PEMANFAATAN TEKNOLOGI GIS PADA PROGRAM PENDAMPINGAN PERLUASAN AREAL TANAM PADI DI KABUPATEN PAKPAK BHARAT
Bibliographic record
Abstract
Pelaporan Perluasan Areal Tanam (PAT) padi di Kabupaten Pakpak Bharat menghadapi permasalahan signifikan, di mana proses survei yang dilakukan secara acak di lapangan sering kali menghasilkan pelaporan data yang lambat dan tidak akurat. Selain itu, seringkali terjadi tumpang tindih dengan data luas baku sawah tadah hujan dari tahun sebelumnya. Penelitian ini bertujuan untuk menganalisis pemanfaatan teknologi Geographic Information System (GIS) dalam pelaporan data PAT padi sawah, khususnya pada kegiatan pendampingan Program Antisipasi Darurat Pangan di Kabupaten Pakpak Bharat. Metode yang digunakan adalah tracking menggunakan aplikasi Avenza Maps, dengan survei lapangan pada 17 titik koordinat yang merepresentasikan area sawah tadah hujan. Hasil penelitian menunjukkan bahwa akurasi tracking sawah tadah hujan melalui pemanfatan teknologi GIS sebesar 100% dengan capaian PAT padi sawah sebesar 1.204%. Penerapan peta GIS melalui aplikasi Avenza Maps terbukti sangat bermanfaat dalam membantu memvisualisasikan data, meningkatkan akurasi dan akselerasi pelaporan perluasan areal tanam padi di Kabupaten Pakpak Bharat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 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".