Penerapan Sistem Pendukung Keputusan Dengan Metode Fuzzy AHP Untuk Penentuan Kriteria Prioritas Pelayanan Dikementrian Agama Kota Binjai
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menerapkan Sistem Pendukung Keputusan (SPK) dengan metode Fuzzy Analytical Hierarchy Process (F-AHP) untuk menentukan prioritas pelayanan di Kementerian Agama Kota Binjai. Metode F-AHP digunakan untuk mengatasi ketidakpastian dan subjektivitas dalam pengambilan keputusan multi-kriteria. Kriteria yang digunakan meliputi: Tingkat Kebutuhan Masyarakat, Dampak Sosial, Aksesibilitas Pelayanan, Kesiapan Sumber Daya, dan Dukungan Kebijakan/Regulasi. Hasil penelitian menunjukkan bahwa Tingkat Kebutuhan Masyarakat merupakan kriteria paling dominan dengan bobot 1,0. Dari sepuluh alternatif pelayanan, Fasilitasi Perayaan Hari Besar Agama Non-Muslim dan Penyusunan Kurikulum Pendidikan Agama Non-Muslim di Sekolah memperoleh skor tertinggi (5,0), sedangkan Pengadaan Buku & Media Ajar Agama Non-Muslim memiliki skor terendah (1,0). Sistem yang dibangun menggunakan PHP dan MySQL mampu menghasilkan perangkingan yang objektif dan terstruktur, sehingga dapat mendukung pengambilan keputusan yang lebih akurat dan konsisten
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".