Garuda - Garba Rujukan Digital

p-Index From 2019 - 2024

2.765

P-Index

This Author published in this journals

All Journal Jurnal Khatulistiwa Informatika Paradigma Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) Jurnal Riset Informatika Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal Minfo Polgan (JMP)

Hafdiarsya Saiyar

Universitas Bina Sarana Informatika

Author-ID : 2551358

Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Medicine & Pharmacology Social Sciences

Published : 20 Documents Claim Missing Document

Claim Missing Document

Articles

Title

KLASIFIKASI RETINOPATI DIABETES DENGAN METODE NEURAL NETWORK Hafdiarsya Saiyar
Paradigma Vol 19, No 2 (2017): Periode September 2017
Publisher : LPPM Universitas Bina Sarana Informatika

Abstract— Diabetic retinopathy (DR) is one of the complications in the retina caused by diabetes. The symptoms shown by patients with DR, among others mikroaneurysms, hemorrhages, hard exudate and soft exudates. These symptoms at a certain intensity can be an indicator of phase (severity) of diabetic retinopathy. DR severity levels are divided into four classes namely: Normal, Non-Proliferative Diabetic Retinopathy (NPDR), Proliferative Diabetic Retinopathy (PDR), and Macular edema (ME) .The system built in this thesis is the detection of diabetic retinopathy level of images obtained from STARE (Structured Analysis of the Retina). There are four main stages to resolve the problems of the pretreatment, extraction of anatomical structures, feature extraction and classification. Pretreatment methods are used including gray image (grayscale), a Gaussian filter, Histogram retinal image with wavelet de noising and Masking. The retinal image using neural network trained with backpropagation algorithm for classification. The resulting performance of this approach is the sensitivity 100% , sfesificity 95%, accuracy 96%. Keywords: Diabetic retinopathy, Neural Network, Backpropagation, STARE.

Aplikasi Diagnosa Penyakit Tuberculosis Menggunakan Algoritma Data Mining Amrin Amrin; Hafdiarsya Saiyar
Paradigma Vol 20, No 2 (2018): Periode September 2018
Publisher : LPPM Universitas Bina Sarana Informatika

It is important for doctors to make an early diagnosis of tuberculosis in order to reduce the transmission of the disease to the wider community. In this study, the authors will apply and compare several methods of data mining classification, including AlgoritmaC4.5, Naïve Bayes, and Neural Network to diagnose tuberculosis disease, then compare which of the three methods are the most accurate. Based on the performance measurement results of the three models using Cross Validation, Confusion Matrix and ROC Curve methods, it is known that Naïve Bayes method is the best method with accuracy of 94.18% and under the curva (AUC) value of 0.977 , then neural network method with accuracy 89,89% and under the curva value (AUC) 0,975, and then C4.5 method with accuracy level equal to 84,56% and under the curva value (AUC) equal to 0,938. This shows that the three models that are produced including the category of classification is very good because it has an AUC value between 0.90-1.00.

Co-Authors Amrin Amrin Amrin Amrin Desmulyati Desmulyati Desmulyati Desmulyati Mohammad Noviansyah Mohammad Noviansyah Rudianto Rudianto Sopyan Sopyan

Title Search

Found 2 Documents Search Journal : Paradigma

Abstract

Abstract

Title

Found 2 Documents
Search
Journal : Paradigma