DOI: https://doi.org/10.14710/j.gauss.15.1.255-263

IMPLEMENTASI ALGORITMA FUZZY K-NEAREST NEIGHBOR UNTUK KLASIFIKASI PENYAKIT DIARE

*Nur Dihyah  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Budi Warsito  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Iut Tri Utami  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2026 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

Diarrhea is digestive disruption retrieved by defecation that become more fluid and occur over three times a day. The prevalence of diarrhea in Indonesia is public health problem with high cases. Diarrhea management is carried out with rehydration efforts by administering oral rehydration salts at puskesmas. Diarrhea is classified into 2 types, namely acute diarrhea (mild) and chronic diarrhea (severe). Puskesmas only handles mild diarrhea so that a method is needed to classify diagnosis of diarrhea that occurs at puskesmas appropriately so that diagnosis of acute diarrhea is not misclassified into chronic diarrhea. This research implements Fuzzy K-Nearest Neighbor procedure for Diarrhea Classification. Fuzzy K-Nearest Neighbor incorporates fuzzy logic and K-Nearest Neighbor in the classification practice. The advantage of Fuzzy K-Nearest Neighbor is data will have membership value in each data class so that it further strengthens reason for data to enter predicted class. Data is processed by applying Shiny Package in Rstudio to create Graphical User Interface (GUI-R) so that it makes it easier for researchers to process data. The results obtained highest classification accuracy at K = 3 with accuracy of 80.19% and specificity of 85.45% so that Fuzzy K-Nearest Neighbor was able to classify diarrhea well.

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IMPLEMENTASI ALGORITMA FUZZY K-NEAREST NEIGHBOR UNTUK KLASIFIKASI PENYAKIT DIARE (STUDI KASUS: PUSKESMAS SRONDOL PERIODE 1 JANUARI - 31 DESEMBER 2022)
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Keywords: Diarrhea; Data Mining; Classification; Fuzzy K-Nearest Neighbor

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  1. Han, J., Kamber, M., dan Pei, J. (2011). Data Mining: Concepts and Techniques 3rd Edition. Waltham: Morgan Kaufmann
  2. Hidayat, W., Ardiansyah, M., dan Setyanto, A. (2021). Pengaruh Algoritma ADASYN dan SMOTE terhadap Performa Support Vector Machine pada Ketidakseimbangan Dataset Airbnb. Jurnal Pendidikan Informatika, Vol. 5, No. 1: 11-20
  3. Indriani, A. (2014). Klasifikasi Data Forum dengan Menggnakan Metode Naive Bayes Classifier. Seminar Nasional Aplikasi Teknologi Informasi, Vol. 1, No. 1: 5-10
  4. Irwan. (2017). Epidemiologi Penyakit Menular. Yogyakarta: CV. Absolute Media
  5. Li, D., Deogun, J. S., dan Wang, K. (2007). Gene Function Classification Using Fuzzy K-Nearest Neighbor Approach. 644-647
  6. Nugraha, S. D., Putri, R. R., dan Wihandika, R. C. (2017). Penerapan Fuzzy K-Nearest Neighbor (FK-NN) Dalam Menentukan Status Gizi Balita. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, Vol. 1, No. 9: 925-932
  7. Prasetyo, E. (2012). Data Mining: Konsep dan Aplikasi Menggunakan Matlab. Yogyakarta: Penerbit Andi
  8. Prasetyo, E. (2014). Data Mining: Mengolah Data menjadi Informasi Menggunakan MATLAB. Yogyakarta: Penerbit Andi
  9. Qadrini, L., Seppewali, A., & Aina, A. (2021). Decision Tree dan Adaboost pada Klasifikasi Penerima Program Bantuan Sosial. Jurnal Inovasi Penelitian, Vol. 2, No. 7: 1959-1966
  10. Setiadi. (2013). Konsep dan Praktik Penulisan Keperawatan. Yogyakarta: Graha Ilmu

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