Calculus-Based Approaches for Enhancing Performance Efficiency in Computational Information System
DOI:
https://doi.org/10.31004/jpion.v5i1.934Keywords:
calculus-based approaches, computational information systems, performance efficiency, optimization, algorithmsAbstract
The rapid expansion of computation-based information systems has intensified the demand for high performance, efficiency, and reliability in data processing environments. Increasing data volumes, system complexity, and real-time service requirements pose significant challenges to system optimization. This study aims to examine how calculus-based approaches contribute to enhancing performance efficiency in computational information systems. A qualitative literature review method was employed by analyzing peer-reviewed journal articles, conference proceedings, and authoritative technical reports published between 2015 and 2024. The analysis focuses on the application of differential and integral calculus in algorithm optimization, resource allocation, and system performance modeling. The findings indicate that calculus-based techniques play a critical role in reducing computational complexity, improving processing speed, and optimizing resource utilization. Calculus enables systematic performance evaluation by modeling rates of change and cumulative system behavior, allowing developers to identify optimal operational conditions. This study concludes that calculus-based approaches provide a strong mathematical foundation for improving efficiency and scalability in modern computational information systems.
References
Boyd, S., & Vandenberghe, L. Convex Optimization. Cambridge University Press.
Creswell, J. W., & Poth, C. N. Qualitative Inquiry and Research Design. Sage Publications.
Dean, J., & Ghemawat, S. MapReduce: Simplified data processing on large clusters. Communications of the ACM.
García-Molina, H., Ullman, J. D., & Widom, J. Database Systems: The Complete Book. Pearson Education.
Goodfellow, I., Bengio, Y., & Courville, A. Deep Learning. MIT Press.
Hennessy, J. L., & Patterson, D. A. Computer Architecture: A Quantitative Approach. Morgan Kaufmann.
Kumar, R., & Singh, A. K. Performance optimization techniques in cloud computing. Journal of Cloud Computing.
Kumar, S., & Kaur, A. Algorithm efficiency analysis using calculus-based optimization techniques. International Journal of Computer Applications.
Lincoln, Y. S., & Guba, E. G. Naturalistic Inquiry. Sage Publications.
Miles, M. B., Huberman, A. M., & Saldaña, J. Qualitative Data Analysis. Sage Publications.
Papoulis, A., & Pillai, S. U. Probability, Random Variables, and Stochastic Processes. McGraw-Hill.
Pressman, R. S., & Maxim, B. R. Software Engineering: A Practitioner’s Approach. McGraw-Hill Education.
Silberschatz, A., Korth, H. F., & Sudarshan, S. Database System Concepts. McGraw-Hill Education.
Stewart, J. Calculus: Early Transcendentals. Cengage Learning.
Strang, G. Calculus. Wellesley-Cambridge Press.
Tan, P. N., Steinbach, M., Karpatne, A., & Kumar, V. Introduction to Data Mining. Pearson Education.
Zhang, Q., Chen, M., Li, L., & Li, H. Performance optimization in big data systems. IEEE Access.
Zhou, Z. H. Machine Learning. Springer.
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Copyright (c) 2026 Ghina Zalianti, Anistasya Elsa Putri,Nabila Jaisya Haq,Regita Cahyani,Pandu Herlangga,Risayd Baihaqqi
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