Model Komunikasi Organisasi dalam Pengembangan KinerjaBalai Penyuluhan Keluarga Berencana(Kasus di Kabupaten Cianjur dan Indramayu Provinsi Jawa Barat)
Bibliographic record
Abstract
Balai penyuluhan Keluarga Berencana (KB) memiliki peran strategis dalam meningkatkan kualitas sumber daya manusia di Indonesia melalui pelaksanaan penyuluhan Program Pembangunan Keluarga, Kependudukan, dan Keluarga Berencana (Bangga Kencana). Penelitian ini bertujuan untuk menganalisis faktor penentu kinerja organisasi balai penyuluhan KB dan merumuskan model komunikasi organisasi yang dapat mendukung kinerja organisasi balai penyuluhan KB. Penelitian ini menggunakan paradigma positivistik, dengan pendekatan kuantitatif yang menggunakan analisis Partial Least Square-Structural Equation Model (PLS SEM) dan desain survei. Teknik pengambilan sampel menggunakan teknik acak bertingkat. Penelitian ini menentukan jumlah sampel dengan menggunakan rumus Slovin, dengan total sampel dalam penelitian ini sebanyak 430 sampel, dengan rincian 228 sampel di Kabupaten Cianjur dan 202 sampel di Kabupaten Indramayu. Hasil penelitian menunjukkan bahwa terdapat pengaruh positif dan signifikan dari kepuasan komunikasi tenaga lini lapangan dan efektivitas komunikasi organisasi di balai penyuluhan KB terhadap kinerja organisasi balai penyuluhan KB. Model kinerja organisasi balai penyuluhan KB berbasis komunikasi organisasi, berimplikasi pada perumusan konsep pengembangan kinerja organisasi balai penyuluhan KB dengan menerapkan konsep hubungan manusia dalam organisasi.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".