Implementasi Business Intelligence untuk Visualisasi Data Akademik Menggunakan Power BI Dalam Meningkatkan Kualitas Layanan Akademik
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DOI:
https://doi.org/10.30865/jurikom.v13i3.9844Keywords:
Business_Intelligence, Academic_Data, Data_Visualization , visualisasi_data PowerBI SPK, DSSAbstract
Improving the quality of higher education requires the effective, integrated, and measurable management and analysis of students’ academic data. However, many higher education institutions still face challenges in collecting, integrating, and analyzing academic data efficiently. Consequently, the monitoring of academic performance has not yet been conducted in a standardized manner and has not fully supported the achievement of additional performance indicators. In this context, the implementation of Business Intelligence (BI) is highly relevant, as it provides a systematic platform for managing students’ academic performance data and presenting it through informative visualizations. This study aims to implement a Business Intelligence system using Power BI architecture to integrate and manage students’ academic performance data obtained from various academic sources. The data used in this study consisted of 80 student records, including grades, semester grade point average, cumulative grade point average, academic status, study program, and cohort year for the 2023 - 2024 period. The data integration process was carried out through the stages of extraction, transformation, and loading into the Power BI model. The implementation results show that the system was able to process 98% of the available academic data and generate three interactive dashboards, including visualizations of GPA trends, student attendance distribution, and student grade distribution. Based on the testing results, the use of the Power BI dashboard improved the efficiency of the academic reporting process from three days to one hour, representing an increase of 93.1%. In addition, the level of conformity between the information displayed on the dashboard and the source data achieved an accuracy of 97.5%, indicating that the system can support information validity in the academic decision-making process. The implementation of Business Intelligence contributes to improving academic data management, accelerating the monitoring of student achievement, assisting stakeholders in interpreting data more effectively, and supporting the enhancement of academic service quality and higher education performance evaluation.
References
[1] S. S. Dieniezhnikov, L. Pshenychna, D. N. Kozlov, and O. Kozlova, “Modern demands of education quality management in a higher education institution,” vol. 10, no. 41, pp. 65–73, Jun. 2021, doi: 10.34069/AI/2021.41.05.6.
[2] Abdelaziz, S., “The Relation between Good Governance and Improving Performance in the Higher Education (HE) Institutions,” Business and Management Studies, vol. 8, no. 2, pp. 24–33, 2022, doi: 10.11114/bms.v8i2.5789.
[3] S. Gaftandzhieva, R. Doneva, and M. Bliznakov, “Data Analytics, Students’ Academic Performance and Decision-Making in Higher Education,” in Education Research and Information Systems, vol. 3372, pp. 59–68, 2023. [Online]. Available: CEUR Workshop Proceedings
[4] H. A. Mengash, “Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems,” IEEE Access, vol. 8, pp. 55462–55470, 2020, doi: 10.1109/ACCESS.2020.2981905.
[5] C. F. de Oliveira, S. R. Sobral, M. J. Ferreira, and F. Moreira, “How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review,” Big Data and Cognitive Computing, vol. 5, no. 4, p. 64, 2021, doi: 10.3390/bdcc5040064.
[6] C. T. Gonçalves, M. J. A. Gonçalves, and M. I. Campante, “Developing Integrated Performance Dashboards Visualisations Using Power BI as a Platform,” Information, vol. 14, no. 11, p. 614, 2023, doi: 10.3390/info14110614.
[7] O. E. Olorunshola and F. N. Ogwueleka, “Review of System Development Life Cycle (SDLC) Models for Effective Application Delivery,” in Information and Communication Technology for Competitive Strategies (ICTCS 2020), vol. 191, A. Joshi, M. Mahmud, R. G. Ragel, and N. V. Thakur, Eds. Singapore: Springer, 2021, pp. 281–289, doi: 10.1007/978-981-16-0739-4_28.
[8] B. A. A. Al-khateeb, “Business Intelligence (BI): A Critical Strategy for University Success and Sustainability,” International Journal of Asian Business and Information Management, vol. 15, no. 1, pp. 1–15, 2024, doi: 10.4018/IJABIM.340387.
