…
comment: The repository administrator has not yet configured an RDF license.
primary topic: COMPARISON OF SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS FOR DIABETES DISEASE PREDICTION

COMPARISON OF SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS FOR DIABETES DISEASE PREDICTION
type: Thesis
type: Article
abstract: COMPARISON OF SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS FOR DIABETES DISEASE PREDICTION Tjut Awaliyah Zuraiyah1 , Halimah Tussa’diah2 , Dinnar Nurhuda Hermawan3 1,2,3 Department of Computer Science, Faculty of Mathematics and Natural Science, Pakuan University, Bogor, West Java, 16143, Indonesia Abstract Diabetes mellitus is one of the most widespread and persistent diseases affecting humans worldwide. Around 425 million people have suffered globally today and it is estimated that up to 700 million people will be affected by 2045. Before diagnosing this disease, doctors must analyze several factors, making the doctor's work inefficient. However, technology can be used to make predictions or detect diabetes, according to current advances. This technological advancement can help make it easier for doctors to treat the disease. In the medical field, classification methods can be used to classify the severity of a patient's disease. The purpose of this study was to determine the accuracy of the support vector machine and random forest algorithm classification on the dataset of diabetes patients with 4 scenarios of dividing train data and test data, then a web application was created. The benefits of this study are to provide a model that can be used by health workers and the public for the first screening tool to identify patients who may have diabetes. The research method used is CRISP-DM with the stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results of this study prove that the random forest algorithm is able to predict diabetes with high performance in various data sharing scenarios compared to the support vector machine algorithm. The developed diabetes prediction model has the highest performance in data splitting 70:30, 75:25, and 90:10 with the following values: accuracy of 96%, recall of 96%, precision of 94%, and f1-score of 95%. Keywords: prediction; diabetes; CRISP-DM; random forest…
date: 2024-07-14
status: published
author list: authors
type: EPrint
type: ThesisEPrint
is part of: repository
has document: COMPARISON OF SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS FOR DIABETES DISEASE PREDICTION (Text)
subject: Ilmu Komputer

COMPARISON OF SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS FOR DIABETES DISEASE PREDICTION (Text)
type: Document
has file: SKRIPSI.docx
has part: SKRIPSI.docx

Universitas Pakuan
type: Organization

Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Pakuan
type: Organization

Tjut Awaliyah Zuraiyah
type: Person
given name: Tjut
family name: Awaliyah Zuraiyah

Dinnar Nurhuda Herman
type: Person
given name: Dinnar Nurhuda
family name: Herman

Halimah Tus Sadiah
type: Person
given name: Halimah
family name: Tus Sadiah

Fakultas Matematika dan Ilmu Pengetahuan Alam
type: Organization

Program Studi Ilmu Komputer
type: Organization

HTML Summary of #8777 COMPARISON OF SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS FOR DIABETES DISEASE PREDICTION
format: text/html
primary topic: COMPARISON OF SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS FOR DIABETES DISEASE PREDICTION

Ilmu Komputer
type: Concept