Prediksi Tinggi Gelombang Laut di Perairan Laut Sulawesi Utara dengan Menggunakan Model Vector Autoregressive (VAR)
Bibliographic record
Abstract
Telah dilakukan penelitian tentang tinggi gelombang laut di perairan laut Sulawesi Utara yang bertujuan untuk menerapkan model Vector Autoregressive (VAR) dalam memprediksi tinggi gelombang laut di wilayah perairan Bitung, perairan Manado, dan perairan Tahuna. Model VAR merupakan salah satu model time series yang menghendaki pemodelan secara simultan dengan beberapa peubah. Data yang digunakan dalam penelitian ini adalah data rata-rata harian tinggi gelombang laut di wilayah perairan Bitung, wilayah perairan Manado, dan wilayah perairan Tahuna yang diperoleh dari Badan Meteorologi, Klimatologi, dan Geofisika Maritim Bitung pada periode Januari 2015 sampai Desember 2016. Hasil penelitian menunjukkan bahwa model yang sesuai untuk memprediksi tinggi gelombang laut yaitu model VAR(3) dimana model ini cukup baik untuk digunakan dalam memprediksi tinggi gelombang laut wilayah Bitung, Manado, dan Tahuna pada periode 5 hari yakni tanggal 01 Januari 2017 sampai 05 Januari 2017, wilayah Bitung memiliki rata-rata tinggi gelombang mencapai 1,6 - 1,7 Meter, untuk tinggi gelombang laut wilayah Manado mencapai 1,3 - 1,5 Meter, dan wilayah Tahuna mencapai tinggi gelombang sebesar 1,8 - 2,1 Meter.Kata Kunci : Model Vector Autoregressive (VAR), Tinggi Gelombang Laut, Sulawesi Utara
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".