TINGKAT KEBISINGAN LINGKUNGAN SIANG MALAM (LSM) DI KAWASAN TERMINAL BUS BARANANGSIANG, KOTA BOGOR
Bibliographic record
Abstract
Revitalisasi Terminal Bus Baranangsiang Bogor sejak 2019 berdasarkan Rencana Induk Transportasi Jabodetabek 2018–2029 dengan kebijakan transportasi perkotaan ramah lingkungan. Kebisingan adalah masalah lingkungan yang belum diperhatikan. Penelitian ini bertujuan mengkaji tingkat kebisingan di Terminal Baranangsiang terhadap baku tingkat kebisingan 70dB(A) dan memetakannya untuk menggambarkan rona awal terminal. Metode pengukuran bising dan pengolahan data menggunakan KepMenLH 48/1996. Pengukurang kebisingan dengan Sound Level Meter di 6 titik sampling: Gerbang Masuk Terminal, Halte Penumpang, Gerbang Keluar Terminal Pos Polisi, Gerbang Keluar Terminal Utama, Menara Pengawas, dan Kios Zona C. Sampling dilakukan 6–19 September 2019 (2 minggu) untuk memperoleh kebisingan siang malam (LSM) terdiri 7 segmen waktu: 4 segmen LS (06.00–22.00) dan 3 segmen LM (22.00–06.00). Analisis kebisingan secara visual basic dan pemetaan kebisingan dengan Surfer-11. Penelitian menyimpulkan LSM berkisar 66,9–79,2dB(A), terendah di Titik 6 (Kios Zona C) dan tertinggi di Titik 4 (Gerbang Keluar Terminal Utama) yang cenderung melebihi 70dB(A). LSM pada hari libur (Sabtu–Minggu) cenderung lebih tinggi dibandingkan hari kerja (Senin–Jumat). Secara visual tingkat kebisingan terlihat melebihi 70dB(A). Pola sebaran kebisingan dipengaruhi arah angin dominan terkonsentrasi di Titik 4. Dampak kebisingan adalah ketidaknyamanan, gangguan pendengaran, dan psikologis. Upaya pengendalian kebisingan yaitu dengan memasang peredam bising.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".