Open AccessDiagnosa Penyakit Demam Berdarah Dengue (DBD) menggunakan Metode Learning Vector Quantization (LVQ)
Author(s) -
Firman Tawakal,
Ahmedika Azkiya
Publication year - 2020
Publication title -
jiska (jurnal informatika sunan kalijaga)
Language(s) - English
Resource type - Journals
eISSN - 2528-0074
pISSN - 2527-5836
DOI - 10.14421/jiska.2020.43-07
Subject(s) - learning vector quantization , dengue fever , dengue hemorrhagic fever , aedes aegypti , medicine , artificial intelligence , artificial neural network , dengue virus , virology , computer science , biology , botany , larva
Dengue Hemorrhagic Fever is a disease that is carried and transmitted through the mosquito Aedes aegypti and Aedes albopictus which is commonly found in tropical and subtropical regions such as in Indonesia to Northern Australia. in 2013 there are 2.35 million reported cases, which is 37,687 case is heavy cases of DHF. DHF’s symthoms have a similarity with typhoid fever, it often occur wrong handling. Therefore we need a system that is able to diagnose the disease suffered by patients, so that they can recognize whether the patient has DHF or Typhoid. The system will be built using Neural Network Learning Vector Quantization (LVQ) based on the best training results. This research is to diagnose Dengue Hemorrhagic Fever using LVQ with input parameters are hemoglobin, leukocytes, platelets, and heritrocytes. Based on result, the best accuracy is 97,14% with Mean Square Error (MSE) is 0.028571 with 84 train data and 36 test data. Conclution from the research is LVQ method can diagnose DHF Keywords: Dengue Hemorrhagic Fever; Learning Vector Quantization; classification; Neural Network;