Aplikasi ANP pada pengambilan keputusan pengadaan test bench fuel nozzle di PT. Merpati Nusantara Airlines
Bibliographic record
Abstract
Analytic Network Process (ANP) yang dikembangkan oleh Thomas L. Saaty, merupakan salah satu metode pengambilan keputusan terbaru yang mampu memperhitungkan hubungan secara dependensi dan feedback antar elemen pengambilan keputusan. Keunggulan lain dari metode ini adalah kemampuanya untuk memodelkan situasi pengambilan keputusan yang kompleks, abstrak dan terus berubah. Pada tugas akhir ini ANP diterapkan pada salah satu kasus yang dihadapi manajemen PT.Merpati Nusantara Airlines (PT.MNA), salah satu Badan Usaha Milik Negara (BUMN), untuk memutusakan alternatif terbaik dalam melakukan test bench fuel nozzle. alternatif yang ada antara lain, membangun sendiri sebuah alat test bench, membeli sebuah alat test bench siap pakai dan melakukan subkontrak ke bengkel di luar PT.MNA. Masing-masing alternatif tersebut mempunyai kekurangan dan kelebihan. Kasus ini juga dipengaruhi oleh Cluster atau kumpulan faktor lain yang mempengaruhi pengambilan keputusan, antara lain kondisi keuangan dan administrasi. Hasil analisa dengan metode ANP menyarankan agar PT. MNA melakukan pembangunan test bech . alternatif tersebut merupakan alternatif terbaik yang dapat dibenarkan sesuai dengan kondisi keuangan dan administrasi yang ada di PT.MNA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".