ANALISA PERUBAHAN GARIS PANTAI MENGGUNAKAN TEKNOLOGI PENGINDERAAN JAUH DI WILAYAH PESISIR KECAMATAN LAKUDO KABUPATEN BUTON TENGAH
Bibliographic record
Abstract
Garis pantai adalah garis batas pertemuan antara daratan dan air laut, dimana posisinya tidak tetap dan dapat berpindah sesuai dengan pasang surut air laut dan erosi pantai. Penelitian ini bertujuan untuk mengestimasi perubahan garis pantai dan mengestimasi laju perubahan garis pantai menggunakan data Citra dari Tahun 1998-2018 di Wilayah Pesisir Kecamatan Lakudo, Kabupaten Buton Tengah. Penelitian ini dilaksanakan pada bulan Januari-Mei Tahun 2019. Metode yang digunakan dalam penelitian ini adalah metode Overlay (tumpang susun) antara Citra Landsat 5 TM Tahun 1998, Citra Landsat 7 +ETM Tahun 2001 dan 2010 dan Citra Landsat 8 OLI Tahun 2018. Hasil penelitian menunjukkan bahwa selama 20 tahun perubahan garis pantai yang terjadi di lokasi peneltian berupa abrasi dan akresi. Perubahan garis pantai berupa abrasi berkisar antara 11-156 m terjadi di Desa Lolibu, Wajogu, Moko, Mone, Teluk Lasongko, Matawine, Wongko Lakudo, Lakudo, Gu Timur, Nepa Mekar, Boneoge, Waara dan One Waara. Sedangkan akresi berkisar antara 10-102 m terjadi di Desa Lolibu, Moko, Mone, Teluk Lasongko, Wongko Lakudo, Lakudo, Gu Timur, Nepa Mekar, Boneoge, Mandongka, Waara dan One Waara. Laju perubahan garis pantai berupa abrasi berkisar antara 0.55-7.80 m/thn sedangkan akresi berkisar antara 0.50-5.10 m/thn. Perubahan tersebut utamanya disebabkan oleh faktor hidro-oseanografi yakni arus, pasut dan gelombang serta faktor antropogenik yakni pembangunan pemukiman, penambangan pasir dan degradasi hutan mangrove.Kata Kunci: Perubahan Garis Pantai, Citra Landsat, Kecamatan Lakudo
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".