Evaluasi Arsitektur Sistem Layanan Data Kementerian: Studi Kasus Pusintek Kementerian Keuangan

Authors

  • Dian Adiputro
  • Indra Budi

Keywords:

sistem layanan data, arsitektur sistem layanan data, data warehouse capability maturity

Abstract

Pusintek merupakan unit organisasi di Kementerian Keuangan yang menyelenggarakan layanan TIK bertanggungjawab dalam pelaksanaan integrasi  TIK. Sistem Layanan  Data merupakan bentuk integrasi data di lingkungan Kementerian Keuangan yang terdiri atas 2 komponen utama, yaitu data warehouse dan business intelligence. Pusintek berpedoman pada KMK 351/KMK.01/2011 dalam pengembangan sistem informasi. Salah satu tahapan dalam pengembangan sistem informasi adalah proses evaluasi, akantetapitahapevaluasitersebuttidakdilakukandalampelaksanaanpengembanganSistemLayanan Data di Kementerian Keuangan. Penelitian ini dilakukan untuk memenuhi kebutuhan evaluasi dan memperoleh tingkat kematangan arsitektur Sistem Layanan Data. Penelitian ini menghasilkan tingkat kematangan arsitektur SistemL ayanan Data Kementerian dengan nilai 4,01 pada aspek teknis dengan menggunakan Data Warehouse Capability Maturity Model. Rekomendasi diberikan untuk kriteria-kirteria yang ada di masing-masing sub kategori yang kondisi saat ini masih berada di bawah kondisi ideal dari tingkat keamatangan 4 yang diperoleh.

References

[1] Ameiappane, Chithra, B., & Venkataesan, P. (2013). Evaluation of software Architecture Quality Attribut for an Internet Banking System. International Journal of Computer Applications, 21-24.
[2] Alhyasat, E. B., & Al-Dalahmeh, M. (2013). Data Warehouse Success and Strategic Oriented Business Intelligence: A Theoretical Framework. Journal of Management Research ISSN 1941-899X, 169-184.
[3] Almeida, M. S., Ishikawa, M., Reinschmidt, J., & Roeber, T. (1999). Getting Started with Data Warehouse and Business Intelligence. California: IBM Corporation.
[4] Amin, R., & Arefin, T. (2010). The Empirical Study on the Factor Affecting Data Warehousing Success. International Journal of Latest Trends in Computing, 138-142.
[5] Ariyachandra, T., & Watson, H. (2010). Key Organizational Factor in Data Warehouse Architecture Selection. Decision Support Systems, 200-212.
[6] Bellgran, M., & Safsten, K. (2004). Production System Design and Evaluation for Increased System Robustness. Second World Conference on POM.
[7] Boateng, O., Singh, J., Greeshma, & Singh, P. (2012). Data Warehousing. Business Intelligence Journal, 224-234.
[8] Harison, E. (2012). Critical Success Factor of Business Intelligence System Impelementation Evidence from Energy Sector. International Journal of Enterprise Information Systems, 1-13.
[9] Hostmann, B., & Hagerty, J. (2010). ITScore for Business Intelligence and Performance Management Maturity Model. Garner IT Leader Research Note.
[10] Jalaja, T., & Shailaja, M. (2015). A Comparative Study on Operational Database, Data Warehouse and Hadoop File System. International Journal of Engineering and Techniques, 37-41.
[11] Kherdekar, V. A. (2016). A Technical Comprehensive Survey of ETL Tools. International Journal of Applied Engineering Research, 2557-2559.
[12] Lapa, J., Bernardino, J., & Figueiredo, A. (2014). A Comparative Analysis of Open Source Business Intelligence Platform. ISDOC, 86-92.
[13] Lungan, R. (2006). Aplikasi Statistika dan Hitung Peluang. Yogyakarta: Graha Ilmu. M. Ghazanfari, M. Jafari, & S. Rouhani. (2011). A Tool to Evaluate The Business Intelligence of Enterprise System. Scientia Iranica, 1579-1589.
[14] Mali, N., & Bojewar, S. (2015). A Survery of ETL Tools. International Journal of Computer Techniques, 20-27.
[15] Mannino, M., Hong, S. N., & Choi, I. C. (2007). Efficiency Evaluation of Data Warehouse Operations. ScienceDirect, 883-898.
[16] Ponniah, P. (2001). Data Warehouse Fundamentals. New York: John Wiley.
[17] Pressman, R. (2010). Software Engineering : A Practitioner’s Approach. New York: Mc Grow Hill.
[18] Rahman, N., Marz, J., & Akhter, S. (2012). An ETL Metadata Model for Data Warehousing. Journal of Computing and Information Technology, 95-111.
[19] Rupnawar, A., & D. Shinde, Y. (2016). Comparative Study on Data Warehouse and Big Data. International Journal on Recent and Innovation Trends in Computing and Communication, 258-261.
[20] Sheta, O., & Eldeen, A. (2013). Evaluating a Healthcare Data Warehouse for Cancer Diseases. IRASACT, 236.
[21] Spruit, M., & Sacu, C. (2015). DWCMM: The Data Warehouse Capability Maturity. Journal of Universal Computer Science, 1508-1534
sisfotek 2017

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Published

2017-10-04

How to Cite

Adiputro, D., & Budi, I. (2017). Evaluasi Arsitektur Sistem Layanan Data Kementerian: Studi Kasus Pusintek Kementerian Keuangan. Prosiding SISFOTEK, 1(1), 24 - 30. Retrieved from http://seminar.iaii.or.id/index.php/SISFOTEK/article/view/12

Issue

Vol. 1 No. 1 (2017): SISFOTEK 2017

Section

1. Sistem Informasi Manajemen

License

http://creativecommons.org/licenses/by/4.0