Analisis Multi Kriteria Analisis Multi Kriteria Menggunakan Multi Attribute Utility Theory Dalam Seleksi Penerima Beasiswa
Bibliographic record
Abstract
Politeknik Kampar adalah sebuah perguruan tinggi kejuruan di Kabupaten Kampar, Provinsi Riau. Penetapan penerima beasiswa di perguruan tinggi masih bersifat manual dan membutuhkan waktu yang lama untuk mengambil keputusan yang tepat. Permasalahan tersebut dapat dianalisis dengan menggunakan metode sistem pendukung keputusan Multi Attribute Utility Theory (MAUT) supaya proses pengambilan keputusan dapat lebih cepat dilakukan. Dimana analisis metode MAUT yang cocok untuk kriteria yang banyak. Kriteria penilaian yang digunakan terdiri dari 10 kriteria antara lain pendapatan orang tua, status kepemilikan rumah, kondisi perumahan, jumlah tanggungan, status orang tua, raport, prestasi akademik, prestasi non akademik, kegiatan ekstrakurikuler dan pengalaman berorganisasi dengan total 50 dataset. Proses penelitian terdiri dari literatur ilmiah, identifikasi masalah, pengumpulan data, analisis metode MAUT, pemeringkatan dan penarikan kesimpulan. Hasil penelitian dengan menggunakan metode MAUT dapat membantu tim seleksi penerima beasiswa dengan proses yang cepat, efektif dan objektif dalam proses pengambilan keputusan.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".