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Riyant Fajar
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Implementasi Metode Naive Bayes Dengan Perbaikan Missing Value Menggunakan Metode Nearest Neighbor Imputation Studi Kasus: Penyakit Malaria Di Kabupaten Malang Riyant Fajar; Rizal Setya Perdana; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Malaria is an infectious disease that is transmitted among humans by the bites of female Anopheles mosquit. There are four types of Plasmodium that are frequently found in the case of malarial infection in Indonesia: Plasmodium vivax (Tertiana), Plasmodium malariae (Quartana), Plasmodium falcifarum (Tropica), and Plasmodium ovale (Pernisiosia). Thus far, people are having difficulty in differentiating the symptoms found in malaria and in another common cold or influenza as the laymen rely only on general knowledge without any medical facts and reviews. As a result, the patient of malaria is often mistreated. The symptoms of malaria depend on the types of malaria itself. Classic symptoms of malaria suffered by non-immune patients (patients who live in non-endemic area) are paroxysmal (sudden acute fever) preceded by chills and oversweating. On the other hand, classic symptoms of malaria suffered by immune patients are headache, nausea and vomitting, diarrhea , as well as muscle pain. Malaria is a life-threatening disease that can lead into death if not treated in an immediate manner. On that account, a computer system that can accelerate the detection is needed to help in diagnosing whether or not the patient is infected. The said system was designed using Naive Bayes method and the improvement of missing value with the usage of nearest neighbor imputation method. The verdict of the system's accurateness from two testing scenarios has been acquired with the best accuracy point of 77.14% in the first testing scenario and 64.70% in the second testing scenario.
Co-Authors Indriati Indriati Rizal Setya Perdana