PEMANFAATAN TEKNOLOGI DRONE GUNA MENDUKUNG TUGAS PENYELIDIKAN SATUAN ZENI TNI AD
Bibliographic record
Abstract
Perkembangan ilmu pengetahuan dan teknologi merebak ke segala lini kehidupan. Hal tersebut juga menjadi tantangan dalam dunia militer, salah satunya yaitu pemanfaatan teknologi drone untuk melaksanakan penyelidikan, pemetaan, dan pengawasan wilayah dalam mendukung tugas penyelidikan bagi satuan Zeni TNI AD. Drone atau Pesawat Udara Tanpa Awak (Unmanned Aerial Vehicle, UAV). Pemanfaatan drone untuk mendukung tugas penyelidikan zeni karena kondisi pelaksanaan tugas penyelidikan zeni Satuan Zeni TNI AD masih dilakukan secara manual dan belum ada kebijakan penggunaan drone serta personel dan taktiknya yang belum disiapkan. Penelitian ini bertujuan untuk mengkaji keuntungan, tantangan, dan saran rekomendasi terkait pemanfaatan drone dalam mendukung tugas penyelidikan Satuan Zeni TNI AD. Penelitian menggunankan metode kualitatif dengan pengumpulan data melalui studi pustaka dan observasi lapangan yang dianalisis secara deskripsi analisis. Hasil penelitian menunjukkan bahwa reformasi teknologi drone di tubuh Satuan Zeni adalah keniscayaan yang harus segera diwujudkan karena dengan pemanfaatan drone akan menghemat waktu, lebih aman dan lebih akurat atas hasil penyelidikan zeni yang dilakukan oleh prajurit Zeni TNI AD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".