Fever Classification Using the Neighbor Weighted K-Nearest Neighbor Method - Surayya Safira Milania; Cucu Suhery; Tedy Rismawan (CESS (Journal of Computer Engineering, System and Science), 2023)
Demam merupakan gejala atau reaksi tubuh terhadap suatu infeksi atau penyakit. Demam dapat disebabkan karena adanya infeksi virus, bakteri, dan parasit. Serta demam akibat gigitan nyamuk. Beberapa penyakit penyebab demam yang perlu diwaspadai antara lain Demam Berdarah Dengue (DBD), Demam Tifoid, dan Malaria dikarenakan gejala klinis dari ketiga penyakit tersebut sangat mirip dan sulit untuk dibedakan. Akibat dari gejala yang mirip, seringkali menyebabkan kesulitan dalam mendapatkan diagnosis awal sehingga kurang tepat dalam penanganan. Oleh karena itu, pada penelitian ini dibangun sebuah sistem yang dapat mengklasifikasikan demam menggunakan metode Neighbor Weighted K-Nearest Neighbor. Data yang digunakan berjumlah 300 data dengan komposisi rasio data latih dan data uji sebesar 70%:30% sehingga data latih yang digunakan berjumlah 210 data dan data uji berjumlah 90 data. Penelitian ini dilakukan dengan mengamati variasi nilai ketetanggaan (K) dan nilai exp (E) terhadap akurasi sistem klasifikasi demam. Hasil pelatihan menunjukkan bahwa nilai K dan E yang bervariasi tidak mempunyai pengaruh terhadap akurasi tersebut. Hasil pengujian yang dilakukan mendapatkan akurasi sebesar 100% pada setiap variasi nilai K dan E.Fever is a symptom of the body's reaction to an infection or disease. Fever can be caused by viral, bacterial, or parasitic infections. as well as fever due to mosquito bites. Several diseases that cause fever that need to be watched out for include dengue hemorrhagic fever (DHF), typhoid fever, and malaria because the clinical symptoms of these three diseases are very similar and difficult to distinguish. As a result of similar symptoms, it often causes difficulties in getting an early diagnosis, so treatment is not appropriate. Therefore, in this study, a system was developed that could classify fever using the neighbor weighted K-nearest neighbor method. The data used totaled 300, with a composition ratio of 70% training data to 30% test data, for a total of 210 training data and 90 test data. This research was conducted by observing the variation in the value of neighborliness (K) and the value of exp (E) on the accuracy of the fever classification system. The results of the training show that the varying K and E values have no effect on accuracy. The results of the tests carried out obtained an accuracy of 100% for each variation in the values of K and E.Â