Sistem Pengambilan Keputusan Penentuan Prioritas Pekerjaan Smart City Menggunakan Metode Analytic Hierarchy Process (AHP), Studi Kasus Kabupaten Kotabaru
Bibliographic record
Abstract
Transformasi digital melalui pengembangan Smart City menjadi salah satu strategi utama dalam meningkatkan efisiensi tata kelola, kualitas pelayanan publik, dan daya saing daerah. Kabupaten Kotabaru telah menyusun Masterplan Smart City dengan berbagai program strategis. Namun, keterbatasan sumber daya menuntut adanya penentuan prioritas pekerjaan yang sistematis. Penelitian ini bertujuan menentukan urutan prioritas pekerjaan berdasarkan metode Analytic Hierarchy Process (AHP). Tiga kriteria utama yang digunakan adalah urgensi, dampak strategis, dan ketergantungan antar kegiatan. Diperoleh Nilai Prioritas AHP yaitu tinggi (AHP > 0.11), sedang (0.09 ≤ AHP ≤ 0.11) dan rendah (AHP < 0.09). Nilai hasil uji consistency ratio (CR) < 0,1 menunjukkan hasil yang dapat diterima dan reliabel. Berdasarkan hasil AHP, pembentukan kelembagaan Smart City menempati prioritas tertinggi, diikuti finalisasi SOP dan e-Gov. Hasil penelitian ini diharapkan dapat dijadikan acuan kuantitatif dalam pengambilan keputusan bagi pemerintah daerah untuk mengoptimalkan alokasi sumber daya dan memastikan inisiatif Smart City dapat dijalankan secara efektif.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".