Optimasi K-Nearest Neighbor Menggunakan Bat Algorithm Untuk Klasifikasi Penyakit Ginjal Kronis - Komang Anggada Sugiarta; Imam Cholissodin; Edy Santoso (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Chronic Kidney Disease (CKD) is deadly disease and need high cost to do hemodialysis every week. CKD can happen because acute kidney disease for long time and not do any further treatment or change lifestyle to prevent CKD. So, people need to know is that potentially suffer CKD or not for early time. One of ways to know people suffer CKD is compare pattern with people that already suffer CKD. K-Nearest Neighbor is method that can to compare and classify data to nearest similarity of CKD's data. However, CKD has many feature that can use to classify people that suffer CKD. That feature can be people health data and lifestyle. That many feature must choose only most effective feature for classify CKD and discard the other feature. The feature selected can improve KNN to better classify CKD. KNN can know the better feature with try every combination feature. If KNN do that, it will need long time to find it. KNN need to improve efficiently and short way to know most effective feature for CKD. Bat Algorithm (BA) is method that can search solution that imitate behavior of bat. BA-KNN or combination BA and KNN proposed to solve the problem.