MACHINE LEARNING UNTUK PERBANDINGAN TINGKAT AKURASI PREDIKSI PENYAKIT DIABETES DENGAN SUPERVISED LEARNING

Murtadho, Ali (2020) MACHINE LEARNING UNTUK PERBANDINGAN TINGKAT AKURASI PREDIKSI PENYAKIT DIABETES DENGAN SUPERVISED LEARNING. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning (ML) techniques allows us to obtain predictive, the dataset we are testing is pima-indian-diabetes with a dataset of 768 raw data with 8 data features (Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI (Body Mass Index), Diabetes Pedigree Function, Age) and one dataset label (Outcome), we developed a method to achieve the best accuracy from the five methods we use with the stages of separation traning and testing the dataset, scaling features, parameters evaluation, confusion matrix and we get the accuracy of each method, and the results of the accuracy we get with these 5 methods Gradient-boosting is best with an accuracy score of 0.8, Decision Tree 0.72, Random Forest 0.72, next is Logistic Regression 0.7, and then followed by K-NN method with a score of 0.65.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Machine Learning Prediction Diabetes, Performa Accuration Algorithm , Supervised Learning, AI(artificial intelligence)
Subjects:	R Medicine > R Medicine (General) R Medicine > RZ Other systems of medicine T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions:	Fakultas Teknik > Program Studi Teknik Informatika
Depositing User:	Ali Murtadho .
Date Deposited:	09 Mar 2020 03:18
Last Modified:	08 Jul 2020 19:20
URI:	http://repository.untag-sby.ac.id/id/eprint/2923

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