Article Per Year (5 Year)
p-Index From 2020 - 2025
1.745
P-Index
This Author published in this journals
All Journal Proyeksi: Jurnal Psikologi Cakrawala Pendidikan Medikora: Jurnal Ilmiah Kesehatan Olahraga Psikologika : Jurnal Pemikiran dan Penelitian Psikologi Kertha Patrika UNEJ e-Proceeding PADJADJARAN Jurnal Ilmu Hukum (Journal of Law) Jurnal Pertanian Agros Jurnal Meteorologi Klimatologi dan Geofisika Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Yuridika JURNAL MULTIDISIPLIN ILMU AKADEMIK Journal of Statistics and Data Science
Sigit Nugroho
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

Published : 17 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Statistics and Data Science

 1 
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 Aisiyah Erlin Kartika Amri Hartanto Andrian Rahman Ayudi Aprillia Damayanti Betrix Teofa Perkasa Wibafied Billy Yachsie Claudia Nevani Darwance Darwance Derita Prapti Rahayu Dian Sari Utami Farradinna, Syarifah Feri Catur Yuliani Laily Fajarwati Mieke Yustia Ayu Ratna Sari Mieke Yustia Sari Millaty, Marosimy Nevani, Claudia Nuzulia Kumalasari Ratna Ratna Reti Nurhayati Ringgho Agustino Risky Arifimanto Ujang Nurdin Winalia Agwil yatini yatini Yokotani Yokotani