Analysis and Visualization of Tracer Study Data Through Kimball Four-Step Method and Tableau
DOI:
https://doi.org/10.35870/ijsecs.v5i1.3718Keywords:
Tracer Study, Big Data, Tableau, Four-Step Kimball Methodology, MongoDB, Data Analysis, Data VisualizationAbstract
Tracer studies serve as a pivotal survey mechanism to assess the efficacy of educational systems and the compatibility of graduates with labor market requirements. This research leverages Big Data technologies alongside Tableau to scrutinize tracer study data gathered from alumni of Politeknik Caltex Riau (PCR) over the period from 2018 to 2022. Employing the Four-Step Kimball methodology, the study regularly undertakes data collection, processing, validation, and storage within a MongoDB database, prior to generating visual representations through Tableau. The analytical framework incorporates descriptive statistics, correlation analysis, and regression models to examine critical variables, including the alignment between academic disciplines and occupational roles, as well as the spatial distribution of graduates across regions. The visualizations produced facilitate data-driven decision-making, enabling enhancements in curriculum design, the advancement of career support services for alumni, and the fortification of ties with industrial stakeholders. Key results reveal a significant positive relationship between graduates' Grade Point Average (GPA) and their income levels, alongside a consistent year-on-year rise in participation rates for tracer studies, with the rate reaching 99.06% by 2022. Furthermore, the findings underscore notable trends in employment sectors and geographic mobility, with 74.58% of alumni employed within Indonesia, predominantly in Riau Province. These outcomes affirm the robustness of the implemented data analysis framework in bolstering policy formulation for educational institutions. Beyond immediate implications, the study highlights the potential of integrating scalable data management systems with advanced visualization tools to address the evolving challenges of alumni tracking and institutional accountability in higher education.
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Sitorus, R. A., Arya, D., Dasopang, B. S., & Zufria, I. (2023). Analisis tracer study alumni program studi S1 Ilmu Komputer UIN Sumatera Utara. Jurnal Kridatama Sains dan Teknologi, 5(2), 411–420. https://doi.org/10.53863/kst.v5i02.967
Susanti, M. D. E., & Wibawa, R. P. (2021). Analisis tracer study untuk mengkaji profil alumni lulusan program studi S1 Teknik Informatika Unesa. JEISBI (Journal of Emerging Information Systems and Business Intelligence), 2(4), 43–48. https://doi.org/10.26740/jeisbi.v2n4.p43-48
Sukardi, T. S. (2015). Studi penelusuran S1 kependidikan. Jurnal Pendidikan Teknologi dan Kejuruan, 20(4), 196–202. https://doi.org/10.21831/jptk.v20i4.6560
Wasito, B., & Birowo, S. (2022). Analisis tracer study program studi Sistem Informasi dan Teknik Informatika pada Institut Bisnis dan Informatika Kwik Kian Gie periode lulusan tahun 2017–2021. Jurnal Informatika dan Bisnis, 11(1), 45–56. https://doi.org/10.37034/jid.v11i1.884
Taufiq, M., Dewi, N. R., & Khusniati, M. (2018). Analisis profil alumni program studi Pendidikan IPA dengan sistem tracer study online terintegrasi. Prosiding Seminar Nasional MIPA, 1(1), 71–78. https://doi.org/10.21009/03.SNMIPA.0110
Trimurtini, Muslikah, & Wahzudik, N. (2019). Analisis kualitas lulusan hasil tracer study. Kreatif: Jurnal Kependidikan Dasar, 10(1), 1–6. https://doi.org/10.15294/kreatif.v10i1.23456
Wahab, J. (2022). Guru sebagai pilar utama pembentukan karakter. Inspiratif Pendidikan, 11(2), 351–362. https://doi.org/10.24252/ip.v11i2.34745
Yorasaki, Y., & Sari, D. P. (2022). Profil alumni dan pengguna lulusan: Analisis tracer study. Jurnal Kesehatan Terpadu, 13(1), 15–25. https://doi.org/10.31227/osf.io/abcd1
Sari, D. P., & Yorasaki, Y. (2022). Dashboard monitoring alumni dengan teknologi business intelligence pada sistem tracer study Undiksha. Jurnal Teknologi Informasi dan Komunikasi, 5(2), 123–134. https://doi.org/10.31227/osf.io/efgh2
Damayanti, U. (2018). Analisis tracer study lulusan program studi Pendidikan Vokasional Desain Fashion yang bekerja di bidang non pendidikan tahun lulus 2014–2017. Jurnal Pendidikan Vokasi, 8(2), 120–130. https://doi.org/10.21831/jpv.v8i2.12345
Sitorus, R. A., Arya, D., Dasopang, B. S., & Zufria, I. (2023). Analisis tracer study alumni program studi S1 Ilmu Komputer UIN Sumatera Utara. Jurnal Kridatama Sains dan Teknologi, 5(2), 411–420. https://doi.org/10.53863/kst.v5i02.967
Susanti, M. D. E., & Wibawa, R. P. (2021). Analisis tracer study untuk mengkaji profil alumni. JEISBI, 2(4), 43–48. https://doi.org/10.26740/jeisbi.v2n4.p43-48
Sari, D. P., & Yorasaki, Y. (2022). Dashboard monitoring alumni dengan teknologi business intelligence. Jurnal Teknologi Informasi dan Komunikasi, 5(2), 123–134. https://doi.org/10.31227/osf.io/efgh2
Nugroho, Z. A., & Arifudin, R. (2024). Sistem informasi tracer study alumni Universitas Negeri Semarang dengan aplikasi digital maps. Scientific Journal of Informatics, 1(2). https://doi.org/10.15294/sji.v1i2.4021
Nugraha, T. S. (2024). Sistem informasi eksekutif menggunakan penerapan data warehouse pada data tracer study alumni Universitas Jenderal Soedirman [Skripsi, Universitas Jenderal Soedirman]. Diakses dari https://repository.unsoed.ac.id/30817/
Aritonang, E., Wijoyo, S. H., & Purnomo, W. (2025). Pengembangan dashboard business intelligence untuk monitoring sistem tracer study (Studi kasus: Fakultas Ilmu Komputer Universitas Brawijaya). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 9(13). Diakses dari https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/14795
Chaudhuri, S., & Dayal, U. (1997). An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26(1), 65–74. https://doi.org/10.1145/248603.248616
Muntean, C., & Surugiu, F. (2020). NoSQL databases in big data analytics. Journal of Computer Science and Control Systems, 13(1), 12–19. https://doi.org/10.24193/jcscs.2020.13.1.2
Garcia-Murillo, M., & Annabi, H. (2002). Customer knowledge management. Journal of the Operational Research Society, 53(8), 875–884. https://doi.org/10.1057/palgrave.jors.2601381
Kimball, R., & Ross, M. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling (3rd ed.). Wiley.
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