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All Journal Marwah: Jurnal Perempuan, Agama dan Jender SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Sains Matematika dan Statistika E-Dimas: Jurnal Pengabdian kepada Masyarakat Seminar Nasional Teknologi Informasi Komunikasi dan Industri EKOLOGIA : Jurnal Ilmiah Ilmu Dasar dan Lingkungan Hidup Journal of Applied Engineering and Technological Science (JAETS) Majalah Ilmiah Matematika dan Statistika (MIMS) Al-Ittizaan : Jurnal Bimbingan Konseling Islam Psikobuletin: Buletin Ilmiah Psikologi Widya Pranata Hukum : Jurnal Kajian dan Penelitian Hukum Kutubkhanah Jurnal Penelitian Sosial Keagamaan Jurnal Administrasi Pendidikan dan Konseling Pendidikan Innovative: Journal Of Social Science Research Indonesian Council of Premier Statistical Science Jurnal Hukum dan Pembangunan Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
M Marizal
Universitas Islam Negeri Sultan Syarif Kasim Riau
Religion Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Vokasi UNESA Bulletin of Engineering, Technology and Applied Science

 1 
Application of Binary Logistic Regression Method in Diagnosing Ischemic Stroke Disease at Petala Bumi Hospital, Riau Province Marizal, Muhammad; Uci Lestari, Tri
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 1 No. 1 (2024)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v1i1.34099

Abstract

Stroke occurs suddenly when blood flow is obstructed while supplying blood to the brain. Ischemic stroke is a type of stroke that often occurs and is a major cause of disability and even death. The large number of ischemic stroke incidents is the result of ignorance of the risk factors that lead to the emergence of ischemic stroke events. The purpose of this study was to determine the risk factors that significantly influence ischemic stroke and to determine the chances of thrombotic and embolic ischemic stroke by involving several independent variables. Factors that are thought to influence the occurrence of ischemic stroke in this study were age, gender, hypertension status, diabetes mellitus status, hypercholesterolemia status, obesity, triglycerides, body mass indeks, diet, and smoking habits. The method used in this research is binary logistic regression. The results of the analysis show that age and hypertension status have a significant effect on ischemic stroke with a classification accuracy of 74% the rest is influenced by other factors
Modeling of Multiple Statistical Distributions for Extreme Rainfall Data Using Maximum Likelihood Estimation Methods and Bayesian Methods Marizal, Muhammad; Jannah, Zahratul
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 1 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i1.44270

Abstract

The city of Pekanbaru has rapidly developed into a metropolitan hub, facing challenges such as floods and haze caused by extreme rainfall events. This study proposes a novel combination of Generalized Extreme Value (GEV), Generalized Logistic (GLO), and Generalized Pareto (GP) distributions, utilizing Bayesian Markov Chain Monte Carlo (MCMC) and Maximum Likelihood Estimation (MLE) methods, to model annual extreme rainfall data for the period 2010–2024. Rainfall data were sourced from NASA/POWER. Model performance was evaluated using Relative Root Mean Square Error (RRMSE), Relative Absolute Square Error (RASE), and Probability Plot Correlation Coefficient (PPCC). The Bayesian method yielded superior performance with RRMSE = 0.3166, RASE = 0.2682, and PPCC = 0.00485 for the GEV distribution, outperforming MLE. The novelty lies in applying this methodological combination to Pekanbaru's rainfall dataset for the first time, providing valuable insights for flood mitigation, drainage planning, and urban water resource management.
Co-Authors Afifa Nurmita Afrizal Mansur Afrizal Mansur Alvionita, Veny Amirah Diniaty Ardhi Razaq A, Muhammad Ari Pani Desvina Ari Pani Desvina Ari Pani Desvina Aryo Saputra Diniya Diniya Diniya Diniya Fitria Khairum Nisa Haris Sudibjo Haswir Haswir Hayatul Atiqah Ibrahim Sulaiman Hanaish Intan Wahyu Sawitri Jamaluddin Jamaluddin Jannah, Zahratul Jumni Nelli Juniarti, Sony Kasmuri Selamat Khairi Muslimah Lestari, Tri Uci Monalisa, Kartika Anjani Mufarida, Abdilla Muhammad Hanif Mulyani, Septia Mutiarani, Fikha Noraini Binti Nazam Nur Wahid Petir Papilo Putri Awwaliyah, Rizka Putri, Mutia Rahmatul Putri, Satriani Rabbani Kurniawan, Hafidz Rado Yendra Rahmadeni Rahmadeni Rahmi, Asra Ramadhani, Hilvania Saifullah Saifullah Sarina Fadila H Suci Habibah Triantono Triantono, Triantono Triantono, Triantono Trisnawati , Trisnawati Uci Lestari, Tri Windy Ayu Pratiwi Yendra, Rado Yendra, Rado Zikri Darussaamin