Perbandingan Kinerja Algoritma K-Nearest Neighbor dan Algoritma Random Forest Untuk Klasifikasi Data Mining Pada Penyakit Gagal Ginjal

Kidney failure is one of the most common chronic diseases worldwide. This condition occurs when the kidneys lose their ability to filter waste and excess fluid from the blood. Kidney failure is a serious condition that occurs when kidney function decreases significantly or stops altogether. Kidney failure has a wide impact on the physical, mental, and social health of patients. Therefore, early treatment and a holistic approach are needed to minimize its impact. In the health sector, technological advances have enabled more effective processing of medical data through the application of data mining. Data Mining is the process of exploring and analyzing large amounts of data to find patterns, relationships, or valuable information that was previously unknown. Classification in Data Mining is the process of grouping or categorizing data into certain classes or labels based on the attributes or features it has. In the classification itself, there are various algorithms in it such as the K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms. The K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms are two algorithms that are widely used in classification tasks. Therefore, this study will carry out a comparison process on the performance of the K-Nearest Neighbor algorithm and the Random Forest algorithm. Comparison of data mining algorithm performance to evaluate and determine which algorithm is the most effective and efficient in solving a particular problem based on various evaluation metrics. Overall, the accuracy value obtained is above 90%, but the Random Forest algorithm has better performance. Where the accuracy level results obtained from the Random Forest algorithm are 99.75%. Therefore, the model or pattern produced by the Random Forest algorithm will later be used to assist in the process of diagnosing kidney failure and the Random Forest algorithm is an algorithm that has better performance.