Implementasi dan Optimasi Server JupyterHub sebagai Pendukung Pembelajaran di Laboratorium Informatika Universitas Janabadra

Authors

  • Sri Rahayu Universitas Janabadra, Indonesia
  • Muhammad Fuad Universitas Janabadra, Indonesia

DOI:

https://doi.org/10.35889/jutisi.v15i3.3453

Keywords:

JupyterHub, Laboratorium, Python, Praktikum, Server Terpusat

Abstract

Python programming laboratories often encounter challenges related to inconsistent software configurations, library installation errors, and the lack of centralized management of the practical learning environment. This study aims to design and implement JupyterHub as a centralized Python laboratory environment based on a local server at the Informatics Laboratory of Universitas Janabadra. The study employed an applied experimental approach consisting of system requirements analysis, architecture design, service implementation, and multi-user performance testing and evaluation. The results demonstrate that the proposed system was successfully implemented according to the designed architecture. JupyterHub and the Dask cluster operated in an integrated manner, while user authentication, automated user management, and resource optimization through the Idle Culler mechanism functioned effectively. Furthermore, monitoring using the Dask Dashboard indicated that computational tasks were successfully distributed across the cluster with stable server resource utilization. These findings indicate that the proposed system provides a reliable, centralized, and efficient environment for supporting Python programming laboratories in higher education.

Keywords: JupyterHub; Laboratory; Python; Practical Learning; Centralized Server

 

Abstrak

Pelaksanaan praktikum pemrograman Python di laboratorium komputer sering menghadapi permasalahan perbedaan konfigurasi perangkat, kesalahan instalasi pustaka, serta keterbatasan pengelolaan lingkungan praktikum secara terpusat. Penelitian ini bertujuan merancang dan mengimplementasikan JupyterHub sebagai lingkungan praktikum Python terpusat berbasis server lokal di Laboratorium Informatika Universitas Janabadra. Pendekatan yang digunakan adalah eksperimen terapan melalui tahapan analisis kebutuhan, perancangan arsitektur sistem, implementasi layanan, serta pengujian dan evaluasi performa multiuser. Hasil penelitian menunjukkan bahwa sistem berhasil diimplementasikan sesuai rancangan, layanan JupyterHub dan cluster Dask berfungsi secara terintegrasi, serta mekanisme autentikasi, otomatisasi manajemen pengguna, dan optimasi sumber daya melalui Idle culler berjalan dengan baik. Monitoring menggunakan Dashboard Dask menunjukkan bahwa distribusi komputasi berlangsung secara normal dengan penggunaan sumber daya server yang stabil.

Kata kunci: JupyterHub; Laboratorium; Python; Praktikum; Server Terpusat

Author Biographies

Sri Rahayu, Universitas Janabadra

Informatika

Muhammad Fuad, Universitas Janabadra

Informatika

References

[1] M. H. Maulana, “Python Bahasa Pemograman Yang Ramah Bagi Pemula,” JISCO : Journal of Information System and Computing, vol. 2, no. 2, pp. 73–78, Dec. 2024, doi: 10.30631/jisco.v2i2.105.

[2] “The Littlest JupyterHub,” The Littlest JupyterHub. Accessed: Aug. 04, 2026. [Online]. Available: https://tljh.jupyter.org/en/latest/index.html

[3] N. S. N. Az-zahrani, H. K. A. Eloi, F. Salim, A.-Z. A. Ramadhani, C. Meysyanti, and L. N. A. Purwantiningsih, Python untuk Analisis Data. SIEGA Publisher, 2025.

[4] J. Flemming, “JupyterHub and autograding on bare-metal lab servers,” Westsächsische Hochschule Zwickau, 2022. doi: 10.34806/6F2W-V119.

[5] T. P. Rezeki and M. I. P. Nasution, “Integrasi Data Base Berbasis Cloud Untuk Skalabilitas Bisnis: Studi Kasus Netflix,” Journal Sains Student Research, vol. 3, no. 3, pp. 328–340, May 2025, doi: 10.61722/jssr.v3i3.4743.

[6] J. Bascuñana, S. León, M. González-Miquel, E. J. González, and J. Ramírez, “Impact of Jupyter Notebook as a tool to enhance the learning process in chemical engineering modules,” Education for Chemical Engineers, vol. 44, pp. 155–163, Jul. 2023, doi: 10.1016/j.ece.2023.06.001.

[7] M. Rocklin, “Dask: Parallel Computation with Blocked algorithms and Task Scheduling,” presented at the Python in Science Conference, Austin, Texas, 2015, pp. 126–132. doi: 10.25080/Majora-7b98e3ed-013.

[8] J. Stubbs et al., “Integrating Jupyter into Research Computing Ecosystems: Challenges and Successes in Architecting JupyterHub for Collaborative Research Computing Ecosystems,” in Practice and Experience in Advanced Research Computing 2020: Catch the Wave, in PEARC ’20. New York, NY, USA: Association for Computing Machinery, Jul. 2020, pp. 91–98. doi: 10.1145/3311790.3396648.

[9] P. Prathanrat and C. Polprasert, “Performance Prediction of Jupyter Notebook in JupyterHub using Machine Learning,” 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), pp. 157–162, Oct. 2018, doi: 10.1109/ICIIBMS.2018.8550030.

[10] D. Li, R. Pyke, and R. Jiang, “A Scalable Cloud-based Architecture to Deploy JupyterHub for Computational Social Science Research,” in Practice and Experience in Advanced Research Computing, Boston MA USA: ACM, Jul. 2021, pp. 1–4. doi: 10.1145/3437359.3465591.

[11] J. Reppin et al., “Interactive analysis notebooks on DESY batch resources: Bringing Juypter to HTCondor and Maxwell at DESY,” Comput Softw Big Sci, vol. 5, no. 1, p. 16, Dec. 2021, doi: 10.1007/s41781-021-00058-y.

[12] S. Ibrahim, T. Stitt, M. Larsen, and C. Harrison, “Interactive in situ visualization and analysis using Ascent and Jupyter,” in Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, in ISAV ’19. New York, NY, USA: Association for Computing Machinery, Jan. 2020, pp. 44–48. doi: 10.1145/3364228.3364232.

[13] S. Böhm and J. Beránek, “Runtime vs Scheduler: Analyzing Dask’s Overheads,” in 2020 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS), Nov. 2020, pp. 1–8. doi: 10.1109/WORKS51914.2020.00006.

[14] Steven Stetzler, Mario Jurić, Kyle Boone, Andrew Connolly, Colin T. Slater, and Petar Zečević, “The Astronomy Commons Platform: A Deployable Cloud-based Analysis Platform for Astronomy,” The Astronomical Journal, vol. 164, no. 68, p. (18 pp), 2022, doi: https://doi.org/10.3847/1538-3881/ac77fb.

[15] J. Rolf, M. Wolf, and D. Gerhard, “Concept to Manage and Grade Python Programming Assignments in Large Cohorts,” in Open Science in Engineering, M. E. Auer, R. Langmann, and T. Tsiatsos, Eds., Cham: Springer Nature Switzerland, 2023, pp. 737–747. doi: 10.1007/978-3-031-42467-0_69.

Downloads

Published

2026-07-14

How to Cite

Rahayu, S., & Fuad, M. (2026). Implementasi dan Optimasi Server JupyterHub sebagai Pendukung Pembelajaran di Laboratorium Informatika Universitas Janabadra. Jutisi : Jurnal Ilmiah Teknik Informatika Dan Sistem Informasi, 15(3), 1446–1455. https://doi.org/10.35889/jutisi.v15i3.3453

Issue

Section

Articles

Citation Check