Klasifikasi Risiko Hipertensi Menggunakan Metode Learning Vector Quantization (LVQ) - Ivan Agustinus; Edy Santoso; Bayu Rahayudi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Hypertension is one of the health problems globally and perceived by the world community. From various surveys conducted, the number of cases of hypertension that occur each year will continue to grow and the number of deaths caused by hypertension also increases. This study attempts to classify hypertensive diseases. In this study using patient data of hypertension disease by divided into 4 classes. Classification method used in this research is Learning Vector Quantization. Data in the form of weight will be entered into the database system for further classification process with LVQ. Weight obtained from medical records of hypertensive patients, This study uses 12 features. This study used 6 test scenarios that resulted in recommendation of value of learning rate 0.1, multiplier learning rate 0.2, training data as much as 50%, alpha minimum 0.001, maximum iteration of 6 and train data used in the sequence of initial id. The result of accuracy obtained is 93.841%