Utilizing phpMyAdmin for System Design in Enterprise Administration
DOI:
https://doi.org/10.51903/jtie.v3i2.193Kata Kunci:
phpMyAdmin, data management, small and medium-sized enterprises, operational efficiency, user satisfactionAbstrak
In today's digital landscape, effective data management is essential for organizations, particularly small and medium-sized enterprises (SMEs) that often struggle with traditional manual methods, leading to inefficiencies and data inaccuracies. This research aims to investigate the implementation of phpMyAdmin, a web-based database management tool, to enhance administrative systems within SMEs. The study employs a mixed-methods approach, integrating qualitative case studies and quantitative surveys to gather comprehensive insights into user experiences and operational performance. The findings reveal that the adoption of phpMyAdmin significantly improves data management efficiency, with 75% of respondents expressing satisfaction with its user-friendly interface. However, challenges such as security vulnerabilities and the necessity for user training were also identified, indicating that while phpMyAdmin offers substantial benefits, organizations must address these issues to fully leverage their capabilities. The implications of this research suggest that SMEs should prioritize investing in user training and implementing robust security measures to mitigate risks associated with data management. By doing so, organizations can enhance their operational efficiency and decision-making processes. Future research should focus on the long-term impacts of phpMyAdmin and explore its integration with other management systems to further optimize organizational performance.
Referensi
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Adekugbe, A. P., & Ibeh, C. V. (2024). Navigating Ethical Challenges In Data Management For U.S. Program Development: Best Practices And Recommendations. International Journal of Management & Entrepreneurship Research, 6(4), 1023–1033. https://doi.org/10.51594/IJMER.V6I4.982
Anyanwu, A., Olorunsogo, T., Abrahams, T. O., Akindote, O. J., Reis, O., & Author, C. (2024). Data Confidentiality And Integrity: A Review Of Accounting And Cybersecurity Controls In Superannuation Organizations. Computer Science & IT Research Journal, 5(1), 237–253. https://doi.org/10.51594/CSITRJ.V5I1.735
Atzeni, P., Bugiotti, F., Cabibbo, L., & Torlone, R. (2020). Data modeling in the NoSQL world. Computer Standards & Interfaces, 67, 103149. https://doi.org/10.1016/J.CSI.2016.10.003
Bandari, V. (2023). Enterprise Data Security Measures: A Comparative Review of Effectiveness and Risks Across Different Industries and Organization Types. International Journal of Business Intelligence and Big Data Analytics, 6(1), 1–11. https://research.tensorgate.org/index.php/IJBIBDA/article/view/3
Bello, H. O., Idemudia, C., Iyelolu, T. V., Bello, H. O., Idemudia, C., & Iyelolu, T. V. (2024). Navigating Financial Compliance in Small and Medium-Sized Enterprises (SMEs): Overcoming challenges and implementing effective solutions. 23(1), 042–055. https://doi.org/10.30574/WJARR.2024.23.1.1984
Caldognetto, T., & Tenti, P. (2014). Microgrid operation based on master-slave cooperative control. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2(4), 1081–1088. https://doi.org/10.1109/JESTPE.2014.2345052
Chang, S. E., & Ho, C. B. (2006). Organizational factors to the effectiveness of implementing information security management. Industrial Management and Data Systems, 106(3), 345–361. https://doi.org/10.1108/02635570610653498
Cruz, Y. J., Villalonga, A., Castaño, F., Rivas, M., & Haber, R. E. (2024). Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises. Operations Research Perspectives, 12, 100308. https://doi.org/10.1016/J.ORP.2024.100308
El Moursi, M. S., Zeineldin, H. H., Kirtley, J. L., & Alobeidli, K. (2014). A dynamic master/slave reactive power-management scheme for smart grids with distributed generation. IEEE Transactions on Power Delivery, 29(3), 1157–1167. https://doi.org/10.1109/TPWRD.2013.2294793
Er, B., Güneysu, Y., & Id, I. D. (2023). A Web-Based Expert System Application for Working Capital Management. Ege Academic Review, 23(3), 393–408. https://doi.org/10.21121/EAB.1145583
