ANALISA DERET WAKTU CURAH HUJAN DAN KARAKTERISTIK IKLIM DI KOTA MAJALENGKA
Bibliographic record
Abstract
Pemanasan global bukan lagi issue karena sudah menunjukkan dampak yang nyata. Peningkatan suhu pasti mengubah karakteristik hujan dan menimbulkan cuaca ekstrem yang berpotensi menimbulkan perubahan iklim dan bencana. Kota Majalengka harus melakukan analisa kerentanan terhadap bencana akibat perubahan iklim sehingga bisa melakukan antisipasi. Antisipasi bencana terutama kekeringan, yang sudah pernah terjadi beberapa kali. Antisipasi bisa dilakukan dengan melakukan proyeksi curah hujan dan mengetahui perubahan karakteristik iklim. Penelitian ini melakukan analisa deret waktu dan proyeksi jangka pendek menggunakan metoda Autoregressive Integrated Moving Average (ARIMA), berdasarkan data hujan harian maksimum selama 10 tahun (2011 – 2021) di Kota Majalengka. Analisa karakteristik iklim meliputi indeks musiman, tipe atau pola iklim dan frekuensi kejadian hujan. Kota Majalengka dalam kurun waktu 2011 – 2021 memiliki indeks musiman yang bervariasi antara 0,50 – 0,98 dan dengan indeks musiman rata-rata 0,79 yang mengklasifikasikan iklim di Kota Majalengka memiliki karakteristik musim yang selalu mulai dan berakhir pada bulan – bulan yang tetap tetapi cenderung memiliki musim kemarau yang lebih panjang. Kota Majalengka rentan terhadap bencana kekeringan. Model Arima terbaik untuk data hujan di Kota Majalengka dengan kurun waktu 2011 – 2020 adalah model ARIMA (3, 0. 1).
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.011 | 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".