Sistem Pendukung Keputusan dalam Penentuan Jurusan Berdasarkan Minat Siswa SMK Harapan Stabat Menggunakan Metode SAW
Bibliographic record
Abstract
For prospective students, SMK Harapan Stabat offers five main subjects with a specialization system that does not require exams. The number of students majoring in office administration has increased, and majoring procedures have shifted from conventional assessments to skills-based assessments. The current method, however, is challenging, especially for the Assistant Curriculum Director because it relies on exam results reports. To solve this problem, this research creates a web-based application that uses the simple additional reduction (SAW) method. This application is intended to help school principals determine student majors more efficiently. The implementation results show that this application was well received by the students of SMK Harapan Stabat and can help them make decisions about their major. Currently, determining majors at SMK Harapan Stabat is not ideal because prospective students tend to choose majors according to their own wishes. In addition, schools only look at test scores without considering students' interests and talents as well as other factors that influence acceptance in certain majors. Collecting data on students' aptitudes, interests, and test scores is critical to improving this process. Therefore, to carry out more accurate calculations, the Simple Additive Weighting (SAW) method is needed. Schools can use this decision support system to make better decisions about the majors their students will take. Therefore, the choice of major is expected to be more appropriate to students' abilities and interests to support their future success.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".