Artificial Neural Network for Classification of Dengue Fever Using Backpropagation Algorithm
Keywords:Artificial Neural Networks, Classification, Dengue Fever, Backpropagation, Visual Basic.
Fever is an increase in body temperature to higher than usual. Normal human body temperature is at 37oC, if the body temperature is more than this figure, it indicates a fever caused by infectious or non-infectious factors. The main symptom of Dengue hemorrhagic fever is high fever with a temperature between 30oC - 40oC which appears suddenly, the fever lasts for 7 days and occurs continuously, body temperature can be normal or low, then will rise slowly every day and can reach 40oC . These two diseases are still a public health problem in urban areas, including in the cities of Binjai and Medan. The problem that has occurred so far is that people in general cannot differentiate the symptoms of Dengue Fever from Malaria, so the treatment given only provides ordinary fever medicine, so that within three days there is no change and the high body temperature makes the patient know that someone has dengue fever. Therefore, the solution provided in this research is to find out the physical characteristics experienced by the sufferer before further diagnosis is carried out. If someone has a fever above 38oC, the body has red spots, irregular breathing, immediately go to the doctor because these symptoms indicate symptoms of dengue hemorrhagic fever or malaria fever. Artificial neural networks are an information processing system designed to imitate the workings of the human brain by carrying out a learning process through changing the weights of synapses. The human brain consists of millions of interconnected neurons known as biological neurons. Each neuron consists of a cell that has a number of dendrites (input) and an axon (output). Axons connect to other neurons through connecting pathways that produce chemical reactions when responding to incoming input. The input required includes the number of input variables, input variable values, weights, learning rate, threshold, maximum epoh and target (output) with the error value classification used is Mean Absolute Error (MAE), there are 2 types of disease with fever symptoms used. The types of disease are dengue hemorrhagic fever and malaria and the system will be designed using the Visual Basic 2010 programming language. From the results of the research that has been carried out, classification results are obtained with a value of 0.893619481 or rounded to equal 1 and classified as dengue hemorrhagic fever.
Agnesia, Y., Nopianto, Sari, S. W., & Ramadhani, D. W. (2023). Demam Berdarah Dengue (DBD) : Determinan & Pencegahan. In NEM.
Damanik, E. H., Irawan, E., & Rizki, F. (2021). Jaringan Syaraf Tiruan Untuk Memprediksi Nilai Siswa SMA Menggunakan Backpropagation. Jurnal Sistem Informasi Dan Ilmu Komputer Prima(JUSIKOM PRIMA), 4(2), 1–7. https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v4i2.1500
Dewi, D. A. I. P., Lely, A. A. O., & Aryastuti, S. A. (2023). Gambaran Faktor Risiko Penyakit Demam Berdarah Dengue pada Anak di Wilayah Kerja Puskesmas Tabanan I. Aesculapius Medical Journal, 3(1).
Fajarwati, E., Nurvinanda, R., & Mardiana, N. (2023). Pengaruh Pemberian Terapi Tepid Sponge Water untuk Mengatasi Hipertermi pada Pasien Demam Berdarah Dengue. Jurnal Penelitian Perawat Profesional, 5(2). https://doi.org/10.37287/jppp.v5i2.1542
Ramli, Nurhayati, & Saragih, R. (2021). Jaringan Syaraf Tiruan Memprediksi Kebutuhan Alat Suntik Medis Dirumah Sakit Menggunakan Backpropagation, (Studi Kasus : RSU Bathesda). JIKSTRA, 3(1).
Rohayani, H., Josh, J., Choirul Umam Fakultas Sains Dan Teknologi, M., Muhammadiyah Jambi, U., Jl Kapten Pattimura, J., Sipin, S. I., Telanaipura, K., & Jambi, K. (2022). Prediksi Penentuan Program Studi Berdasarkan Nilai Siswa dengan Metode Backpropagation. Journal of Information System Research, 3(4), 122–132.
Siregar, A. C., & Octariadi, B. C. (2021). Perbandingan Metode Jaringan Syaraf Tiruan Pada Klasifikasi Motif Kain Tenun Sambas. CYBERNETICS, 4(02). https://doi.org/10.29406/cbn.v4i02.2489
Yuniati, F. (2021). Aplikasi Jaringan Syaraf Tiruan Untuk Memprediksi Prestasi Siswa SMU Dengan Metode Backpropagation. Universitas Islam Negeri Sunan Kalijaga, 6(1), 1–9.
How to Cite
Copyright (c) 2023 Journal of Artificial Intelligence and Engineering Applications (JAIEA)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.