<![CDATA[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]]>
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