cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hengki Tamando Sihotang
Contact Email
hengkitamando26@gmail.com
Phone
+6281381251442
Journal Mail Official
hengkitamando26@gmail.com
Editorial Address
Romeby Lestari Housing Complex Blok C, No C14 Deliserdang, North Sumatra, Indonesia
Location
Unknown,
Unknown
INDONESIA
International Journal of Basic and Applied Science
Published by Institute of Computer Science
ISSN : 23018038     EISSN : 27763013     DOI : https://doi.org/10.35335/ijobas
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Physics
International Journal of Basic and Applied Science provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers, and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
Arjuna Subject : Umum - Umum
Articles 2 Documents
 1 
Search results for , issue "Vol. 13 No. 4 (2025): Computer Science, Engineering, Basic and Applied mathematics Science" : 2 Documents clear
Dynamic model formulation of glucose and lipid lowering by blue-green algae extract (spirulina platensis) Pase, Muslimah; Ainun, Kamaliah; Zuidah, Zuidah; Kristina , Kristina
International Journal of Basic and Applied Science Vol. 13 No. 4 (2025): Computer Science, Engineering, Basic and Applied mathematics Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i4.659

Abstract

Metabolic diseases such as diabetes mellitus and hyperlipidemia are the leading causes of global morbidity, with their prevalence steadily increasing every year. Spirulina platensis, as one of the natural ingredients rich in bioactive compounds, has been empirically proven to have antidiabetic and antihyperlipidemic effects. However, until now, there is no dynamic mathematical model that can model the effect of Spirulina on blood glucose and lipid levels over time. This study aims to develop a dynamic mathematical model based on a system of nonlinear differential equations that models the effect of Spirulina on the decrease in glucose and lipid levels in the body. The model was compiled using the principles of pharmacokinetics-pharmacodynamics and Michaelis-Menten kinetics, then simulated for 72 hours with a daily dose scenario. The simulation results showed that the administration of Spirulina periodically was able to reduce blood glucose levels from 160 mg/dL to 157.79 mg/dL, and lipid levels from 220 mg/dL to 193.85 mg/dL. Spirulina exhibits significant pharmacodynamic effects with faster glucose depreciation than lipids, as well as concentrations of active substances in the body that follow a daily pharmacokinetic pattern of elimination. This model is able to predict the metabolic dynamics of the body against dose and time variations, and can be the basis for the development of personalized therapies based on individual physiological parameters. This research also fills the gap in the quantitative approach in the study of Spirulina, which has been dominated by descriptive experimental studies.
Dynamic model for early detection of preterm labor Nugraeny, Lolita; Suhartini, Suhartini; Sumiatik, Sumiatik; Handayani, Purnama
International Journal of Basic and Applied Science Vol. 13 No. 4 (2025): Computer Science, Engineering, Basic and Applied mathematics Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i4.678

Abstract

Preterm labor is a major challenge in maternal and neonatal health because it contributes to high rates of newborn morbidity and mortality. Early detection is crucial, but conventional static approaches often fail to identify risks accurately and in a timely manner. This study proposes the development of a dynamic machine learning-based preterm birth risk prediction model using the Long Short-Term Memory (LSTM) architecture combined with the Bayesian Updating approach. The model is designed to process multivariate time-series data from various clinical sources such as EHR (electronic medical record), EHG (electrohysterography), CTG (cardiotocography), and vital signals collected longitudinally during pregnancy. By leveraging LSTM's ability to capture long-term temporal relationships and Bayesian probabilistic renewal mechanisms, the model is able to provide real-time and adaptive estimates of preterm labor risk on a weekly basis. Risk prediction results are visualized in the form of interactive graphs with risk categorization (low, medium, high) to support fast and accurate clinical interpretation. The study used simulated data on 500 pregnant patients and showed that the system can adjust risk predictions as new data comes in. This research makes a significant contribution to the development of artificial intelligence-based clinical decision support systems for pregnancy monitoring. Going forward, integration with real clinical data and external validation in the hospital environment is expected to improve the accuracy and implementability of the system in daily medical practice.

Page 1 of 1 | Total Record : 2

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 14 No. 2 (2025): Computer Science, Engineering, Basic and Applied mathematics Science Vol. 14 No. 1 (2025): Computer Science, Engineering, Basic and Applied mathematics Science Vol. 13 No. 4 (2025): Computer Science, Engineering, Basic and Applied mathematics Science Vol. 13 No. 3 (2024): Dec: Optimization and Artificial Intelligence Vol. 13 No. 2 (2024): Sep: Basic and Applied Science Vol. 13 No. 1 (2024): June: Basic and Aplied Science Vol. 12 No. 4 (2024): March: Basic and Applied Science Vol. 12 No. 3 (2023): December: Basic and Applied Science Vol. 12 No. 2 (2023): Sep: Basic and Applied Science Vol. 12 No. 1 (2023): June: Basic and Applied Science Vol. 11 No. 4 (2023): March: Basic and Applied Science Vol. 11 No. 3 (2022): December: Basic and Applied Science Vol. 11 No. 2 (2022): Sep: Basic and Applied Science Vol. 11 No. 1 (2022): June: Basic and Applied Science Vol. 10 No. 4 (2022): March: Basic and Applied Science Vol. 10 No. 3 (2021): December: Basic and Applied Science Vol. 10 No. 2 (2021): September: Basic and Applied Science Vol. 10 No. 1 (2021): June: Basic and Applied Science Vol. 9 No. 4 (2021): March: Basic and Applied Science Vol. 9 No. 3 (2020): December: Basic and Applied Science Vol. 9 No. 2 (2020): September: Basic and Applied Science Vol. 9 No. 1 (2020): June: Basic and Applied Science Vol. 8 No. 4 (2020): March: Basic and Applied Science Vol. 8 No. 3 (2019): December: Basic and Applied Science Vol. 8 No. 2 (2019): September: Basic and Applied Science More Issue