Penerapan Algoritme Fuzzy K-Nearest Neighbor (FK-NN) Pada Pengklasifikasian Penyakit Kejiwaan Skizofrenia

Sianturi, Tania Oka (2018) Penerapan Algoritme Fuzzy K-Nearest Neighbor (FK-NN) Pada Pengklasifikasian Penyakit Kejiwaan Skizofrenia. Sarjana thesis, Universitas Brawijaya.

Abstract

Salah satu penyakit kejiwaan yang banyak menyerang penduduk Indonesia adalah penyakit kejiwaan Skizofrenia. Skizofrenia menyebabkan seseorang mengalami delusi, halusinasi, pikiran kacau, dan perubahan terhadap perilaku. Menurut Riset Kesehatan Dasar (Riskesdas) pada tahun 2013, prevalensi Skizofrenia sebesar 1,7 per 1000 penduduk atau sekitar 400.000 orang. Untuk wilayah Indonesia yang sangat luas dengan jumlah penduduk sekitar 237 juta, jumlah ahli jiwa atau psikiater yang dimiliki sekitar 616 orang tergolong masih sangat sedikit. Dengan keterbatasan tersebut diperlukan sebuah sistem yang dapat digunakan untuk membantu paramedis dalam mendiagnosis dan mengklasifikasi penyakit kejiwaan Skizofrenia. Pada penelitian ini diterapkan algoritme fuzzy K-nearest neighbor untuk mendiagnosis dan mengklasifikasi penyakit kejiwaan Skizofrenia. Jenis-jenis penyakit kejiwaan Skizofrenia yang digunakan pada penelitian ini yaitu Skizofrenia paranoid, Skizofrenia hebefrenik, Skizofrenia katatonik, Skizofrenia tak terinci, dan Skizofrenia simpleks. Proses klasifikasi terdiri atas tiga proses yaitu proses inisialisasi fuzzy, proses algoritme K-nearest neighbor, dan proses algoritme fuzzy K-nearest neighbor. Untuk menguji sistem, maka dilakukan pengujian terhadap nilai K dan pengujian terhadap K-Fold. Berdasarkan hasil pengujian terhadap nilai K, didapatkan nilai akurasi tertinggi sebesar 38,33% pada nilai K=5. Hasil pengujian pengaruh K-Fold didapatkan rata-rata nilai akurasi tertinggi sebesar 34,17% pada K-Fold=10.

English Abstract

One of the psychiatric diseases that affects many Indonesians is schizophrenia. Schizophrenia causes a person to sustain delusions, hallucinations, chaotic thoughts, and behavioral changes. According to Riset Kesehatan Dasar (Riskesdas) in 2013, prevalence of schizophrenia is 1.7 per 1000 people or about 400,000 people. For very wide territory of Indonesia with total population around 237 million, the number of psychologists or psychiatrists about 616 people is still very small. With this limitation, a system that can be used to assist paramedics in diagnosing and classifying psychiatric illnesses of schizophrenia. In this study applied fuzzy K-nearest neighbor algorithm to diagnose and classify psychiatric illness of schizophrenia. Types of schizophrenia used in this study are paranoid schizophrenia, hebephrenic schizophrenia, catatonic schizophrenia, undifferentiated schizophrenia, and simple schizophrenia. The classification process consists of three processes are the fuzzy initialization process, the K-nearest neighbor algorithm process, and the fuzzy K-nearest neighbor algorithm process. The testing consists of the effect of K value and the effect of K-Fold. Based on the test results on the K value, obtained the highest accuracy of 38.33% at K=5. The effect of K-Fold test results obtained the highest average accuracy of 34.17% at K-Fold= 10.

Item Type: Thesis (Sarjana)
Identification Number: SKR/FTIK/2018/4/051800741
Uncontrolled Keywords: Skizofrenia, Assymetric Binary, K-Fold
Subjects: 000 Computer science, information and general works > 004 Computer science > 004.015 1 Finite mathematic
Divisions: Fakultas Ilmu Komputer > Teknik Informatika
Depositing User: Yusuf Dwi N.
Date Deposited: 07 Jun 2018 06:55
Last Modified: 26 Oct 2021 06:27
URI: http://repository.ub.ac.id/id/eprint/11513
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