Case Based Reasoning Diagnosis Penyakit Difteri dengan Algoritma K-Nearest Neighbor

  • Fatoni C
  • Noviandha F
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Abstract

Akhir tahun 2017, masyarakat Indonesia ramai dengan maraknya kematian pada anakanak dan orang dewasa akibat penyakit Difteri. Ditemukan sebanyak 12 orang meninggal dunia dari 318 kasus Difteri menurut catatan Dinas Kesehatan Jawa Timur. Padahal di tahun 2016 kasus Difteri di Jawa Timur tercatat sebanyak 4 orang meninggal dunia dari 209 kasus. Hal tersebut menjadi perhatian bagi pemerintah dan tercatat sebagai kejadian luar biasa (KLB). Kenaikan angka kasus Difteri ini disebabkan karena kurangnya kesadaran masyarakat akan pentingnya imunisasi. Semakin banyaknya kasus Difteri yang terjadi dan minimnya pengetahuan masyarakat tentang Difteri, maka dibutuhkan suatu sistem pakar yang mampu membantu masyarakat maupun pemerintah dalam mendiagnosis penyakit Difteri. Penelitian mengenai Difteri ini menggunakan metode algoritma K-Nearest Neighbour (K-NN) dimana dilakukan perhitungan similaritas pada kasus lama dengan kasus baru. Penelitian penyakit Difteri ini disempurnakan dengan menggunakan penalaran berbasis kasus atau Cased Based Reasoning (CBR) agar hasil diagnosis lebih akurat. Output dari penelitian ini yaitu berupa hasil diagnosa penyakit Difteri berdasarkan gejala-gejala yang dialami dengan hasil akurasi pengujiannya sebesar 95,17%.End of 2017, the people of Indonesia enlivened so many of deaths in children and adults due to Diphtheria. Found 12 people died from 318 cases of Diphtheria according to East Java Health Office records. Whereas in the year 2016 Diphtheria cases in East Java recorded and reported as many as 4 people died from 209 cases. It's of particular concern to government and is noted as an extraordinary event (KLB). The increase in the number of Diphtheria cases is due to a lack of public awareness of the importance of immunization. Increasing number of Diphtheria cases and the lack of public knowledge about Diphtheria, it needs an expert system capable of assisting the public and the government in diagnosing Diphtheria. This research on Diphtheria uses the K-Nearest Neighbors (K-NN) algorithm method in which a similarity case study in the old case with new cases is used. The research of Diphtheria disease is enhanced by using casebased reasoning or Cased Based Reasoning (CBR) to make the diagnosis more accurate. The output of this research is the result of diagnosis of Diphtheria disease based on the symptoms experienced by the result of the accuracy of the test is 95,17%.

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Fatoni, C. S., & Noviandha, F. D. (2018). Case Based Reasoning Diagnosis Penyakit Difteri dengan Algoritma K-Nearest Neighbor. Creative Information Technology Journal, 4(3), 220. https://doi.org/10.24076/citec.2017v4i3.112

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