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dc.contributor.advisorAhmad, Hafidlotul Fatimah
dc.contributor.advisorHerdiyeni, Yeni
dc.contributor.authorHasana, Rio Alvein
dc.date.accessioned2026-07-30T02:33:35Z
dc.date.available2026-07-30T02:33:35Z
dc.date.issued2026
dc.identifier.urihttp://repository.ipb.ac.id/handle/123456789/176450
dc.description.abstractComputational Thinking (CT) merupakan keterampilan dasar yang penting dalam pendidikan tinggi dan telah diterapkan sebagai mata kuliah wajib pada Program Pendidikan Kompetensi Umum (PPKU) IPB University. Skala mahasiswa yang besar menimbulkan tantangan dalam monitoring dan personalisasi pembelajaran jika hanya menggunakan sistem konvensional. Penelitian ini bertujuan menghasilkan modul backend pada Personalized Learning System (PLS) untuk mendukung pembelajaran CT yang adaptif. Pengembangan dilakukan menggunakan metode Scrum dengan framework Django, RDBMS MySQL, dan integrasi Large Language Model (LLM). Proses pengembangan terbagi dalam empat siklus sprint yang mencakup fitur autentikasi, profiling gaya belajar, manajemen konten, hingga fitur chatbot AI dengan feedback adaptif. Hasil penelitian menunjukkan bahwa seluruh 18 item product backlog berhasil diimplementasikan dan lulus pengujian black box sesuai dengan kriteria Definition of Done (DoD)
dc.description.abstractComputational Thinking (CT) is an essential foundational skill in higher education and has been implemented as a mandatory course in the General Competency Education Program (PPKU) at IPB University. The large student population poses challenges in monitoring and personalizing learning when only utilizing conventional systems. This research aims to develop a backend module for a Personalized Learning System (PLS) to support adaptive CT learning. The development was conducted using the Scrum method with the Django framework, MySQL RDBMS, and Large Language Model (LLM) integration. The development process was divided into four sprint cycles, covering authentication features, learning style profiling, content management, and an AI chatbot feature with adaptive feedback. The results show that all 18 product backlog items were successfully implemented and passed black-box testing in accordance with the Definition of Done (DoD) criteria.
dc.description.sponsorship
dc.language.isoid
dc.publisherIPB Universityid
dc.titlePengembangan Modul Backend pada Website Personalized Learning System untuk Pembelajaran Computational Thinkingid
dc.title.alternativeBackend Module Development on Personalized Learning System Websites for Computational Thinking
dc.typeSkripsi
dc.subject.keywordBackendid
dc.subject.keywordComputational Thinkingid
dc.subject.keywordPersonalized Learning Systemid
dc.subject.keywordScrumid
dc.subtypeUndergraduate Theses


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