Show simple item record

dc.contributor.advisorNababan, Anandhini Medianty
dc.contributor.advisorZamzami, Elviawaty Muisa
dc.contributor.authorWijaya, Kevin
dc.date.accessioned2024-08-23T09:03:11Z
dc.date.available2024-08-23T09:03:11Z
dc.date.issued2024
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/96064
dc.description.abstractDiabetes disease is a chronic disease whose prevalence continues to increase throughout the world. Diagnosis of this disease can be done through blood sugar checks and Glycated Hemoglobin (HbA1C) tests, as well as Complication Screening Tests. Several previous studies have succeeded in creating a diabetes detection system using algorithms Random Forest, Naive Bayes, and so on, but this research uses algorithms Deep Neural Network (DNN) and algorithms Support Vector Machine (SVM) by using dataset publicly available on Kaggle. This approach involves data pre-processing,techniques oversampling data, division of data into training-validation-testing sets, data normalization, and use of algorithms Deep Neural Network (DNN) and algorithms Support Vector Machine (SVM) in model development machine learning. The DNN and SVM architecture used consists of layers input, followed by 3 dense layer with 32, 16, and 8 neurons respectively, and using the ReLU activation function (Rectified Linear Units). There are also 3 layers dropout with a rate of 0.4 between each dense layer, and layers output with activation function sigmoid. This model is compiled with optimizer Adam with a learning rate of 0.001, using functions loss squared hinge, and accuracy metrics. Model training is performed for each dataset original, dataset with DNN algorithm, SVM algorithm, and DNN-SVM algorithm. The results show the best accuracy in dataset The DNN-SVM algorithm was applied with a training accuracy of 97,12% and loss training was 0,4279, with validation accuracy of 97,12% and loss validation of 0,4256, and testing accuracy of 97,12% and loss testing of 0,4256. This model is then integrated into website to make it easier for users to detect whether they suffer from diabetes based on the data entered.en_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectDiabetes Diseaseen_US
dc.subjectDeep Neural Networken_US
dc.subjectSupport Vector Machineen_US
dc.subjectWebsiteen_US
dc.subjectMachine Learningen_US
dc.subjectSDGsen_US
dc.titleDeteksi Penyakit Diabetes Menggunakan Algoritma Deep Neural Network dan Algoritma Support Vector Machine Berbasis Websiteen_US
dc.title.alternativeDetection of Diabetes Using Website-Based Deep Neural Network and Support Vector Machine Algorithmsen_US
dc.typeThesisen_US
dc.identifier.nimNIM201401144
dc.identifier.nidnNIDN0013049304
dc.identifier.nidnNIDN0016077001
dc.identifier.kodeprodiKODEPRODI55201#Ilmu Komputer
dc.description.pages68 Pagesen_US
dc.description.typeSkripsi Sarjanaen_US


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record