Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal TIMES Enthusiastic : International Journal of Applied Statistics and Data Science
Azhari, M Fauzan
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
IDEKTIFIKASI GEN MARKER PBMCS ISCHEMIC STROKE MENGGUNAKAN ANALISIS BIOINFORMATIKA DAN SUPPORT VECTOR MACHINE Azhari, M Fauzan; Fajriyah, Rohmatul
Jurnal TIMES Vol 13 No 1 (2024): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51351/jtm.13.1.2024746

Abstract

Penyakit stroke adalah kondisi ketika aliran darah ke otak terhambat atau terputus, mengakibatkan kerusakan pada sel-sel otak. Diperkirakan ada 50 juta kasus stroke di seluruh dunia, dengan 9 juta di antaranya mengakibatkan kecacatan berat. Penelitian ini bertujuan untuk melakukan klasifikasi dan melihat genomic profiling antara penderita stroke iskemik kontrol dan non kontrol dengan analisis support vector machine (SVM). Data yang digunakan adalah data microarray dengan kode series GSE22255 dari Institut Kedokteran Molekuler di kota Lisbon, Portugal. Untuk melihat perbandingan akurasi yang dihasilkan, analisis dilakukan dengan beberapa skema berdasarkan kernel dan nilai cost optimal pada metode SVM yaitu kernel linier, polinomial, RBF dna sigmoid. Dari hasil analisis diketahui bahwa metode SVM dengan skema kernel terbaik yaitu menggunakan kernel RBF dan optimal cost 1 dengan nilai akurasi sebesar 88.0%, Model SVM dengan kernel sigmoid tidak dapat digunakan untuk klasifikasi karena nilai akurasinya yang sangat rendah. Sementara itu, SVM dengan kernel linear dan polynomial masih tetap dapat digunakan karena nilai akurasinya >70% .
Coronary Heart Disease Risk Prediction Using Binary Logistic Regression Based on Principal Component Analysis Azhari, M Fauzan; Fitriani, Farah Ayu
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 2 Issue 1, April 2022
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol2.iss1.art6

Abstract

Based on data from the World Health Organization (WHO), one type of heart disease namely coronary heart disease is the deadliest disease in the world. In 2016 at least 9,4 million people died caused by coronary heart disease. In Indonesia, deaths caused by heart disease, blood vessel (CVD), and respiratory disorders are the fourth highest in ASEAN (23,1%). Because of the danger of coronary heart disease, we need a system or model that can predict heart disease early, so that it can be treated early and can reduce the death rate caused by heart disease. This study uses principal component analysis (PCA) to make a linear combination of variables that have a high correlation so that the assumption of multicollinearity in the data can be resolved. For the prediction, this study uses binary logistic regression to predict heart disease based on existing factors. The result of the PCA there is 7 component variables with a total variance that can be explained as much as 72,9%. From the Bartlett test of the PCA data, the obtained p-value is 1 which means that there is no multicollinearity in the data. Predictive analysis using binary logistic regression based on PCA’s data was proven to increase the accuracy to 85%.
Co-Authors Fitriani, Farah Ayu Rohmatul Fajriyah