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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 119 Documents
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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.
Parallel Batch Processor Machine Scheduling Using Multi-Population SPEA-II Algorithm Tampubolon, Ferdinan Rinaldo; Siagian, Sinta Marito; Samaria Chrisna HS; Rischa Devita; Sitinjak , Anna Angela
International Journal of Basic and Applied Science Vol. 14 No. 1 (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.v14i1.653

Abstract

The increasing competition in the industrial sector requires companies to provide more optimal services, particularly in terms of production speed by increasing machine utilization. This can be achieved by implementing parallel batch scheduling. In conventional scheduling, a machine is only able to handle one job at a time, whereas in parallel batch scheduling, a machine can process a group of jobs simultaneously based on its capacity. Flexible Job Shop with parallel batch processor has been studied by several researchers, but the objective function has generally been limited to minimizing makespan. This research aims to minimize multi objective function that are energy consumption and makespan by using the Modified Strength Pareto Evolutionary Algorithm-II (SPEA2). Modifications of the algorithm are conducted by applying multi-population that run in parallel so that the optimization process can avoid local optima. The results of the research show that Multi-Population SPEA2 provides more optimal results compared to classical SPEA2 and benchmarks from previous research.
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.
Distribution cost optimization: Comparison of NWC, MODI, and Stepping Stone methods in transportation problems Riandari, Fristi; Sihotang, Hengki Tamando
International Journal of Basic and Applied Science Vol. 14 No. 2 (2025): Sep (In Progress)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i2.688

Abstract

Solving transportation problems is essential in minimizing distribution costs in logistics and supply chains. Three classical methods North West Corner (NWC), Modified Distribution Method (MODI), and Stepping Stone are frequently used, but few studies offer a comprehensive comparison. This study fills this gap by evaluating their performance using simulated data representing real-world distribution scenarios. This study applies a structured comparative framework to analyze NWC (a cost-agnostic initial allocation technique), MODI (a dual-variable-based optimization approach), and Stepping Stone (a closed-loop path evaluation method). Each method was tested on a simulated cost matrix using Python. Evaluation metrics included total distribution cost, number of iterations, and computation time. The NWC method yielded a feasible but suboptimal solution with a cost of 540 units. Optimization using MODI reduced the cost to 425, while Stepping Stone further minimized it to 410 after three iterations. MODI showed greater computational efficiency, while Stepping Stone offered visual traceability of cost reductions. This study contributes methodologically by combining heuristic and iterative optimization techniques in one analytical framework. Practically, it provides decision-makers with insights into selecting appropriate solution methods based on trade-offs between simplicity, efficiency, and cost minimization.
A System dynamics quantitative model for enhancing e-government maturity in the indonesian education sector Yulistiawan, Bambang Saras; Widyastuti, Rifka; Mulianingtyas, Rr Octanty; A, Galih Prakoso Rizky; Sihotang, Hengki Tamando
International Journal of Basic and Applied Science Vol. 14 No. 2 (2025): Sep (In Progress)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i2.693

Abstract

This study develops a deterministic mathematical model integrated with system dynamics to measure key success factors driving e-government maturity in Indonesia’s education sector. Addressing the gap in previous research, which mainly relied on descriptive methods, the model quantitatively examines causal relationships among leadership commitment, budget support, digital infrastructure, human capital, service quality, and feedback mechanisms. The methodology involves three stages: (1) constructing a causal loop diagram based on theoretical and empirical insights, (2) converting these relationships into a linear system of equations normalized on a [0–1] scale, and (3) performing simulations and sensitivity analyses to evaluate policy scenarios. Simulation results indicate that even relatively high leadership commitment (K=0.75) only produces moderate maturity levels (M≈0.409). The greatest improvement occurs when feedback loops are reinforced and service quality investments are prioritized. Sensitivity analysis reveals the model is particularly responsive to changes in feedback effectiveness and service quality weighting, identifying these as critical leverage points for accelerating transformation. Under optimal conditions, maturity can increase from 0.41 to 0.48, reflecting a 7% gain over the baseline. The study contributes a replicable quantitative framework for evidence-based policymaking, while noting limitations in parameter assumptions and empirical calibration for future refinement.
A bayesian dynamic latent state model for predicting infant sleep-wake patterns under daily massage intervention A , Galih Prakoso Rizky; Rasenda, Rasenda; Dermawan, Budi Arif; Arifuddin, Nurul Afifah; Alrasyid , Wildan
International Journal of Basic and Applied Science Vol. 14 No. 1 (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.v14i1.699

