PENERAPAN KNNIMPUTER DALAM MENGOLAH DATA MISSING VALUE UNTUK MEMBANTU MENINGKATKAN AKURASI SUPPORT VECTOR MACHINE KLASIFIKASI PENYAKIT TIROID - Supardianto; Lalu Mutawalli; Wafiah Murniati (Jurnal Informatika Teknologi dan Sains, 2022)
Thyroid is a condition of abnormalities in a person due to thyroid disorders. Based on data from the ministry of health in the world, the prevalence of thyroid is still relatively high, if five million babies are born each year, then there are one thousand six hundred babies with hyperthyroidism. The algorithm used for data processing and modeled into knowledge is a support vector machine (SVM), SVM is used for classification. After exploring the datasets of the 23 attributes contained in the datasets, there are 9 attributes that have missing values, including age 4 lines, sex 307 lines, TSH 804 lines, T3 2604 lines, TT4 442 lines, T4U 809 lines, FTI 802 lines, TBG 8823 lines, and target 1626 lines. Based on the evaluation results on the model that has been tested for precision 94%, recall 100%, F1-score 97% with an accumulated accuracy of 93%. The overall total evaluation on the model is 93%.