Perbandingan Ketelitian Metode NDVI Melalui Software Global Mapper Dan Arcgis
Bibliographic record
Abstract
Normal Difference Vegetation Index (NDVI) merupakan algoritma untuk mendeteksi indeks vegetasi dari citra satelit. Pengolahan indeks vegetasi pada penelitian ini menggunakan algoritma NDVI dengan memanfaatkan kanal/band 5 (NIR) dan 4 (RED) pada Landsat 9 OLI 2 perekaman tanggal 26 Oktober 2024 Hasil pada penelitian ini menunjukkan terjadi perbedaan hasil analisis pada masing-masing kelas pada software Global Mapper dan ArcGIS. Luas Kelas Klassifikasi Kerapatan Vegetasi pada Software Global Mapper menunjukkan bahwa luas tertinggi pada kelas Non Vegetasi seluas 16696,2 Ha atau 46,82% diikuti Kelas Tingkat Kehijauan Tinggi seluas 5755,9 Ha atau 16,14% dan luas yang terendah pada kelas Kehijauan Sangat Rendah seluas 3245,7 Ha atau 9,1%. Sedangkan pada Software ArcGIS hasil yang diperoleh, luas Kelas klasifikasi tertinggi pada Kehijauan Rendah Seluas 9396,4 Ha atau 26,35% dan diikuti kelas Non Vegetasi seluas 9206,7 Ha atau 25,82%. Luas terendah ditempati kelas Kehijauan Tinggi seluas 3821,3 Ha atau 10,72%. Hasil uji ketelitian, menunjukkan bahwa menggunakan Software ArcGIS terdapat 8 area yang sesuai dengan kondisi eksisting atau sekitar 80% kesesuaian dari area sampel sedangkan dengan menggunakan software Global Mapper menunjukkan area yang sesuai dengan kondisi eksisting hanya 2 area atau 20% dari area sample. Kata Kunci: NDVI, ArcGIS, Global Mapper
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".