Ghasemaghaei, M. (2019). Does data analytics use improve firm decision-making quality? The role of knowledge sharing and data analytics competency. Decision Support Systems, 120, 14–24. https://doi.org/10.1016/J.DSS.2019.03.004
Heidt, M., Gerlach, J. P., & Buxmann, P. (2019). Investigating the Security Divide between SME and Large Companies: How SME Characteristics Influence Organizational IT Security Investments. Information Systems Frontiers, 21(6), 1285–1305. https://doi.org/10.1007/S10796-019-09959-1
Intezari, A., & Gressel, S. (2017). Information and reformation in KM systems: big data and strategic decision-making. Journal of Knowledge Management, 21(1), 71–91. https://doi.org/10.1108/JKM-07-2015-0293
J, D. V., & D, M. Rustom. (2024). Enhancing Data Consistency Through Referential Integrity In Document Oriented NoSQL Databases. Educational Administration: Theory and Practice, 30(5), 1374–1383. https://doi.org/10.53555/KUEY.V30I5.3096
Khanum, S., & Mustafa, K. (2023). A systematic literature review on sensitive data protection in blockchain applications. Concurrency and Computation: Practice and Experience, 35(1), e7422. https://doi.org/10.1002/CPE.7422
Locher, A. E. (2016). Starting Points for Lowering the Barrier to Spatial Data Preservation. Journal of Map & Geography Libraries, 12(1), 28–51. https://doi.org/10.1080/15420353.2015.1080781
Lozano, S., & Villa, G. (2004). Centralized resource allocation using data envelopment analysis. Journal of Productivity Analysis, 22(1–2), 143–161. https://doi.org/10.1023/B:PROD.0000034748.22820.33
Nudurupati, S. S., Tebboune, S., Garengo, P., Daley, R., & Hardman, J. (2024). Performance measurement in data-intensive organizations: resources and capabilities for the decision-making process. Production Planning & Control, 35(4), 373–393. https://doi.org/10.1080/09537287.2022.2084468
Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih, S. (2017). NoSQL databases for big data. International Journal of Big Data Intelligence, 4(3), 171. https://doi.org/10.1504/IJBDI.2017.085537
Pan, J. J., Wang, J., & Li, G. (2024). Survey of vector database management systems. VLDB Journal, 33(5), 1591–1615. https://doi.org/10.1007/S00778-024-00864
phpMyAdmin. (2005). The Definitive Guide to MySQL5, 87–116. https://doi.org/10.1007/978-1-4302-0071-0_6
Raptis, T. P., Passarella, A., & Conti, M. (2019). Data management in industry 4.0: State of the art and open challenges. IEEE Access, 7, 97052–97093. https://doi.org/10.1109/ACCESS.2019.2929296
Schweinar, A., Wagner, F., Klingner, C., Festag, S., Spreckelsen, C., & Brodoehl, S. (2023). Simplifying Multimodal Clinical Research Data Management: Introducing an Integrated and User-friendly Database Concept. Applied Clinical Informatics, 15(2), 234–249. https://doi.org/10.1055/A-2259-0008/ID/JR202307SOA0151-42/BIB
Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organizations. European Journal of Information Systems, 23(4), 433–441. https://doi.org/10.1057/EJIS.2014.17
Theodorakopoulos, L. ; Theodoropoulou, A. ; Stamatiou, Y. A., Theodorakopoulos, L., Theodoropoulou, A., & Stamatiou, Y. (2024). A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions. Eng 2024, Vol. 5, Pages 1266-1297, 5(3), 1266–1297. https://doi.org/10.3390/ENG5030068
Tohanean, D., & Toma, S.-G. (2024). The Impact of Cloud Systems on Enhancing Organizational Performance through Innovative Business Models in the Digitalization Era. Proceedings of the International Conference on Business Excellence, 18(1), 3568–3577. https://doi.org/10.2478/PICBE-2024-0289
Tuboalabo, A., Buinwi, U., Gbemisola Okatta, C., Johnson, E., Adama Buinwi, J., Author, C., & Buinwi Corresponding Author, U. (2024). Leveraging business analytics for competitive advantage: Predictive models and data-driven decision making. International Journal of Management & Entrepreneurship Research, 6(6), 1997–2014. https://doi.org/10.51594/IJMER.V6I6.1239
Yadav, H. (2024). Structuring SQL/ NoSQL databases for IoT data. International Journal of Machine Learning and Artificial Intelligence, 5(5), 1–12. https://jmlai.in/index.php/ijmlai/article/view/27
Diterbitkan
2024-08-21
Terbitan
Bagian
Articles
Cara Mengutip
Utilizing phpMyAdmin for System Design in Enterprise Administration. (2024). Journal of Technology Informatics and Engineering, 3(2), 217-234. https://doi.org/10.51903/jtie.v3i2.193
