/ijds.v1i2.1200

Keywords: Breast Cancer, Data Mining, Heart Disease, K-Nearest Neighbor

Abstract

Breast Cancer is a cancerous condition that appears in the breast area. This type of cancer is often experienced by women with a characteristic feature of Breast Cancer, namely the appearance of unusual lumps in the breast area. Heart or Heart Disease is a type of Non-Communicable Disease (PTM): which results in a fairly high mortality rate. Heart Disease is caused by several risk factors including smoking, an unhealthy lifestyle, high cholesterol, hypertension, and diabetes.

Based on these facts, an appropriate algorithm is needed to classify Breast Caner and Heart Disease as an effort to prevent an increase in mortality rates due to Breast Cancer and Heart Disease. And the algorithm that will be used is the K-Nearest Neighbor algorithm with 3 distance measurement methods, namely Euclidean distance, Manhattan distance, and Minkowsky distance .

From the stages that have been carried out, the final results of the Euclidean distance method obtained an Accuracy value of 80.88% Breast Cancer data at K = 11, and 78.69% heart Disease data at K = 11. The Manhattan distance method obtained an Accuracy value of 89.71% of Breast Cancer data on K=11, and 78.69% of Heart Disease data on K=20.The Minkowsky distance method obtained an Accuracy value of 98.53% of Breast Cancer data on K=11, and 79.41% of Heart Disease data on K=11. This shows that the Minkowsky distance method works more optimally than the Euclidean distance and Manhattan distance methods.

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