Web-Based Work Practice Report Guidance Management Information System in Schools Using the Waterfall Method
Bibliographic record
Abstract
In the current digital era, managing guidance on Field Work Practice (PKL) reports at SMKN 5 Takalar requires a more structured and efficient system. Conventional methods often lead to ambiguity in communication and scheduling, as well as a lack of good documentation. This research aims to develop a web-based PKL report guidance management information system at SMKN 5 Takalar using the Waterfall method, which includes the stages of needs analysis, design, implementation, testing, and maintenance. This system is designed to overcome the problems of lack of clarity in communication, scheduling, and lack of good documentation in guidance on PKL reports. The research results show that this system is able to make the guidance process more structured with interpretation results of 94%, well documented, and efficient. The features provided facilitate communication between students and teachers, track guidance progress, and improve the quality of PKL reports. Apart from that, this system also optimizes the arrangement of meeting schedules between supervising teachers and students, making it more efficient and structured. Thus, it is hoped that this web-based PKL reports guidance management information system can be an effective solution to overcome the problems faced in the PKL guidance process at SMKN 5 Takalar.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".