Studi Pemanfaatan Arus Laut Sebagai Sumber Energi Listrik Alternatif di Wilayah Selat Sunda
Bibliographic record
Abstract
Sumber energi untuk pembangkit listrik yang berasal dari fosil semakin menipis membuat manusia harus mencari sumber energi alternatif seperti sumber energi alternatif arus laut. Salah satu sumber energi alternatif arus laut berada di wilayah selat Sunda, dengan kecepatan arus laut rata-rata per bulan sebesar 0,66-1,10m/s selama satu tahun. Besarnya potensi ini dapat dimanfaatkan pada skema PLTAL (Pembangkit Listrik Tenaga Arus Laut) sebagai pembangkit listrik tambahan untuk meningkatkan produksi listrik di wilayah selat Sunda. Energi listrik yang dihasilkan tanpa nilai konstanta efisiensi turbin untuk kecepatan arus laut minimum 0,66m/s sebesar 5,89kW dan maksimum 1,10m/s sebesar 27,28kW, sedangkan energi listrik yang dihasilkan dengan nilai konstanta efisiensi turbin untuk kecepatan arus laut minimum 0,66m/s sebesar 2,06kW dan maksimum 1,10m/s sebesar 9,54kW. Besarnya biaya pembangkitan sebesar Rp.452/kWh dan harga jual listrik sebesar Rp.519/kWh, dengan jumlah pendapatan pertahun didapat sebesar Rp.58.365.567,36/tahun. Hasil studi kelayakan secara finansial diperoleh PLTAL di wilayah selat Sunda cukup layak dengan ROR sebesar 22,88%, dan biaya modal investasi dapat kembali dalam waktu 5 tahun.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".