Abstract

Sleep disturbances in infants present a persistent challenge for caregivers and healthcare providers. This study proposes a Bayesian Dynamic Latent State Model to predict infant sleep-wake patterns in response to daily massage, a non-pharmacological intervention. The model captures latent sleep propensity as a dynamic hidden process influenced by current and previous massages, individual random effects, and autoregressive components. Observed outcomes include nocturnal sleep duration and nighttime awakenings, modeled using Gaussian and Poisson distributions respectively. Through numerical simulations and a real-world case study, the model demonstrates clear benefits: average nocturnal sleep duration increased by approximately 1.2–1.5 hours, while nighttime awakenings decreased by about 35–40% on intervention days, with residual improvements on subsequent days. Compared to traditional static and time-series models, the proposed Bayesian approach provides greater flexibility in handling uncertainty, explicitly models carry-over effects, and integrates individual heterogeneity in sleep responses contributions that have not been fully addressed in prior infant sleep studies. This research thus advances the scientific understanding of dynamic, intervention-driven sleep processes, while also offering practical implications for evidence-based pediatric nursing and personalized infant care strategies. While promising, validation is currently limited to a small dataset and simplified assumptions. Future work will involve larger-scale testing, incorporation of additional external factors, and benchmarking against alternative machine learning models.
The completeness role of the function ϕ in generating the Riesz potential operator Vinsensia, Desi; Utami, Yulia; Addini, Puteri Fadjar
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.v13i4.637

Abstract

The Riesz potential operator is a central tool in harmonic analysis and the theory of partial differential equations, commonly defined via convolution with a singular Kernel. In many modern frameworks, function space are generated by a mappings involving such operators. In this paper, we explore the dual role of the generating function- in: (i). Defining the Riesz function space and (ii). Ensuring its completeness. We introduce a Riesz function space whose norm is induced growth function- (a Young function). We establish, through several examples and proofs, that under suitable conditions (specifically, the condition on ), the space is complete. Furthemore, we illustrate discrete analogues and applications to Orlicz space, thereby underscoring the fundamental importance of in both the construction and Banach space structure of these function spaces.
Queueing theory and simulation for reducing patient waiting time in emergency departments Aminah, Aminah; Manalu, Harauly Lady Lusiana; Silaban, Verawaty Fitrinelda; Munthe, Dewi Sartika; Harahap, Rahmaini Fitri
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.v13i4.639

Abstract

Emergency Departments (EDs) are increasingly overwhelmed by rising patient volumes and limited service capacity, leading to long waiting times and reduced care quality. This study addresses the inefficiencies of conventional queue policies by proposing a dynamic scheduling approach known as the Accumulative Priority Queue with Finite Horizon (APQ-h). APQ-h integrates time-based priority accumulation with triage thresholds, allowing for a more realistic representation of how clinicians manage patient flow. Using discrete-event simulation and simulation-based optimization, the research calibrates accumulation rate parameters (β) to minimize total waiting time (TWT) and ensure compliance with clinical response time targets (APT). A real-world case study and sensitivity analysis reveal that optimal configurations of β enable balanced and adaptive queue management without disadvantaging any patient group. The findings contribute a hybrid queue discipline that bridges mathematical modeling and clinical practice, offering practical implications for improving ED throughput and resource utilization. Although the simulation relies on stylized assumptions, it opens avenues for real-time validation, integration with adaptive triage systems, and scalability across diverse healthcare settings.
A Stochastic modeling framework for ICU resource allocation during health crises Rangkuti, Saddiyah; Simatupang, Christine Debora; Stephane, Laurent Seychelle; Eloise, Darrell
International Journal of Basic and Applied Science Vol. 13 No. 3 (2024): Dec: Optimization and Artificial Intelligence
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i3.644

Abstract

The unprecedented surge in demand for intensive care services during health crises such as the COVID-19 pandemic has revealed critical limitations in existing ICU resource allocation models, which often fail to adapt to uncertain and dynamic conditions. This study aims to develop and evaluate a stochastic modeling framework to optimize ICU resource allocation under crisis scenarios, accounting for probabilistic patient arrivals, fluctuating treatment durations, and constrained multi-resource environments. The framework integrates discrete-event simulation (DES), queueing theory (specifically M/M/c/K models), and stochastic optimization to simulate real-time ICU operations and support decision-making. A Monte Carlo simulation was conducted over a 24-hour period involving 100 replications, where key parameters included a patient arrival rate of 4 patients/hour, 5 ICU beds, and a service time distribution with an average of 6 hours. The results indicate a high blocking probability of 84.3%, ICU bed utilization of 94%, ventilator utilization of 90%, an average patient waiting time of 2.4 hours, and a delay-sensitive mortality rate of 8%. The expected system cost, incorporating waiting time, mortality, and resource inefficiency penalties, totaled 190 units. These findings demonstrate the model’s capability to reveal critical system bottlenecks and support adaptive, ethically grounded allocation policies. The proposed framework provides practical implications for hospital administrators and policymakers by offering a dynamic, evidence-based decision-support tool to improve ICU efficiency and patient outcomes during emergencies.

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