Implementasi Metode Learning Vector Quantization Untuk Klasifikasi Penyakit Demam - Nurhidayati Desiani; Lailil Muflikhah; Candra Dewi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Fever is an early symptom of various diseases that have been experienced by almost everyone. Some of the diseases include typhoid fever, malarial fever and dengue fever. These three diseases have similar early symptoms. Similar symptoms of each disease often cause difficulty in obtaining anamnese (temporary diagnosis) so that patients get the initial handling is less precise and further worsen the condition of the patient. To overcome this required a system that can facilitate in identifying the disease based on the symptoms felt by the patient. In this study using Learning Vector Quantization method which is a method of classification. The system works with the training and testing phases that will result in classes of typhoid fever classes, malarial fever and dengue fever. The parameters used are 15 parameters of symptoms of febrile illness. The best average accuracy result is 100% using comparison of test data and training data of 10:90, learning rate 0,1, learning rate reduction constant 0,1, minimum learning rate 10-5, and maximum number of iteration 10.