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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 2 Documents
 1 
Search results for , issue "Vol. 14 No. 1 (2025): Computer Science, Engineering, Basic and Applied mathematics Science" : 2 Documents clear
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.
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.

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