KOMPARASI PERFORMANSI ALGORITMA PENGKLASIFIKASI KNN, BAGGING DAN RANDOM FOREST UNTUK PREDIKSI KANKER PAYUDARA

UPT. PERPUSTAKAAN Universitas Esa Unggul Kampus Emas UEU - Jakarta Barat
Phone	:	021-5674223, ext 282
Fax	:
E-mail	:	[email protected]
Website	:	http://library.esaunggul.ac.id
Support (Customer Service) :
[email protected]

Welcome..guys!

Have a problem with your access?
Please, contact our technical support below:

LIVE SUPPORT

Astrid Chrisafi

! ATTENTION !

To facilitate the activation process, please fill out the member application form correctly and completely

Registration activation of our members will process up to max 24 hours (confirm by email). Please wait patiently

Still Confuse?
Please read our User Guide

UEU » Proceeding » Teknik Informatika
Posted by [email protected] at 23/08/2021 01:17:18 • 581 Views

KOMPARASI PERFORMANSI ALGORITMA PENGKLASIFIKASI KNN, BAGGING DAN RANDOM FOREST UNTUK PREDIKSI KANKER PAYUDARA

Created by :
Agung Mulyo Widodo ( 0021017305 )
Nizirwan Anwar ; Bambang Irawan ; Lista Meria ; Andika Wisnujati

Subject:	KANKER PEMODELAN PREDIKTIF
Alt. Subject :	BREAST CANCER CLASSIFICATION TECHNIQUES
Keyword:	PREDIKSI KANKER PAYUDARA

Description:

Pemodelan prediktif menggunakan teknik klasifikasi adalah salah satu cara data mining digunakan untuk mendukung sistem pengambilan keputusan. Banyak teknik pembelajaran mesin dimasukkan ke dalam pengembangan model klasifikasi prediktif ini. Penelitian ini membandingkan akurasi algoritma klasifikasi, khususnya algoritma Bagging, KNN, dan Random forest, ketika digunakan dengan dataset yang sama untuk mendiagnosis kanker payudara. Berdasarkan hasil perbandingan, algoritma KNN memiliki akurasi tertinggi dari ketiga algoritma tersebut, sedangkan algoritma random forest memiliki akurasi yang paling rendah.

Alt. Description

Predictive modeling using classification techniques is one of the ways data mining is used to support decision-making systems. Numerous machine learning techniques were incorporated into the development of this predictive classification model. This study compares the accuracy of classification algorithms, specifically the Bagging, KNN, and Random forest algorithms, when used with the same dataset to diagnose breast cancer. According to the comparison, the KNN algorithm has the highest accuracy of the three algorithms, while the random forest algorithm has the lowest.

Date Create	:	23/08/2021
Type	:	Text
Format	:	pdf
Language	:	Indonesian
Identifier	:	UEU-Proceeding-14_0066
Collection ID	:	14_0066

Source :
Konferensi Nasional Ilmu Komputer (KONIK) 2021

Relation Collection:
Civitas Akademika Universitas Esa Unggul

Coverage :
Fakultas Ilmu Komputer

Rights :
@2021 Perpustakaan Universitas Esa Unggul

Publication URL :
https://digilib.esaunggul.ac.id/komparasi-performansi-algoritma-pengklasifikasi-knn-bagging-dan-random-forest-untuk-prediksi-kanker-payudara-21249.html

[ Free Download - Free for All ]