Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Jurnal Matematika UNAND Jurnal Berdaya Mandiri Journal of Statistics and Data Science
Winalia Agwil
Unknown Affiliation
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering 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 Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
PREDIKSI LUAS AREA KEBAKARAN HUTAN BERDASARKAN DATA METEOROLOGI DENGAN MENGGUNAKAN PENDEKATAN MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) Winalia Agwil; Izzati Rahmi HG; Hazmira Yozza
Jurnal Matematika UNAND Vol 1, No 1 (2012)
Publisher : Jurusan Matematika FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmu.1.1.77-84.2012

Abstract

Luas area kebakaran hutan dapat diduga berdasarkan datameteorologi antara lain koordinat sumbu x spasial suatu lokasi dalampeta, koordinat sumbu y spasial suatu lokasi dalam peta, bulan, hari, in-deks FFMC, indeks DMC, indeks DC, indeks ISI, temperatur, kelemba-ban relatif, kecepatan angin dan curah hujan. Pendugaan terhadap luasarea kebakaran hutan dapat diduga dengan menggunakan pendekatanMultivariate Adaptive Regression Spline(MARS). Data yang digunakanadalah data meteorologi wilayah Portugal. Hasil pendugaan luas areakebakaran hutan dengan menggunakan MARS menghasilkan beberapavariabel yang berpengaruh secara signikan yaitu : FFMC, hari, temper-atur, DMC, kelembaban relatif, bulan, koordinat sumbu y spasial suatulokasi dalam peta, DC, dan koordinat sumbu x spasial suatu lokasi dalampeta dengan tingkat kepentingan berturut-turut 100%, 90.9%, 73.5%,34.5%, 25,1%,23.1%, 19.6%, 17.9% dan 5.7%.
PELATIHAN SPSS UNTUK ANALISIS DATA PENELITIAN TINDAKAN KELAS Nur Afandi; Herlin Fransiska; Siska Yosmar; Dyah Setyo Rini; Winalia Agwil; Baki Swita
Jurnal Berdaya Mandiri Vol. 4 No. 3 (2022): Jurnal Berdaya Mandiri (JBM)
Publisher : Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jbm.v4i3.2805

Abstract

The implementation of Classroom Action Research (CAR) in SMA 8 Bengkulu City is not optimal. The problem is the teacher's skills in data analysis, such as: methodology, depth of analysis, and statistical analysis tools is low. Statistical analysis commonly used is descriptive statistics rather than inferential statistics. The solution is to carry out data analysis training for classroom action research using SPSS software. The activity has 3 (three) stages: 1. Preparation stage: collect the information, tracking, and survey of teacher needs, which includes problems faced by teachers in analyzing classroom action research data. 2. Implementation stage: training on SPSS software for data analysis. 3. Evaluation stage: practical tests for training participants. Based on evaluation, it concluded that the teacher's skill in data analysis had increased. keyword: Training, CAR, SPSS
Machine Learning Approach to Automated Early Warning System for Medical Vital Signs Monitoring Nevani, Claudia; Sigit Nugroho; Winalia Agwil
Journal of Statistics and Data Science Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v4i1.37437

Abstract

Precise and timely detection of deteriorating vital signs is an important aspect of patient safety and clinical intervention. The current standard of monitoring systems lacks automated early warning systems, instead using manual observation to make judgments. This manual approach can lead to delays in detecting critical changes in a patient's condition. We present a novel approach to developing an automated early warning system for vital signs using a hybrid method that combines LSTM (Long Short Term Memory) and XGBoost (Extra Gradient Boost), both methods offer robust predictive modeling that is able to capture the complex and often non-linear relationships inherent in physiological data. This research believes that using a novel technique that combines LSTM and XGBoost advances predictive systems in healthcare-based technology as well as laying the groundwork for even further innovations in early warning systems. The early warning system will evaluate vital signs such as respiratory rate, SpO2 levels, heart rate, body temperature, and pulse which can recognize and predict early signs of clinical deterioration, allowing early intervention and may save a patient’s life. This research will use error metrics such as MAPE, MAE (Mean Absolute Error), MSE, RMSE, and MAD to compare the predicted actual values.
Application of Tobit Regression on Household Expenditure on Egg and Milk Consumption in Bengkulu City Claudia Nevani; Sigit Nugroho; Winalia Agwil
Journal of Statistics and Data Science Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v4i1.40336

Abstract

Regression analysis is a statistical method used to examine the functional relationship between two or more independent variables and a dependent variable. One of the regression methods designed to handle censored data or data with significant zero values is Tobit regression. This study aims to model household expenditures on egg and milk consumption in Bengkulu City using Tobit regression and to identify the factors influencing these expenditures. The data were obtained from the 2022 National Socioeconomic Survey, with a total sample of 1,170 households. The Tobit regression model was chosen because most household expenditure data had zero values, indicating censored data characteristics. This study identified several factors affecting expenditures on egg and milk consumption, such as the household head's education level, the number of household members, and the household head's employment sector. The results showed that the education level of the household head (elementary, junior high, and high school), the number of household members, and the household head's employment in agriculture and trade sectors had significant impacts on household expenditures for egg and milk consumption. The education level of the household head and their employment sector had a negative relationship, while the number of household members showed a positive relationship with these expenditures. The Tobit regression model successfully modeled household expenditures with adequate accuracy, as indicated by a Mean Absolute Percentage Error (MAAPE) of 1.38%.
Co-Authors Baki Swita Claudia Nevani Dyah Setyo Rini Hazmira Yozza Herlin Fransiska Nevani, Claudia Nur Afandi Sigit Nugroho Siska Yosmar