Penentuan Prioritas Kerja Menggunakan Simple Addtive Weighting Method Berbasis Website
Bibliographic record
Abstract
Pemerintah desa dan perangkat desa menyusun Rencana Kerja Pembangunan Desa sebagai Rancangan Rencana Pembangunan Jangka Menengah Desa. Ada beberapa kendala dalam penetapan RKPDes karena tidak ada dasar yang sama untuk memutuskan pembangunan mana yang harus diprioritaskan. Pengambilan keputusan masih dalam bentuk musyawarah antar desa atau musyawarah desa dan tidak ada hal khusus yang perlu dipertimbangkan. Mekanisme penetapan program kerja dan penyusunan anggaran di wilayah desa Kedungede menerima informasi dari masyarakat dan memberikan saran kembali pada tingkat yang lebih tinggi seperti Musyawarah Gabungan Masyarakat dan Musyawarah Desa. Hal tersebut dapat diatasi dengan pembuatan fitur RKPDes berbasis website yang dilengkapi sistem penunjang keputusan menggunakan Metode Simple Additive Weighting dengan mengikuti model pengembangan sistem Waterfall. Penelitian ini dilakukan dengan cara pengumpulan data observasi, wawancara, studi pustaka dan pengembangan sistem. Penelitian ini menghasilkan aplikasi berbasis website yang membantu masyarakat untuk menyampaikan aspirasinya kepada pemerintah desa dengan kriteria volume, kebutuhan biaya, waktu penyelesaian, urgensi dan pemanfaatan.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".