Perbandingan Klasifikasi Penyakit Diabetes Menggunakan Algoritma K-Nearest Neighbors (KNN) dan Naïve Bayes: Analisis Performa dan Keakuratan. (Comparison of Diabetes Disease Classification Using K-Nearest Neighbors (KNN) and Naïve Bayes Algorithms: Performance and Accuracy Analysis).

Putra, David Christian (2024) Perbandingan Klasifikasi Penyakit Diabetes Menggunakan Algoritma K-Nearest Neighbors (KNN) dan Naïve Bayes: Analisis Performa dan Keakuratan. (Comparison of Diabetes Disease Classification Using K-Nearest Neighbors (KNN) and Naïve Bayes Algorithms: Performance and Accuracy Analysis). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Diabetes adalah penyakit kronis yang mengganggu metabolisme tubuh dengan tingginya kadar glukosa darah, yang mengakibatkan gangguan respons terhadap insulin. Jumlah penderita diabetes diprediksi mencapai 700 juta orang pada tahun 2045. Selain orang berusia 35 ke atas, anak muda usia 18 tahun ke atas kini juga banyak yang terkena diabetes, terutama karena konsumsi gula yang tinggi. Diabetes dapat menyebabkan komplikasi serius seperti masalah ginjal, amputasi, gangguan penglihatan, kerusakan saraf, serta meningkatkan risiko serangan jantung dan stroke dua hingga tiga kali lipat pada orang dewasa. Deteksi dini melalui pemeriksaan gula darah sangat penting untuk mencegah keparahan penyakit ini. Faktor-faktor yang mempengaruhi diagnosis diabetes meliputi jenis kelamin, umur, tekanan darah, BMI, dan riwayat merokok, hba1c level, dan gulad darah. Data mining memungkinkan penggunaan data historis untuk menemukan pola-pola baru. Algoritma K-Nearest Neighbor (KNN) dan Naïve Bayes adalah dua algoritma data mining yang digunakan untuk klasifikasi. KNN memprediksi kelas data baru berdasarkan kedekatan dengan data pelatihan terdekat, sementara Naïve Bayes menghitung probabilitas berdasarkan teorema Bayes dan menganggap atribut saling bebas. Dari hasil penelitian, algoritma K-Nearest Neighbor menghasilkan akurasi 95.38%, presisi 95.77%, recall 99.33%, dan F1 Score sebesar 97.52. Sedangkan algoritma Naïve Bayes Menghasilkan akurasi 90.77%, 96.78%, 92.99%, dan F1-Score 94.85.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Data Mining, Diabetes, Klasifikasi, K-Nearest Neighbor, Naïve Bayes
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Teknik > Program Studi Teknik Informatika
Depositing User: 1462000251 David Christian Putra
Date Deposited: 31 Aug 2001 19:58
Last Modified: 05 Oct 2001 16:19
URI: http://repository.untag-sby.ac.id/id/eprint/35147

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