KLASIFIKASI JENIS PENYAKIT KATARAK MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR - Rahmadayanti, Fitria; Angraini, Inda; ., Sulaiman (JUTIM (Jurnal Teknik Informatika Musirawas), 2025)
The purpose of this study is to assist the Besemah City Hospital in Pagar Alam in classifying cataract data. The large number of cataract sufferers causes the eye polyclinic to collect data randomly and so that it is incomplete in submitting reports, with the classification of cataract disease or data grouping to make it easier for the polyclinic to find out which cataract disease groups are the highest based on their causes and to make it easier for the eye polyclinic to collect reports. The study applies the K-Nearest Neighbor (KNN) method as an algorithm for cataract data classification. The development method used is CRIPS-DM consisting of six phases, namely the business understanding process, data understanding, data preparation, modeling, evaluation and Deployment. There are 842 data from the results of the classification of cataract disease types using the KNN algorithm which were tested with rapid miner types of unsficified cataract disease 390, senile nuclear cataract 43, senile incifient cataract 196, unscified cataract 39, other cataracts 1, complicated cataract 67, senile cataract 11, senile cataract morgagnian type 0, invatile juvenile 4, traumatic cataract 0, and the results of the rapid miner accuracy of 89.19% performance vector.