Development of a Web-Based Job and Career Compatibility System Using the Federal Enterprise Architecture Framework Method: A Case Study in Nusa Putra University
Bibliographic record
Abstract
In the rapidly evolving landscape of higher education and the job market, aligning students' career aspirations with suitable employment opportunities has become a critical challenge.This research presents the development of a web-based Job and Career Compatibility System (JCCS) designed to bridge the gap between academic pursuits and career pathways.The study employs the Federal Enterprise Architecture Framework (FEAF) as a guiding methodology to ensure systematic design, development, and implementation.The case study is conducted within the context of Nusa Putra University, aiming to offer a comprehensive understanding of the system's practical application.The JCCS integrates multifaceted functionalities, including student profiling, career path exploration, and job matching based on skillsets and preferences.FEAF's structured approach assists in defining architectural components, data flows, and interdependencies, ensuring interoperability and sustainability.The research contributes to both academia and industry by showcasing the successful fusion of modern technological solutions with a robust architectural framework.Preliminary feedback from students and career advisors indicates improved career-related decision-making and enhanced awareness of potential opportunities.Furthermore, the utilization of FEAF establishes a precedent for the systematic development of similar systems within other educational institutions or organizational contexts.In conclusion, the web-based Job and Career Compatibility System developed using the Federal Enterprise Architecture Framework stands as an innovative tool addressing the intricate task of aligning academic pursuits with future career goals.This study sheds light on the practical benefits of employing FEAF in developing IT solutions within the educational realm, emphasizing the potential to positively impact students' transition from education to the workforce.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".