[9] T. Santhi, A. M. Sari, D. K. A. M. Putra, G. S. Mahendra, and M. P. Ariasih, “Implementation of Business Intelligence Using Tableau for Visualization of Student Graduation Prediction,” Journal of Software Engineering and Information System (SEIS), vol. 3, no. 2, pp. 51–58, 2023, doi: 10.37859/seis.v3i2.5436.
[10] E. Prasetyo, N. I. Sari, and A. Kurnianti, “Academic Dashboard: University Academic Data Support and Student Study Monitoring,” Semesta Teknika, vol. 26, no. 1, pp. 97–105, 2023, doi: 10.18196/st.v26i1.18516.
[11] D. Mirwansyah, “Implementasi Business Intelligence dalam Data dan Informasi Proses Penerimaan Mahasiswa Baru di Universitas Mulia,” Prosiding Seminar Nasional Matematika, Statistika, dan Aplikasinya (SNMSA), pp. 192–198, 2021.
[12] R. J. Mositsa, J. A. Van der Poll, and C. Dongmo, “Towards a Conceptual Framework for Data Management in Business Intelligence,” Information, vol. 14, no. 10, p. 547, 2023, doi: 10.3390/info14100547.
[13] T. Maaitah, “The Role of Business Intelligence Tools in the Decision Making Process and Performance,” Journal of Intelligence Studies in Business, vol. 13, no. 1, pp. 43–52, 2023, doi: 10.37380/jisib.v13i1.990.
[14] L. T. Moss and S. Atre, Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications. Boston, MA, USA: Addison-Wesley, 2003.
[15] L. T. Becker and E. M. Gould, “Microsoft Power BI: Extending Excel to Manipulate, Analyze, and Visualize Diverse Data,” Serials Review, vol. 45, no. 3, pp. 184–188, Jul. 2019, doi: 10.1080/00987913.2019.1644891.
[16] H. M. Sidik Priadana and D. Sunarsi, Metode Penelitian Kuantitatif. Tangerang Selatan, Indonesia: Pascal Books, 2021. [Online]. Available: Metode Penelitian Kuantitatif PDF
[17] J. Williams, “Developments in Business Intelligence Software,” International Journal of Managing Information Technology, vol. 5, no. 2, pp. 40–54, 2013.
18] X. J. Dia and X. J. Dia, “Business Intelligence and Big Data Analytics: An Overview,” IIMA Business Review, vol. 14, no. 3, pp. 1–12, 2014.
[19] A. Saturday, N. F. M. Azmi, N. N. A. Sjarif, S. A. Ismail, O. M. Yusop, H. Sarkan, et al., “The Challenge of Implementing an Extract, Transform and Loading (ETL) System for a Near-Real-Time Environment,” in Proc. 2017 Int. Conf. Research and Innovation in Information Systems (ICRIIS), 2017, pp. 1–5, doi: 10.1109/ICRIIS.2017.8002497.
[20] S. Kadoli, D. Patil, A. Manai, A. Shinde, and S. Kokate, “Enterprise Resource Planning (ERP) for Construction Companies Together with Business Intelligence (BI),” International Journal of Innovative Research in Science, Engineering and Technology, vol. 3, no. 2, pp. 9487–9493, 2014.
[21] V. Khatibi, A. Keramati, and F. Shirazi, “Dissemination of Business Intelligence Models to Evaluate Iranian National Higher Education,” Social Sciences & Humanities Open, vol. 2, no. 1, p. 100056, 2020, doi: 10.1016/j.ssaho.2020.100056.
[22] T. Work, “Design and Build Sales Dashboard Application,” Journal of Applied Information Systems and Technology, vol. 11, no. 2, pp. 1–10, 2022.
[23] A. Kumar and R. Sharma, “Academic data visualization using spreadsheet-based reporting systems,” Journal of Educational Data Systems, vol. 12, no. 2, pp. 45–56, 2022.
[24] S. Lee et al., “Limitations of static dashboards in educational analytics environments,” International Journal of Information Systems in Education, vol. 18, no. 3, pp. 201–215, 2023.
[25] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 4th ed. Morgan Kaufmann, 2022.
