Penentuan Faktor Prioritas Yang Mempengaruhi Keberhasilan Pengembangan Kawasan Industri Nganjuk (KING) Di Kabupaten Nganjuk
Bibliographic record
Abstract
Pemerintah sedang merancang strategi untuk memulihkan ekonomi pasca-pandemi, dengan fokus pada proyek Kawasan Industri Nganjuk (KING) di Jawa Timur untuk mendorong ekonomi lokal. Proses pengembangan KING melibatkan tantangan kompleks seperti perencanaan infrastruktur, manajemen sumber daya manusia, dan adaptasi terhadap perubahan pasar. Penelitian ini bertujuan mengidentifikasi faktor-faktor yang mempengaruhi keberhasilan pengembangan KING di Nganjuk melalui dua tahapan analisis. Pertama, menggunakan analisis Delphi untuk menentukan variabel dan faktor yang mempengaruhi keberhasilan berdasarkan studi literatur. Kedua, menentukan faktor prioritas dengan analisis Importance Performance Analysis (IPA) menggunakan hasil dari analisis Delphi. Dari 37 kriteria yang terbagi menjadi 15 variabel, analisis IPA mengidentifikasi faktor prioritas, yaitu; luas lahan, penentuan zonasi, jaringan transportasi, kebutuhan air dan pengelolaan limbah, keamanan infrastruktur, kualitas udara dan air, pengelolaan limbah lingkungan, sumber energi terbarukan, integrasi teknologi baru, serta pemanfaatan Internet of Things (IoT).
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".