Analisis Kelayakan Desain Material Recovery Facility (Mrf) Dalam Pengelolaan Sampah Di Tpa Hutan Panjang Kota Banjarbaru
Bibliographic record
Abstract
Pengelolaan sampah di Kota Banjarbaru masih belum maksimal. Hal ini terlihat dari sampah yang dikumpulkan di TPS kemudian diangkut dan ditimbun begitu saja di landfill TPA tanpa pengolahan. Perencanaan Material Recovery Facility (MRF) di TPA Hutan Panjang Gunung Kupang Kota Banjarbaru dapat digunakan sebagai salah satu alternatif untuk mengurangi volume sampah yang masuk ke TPA Hutan Panjang, menghemat kebutuhan lahan landfill, dan memperpanjang umur lahan TPA. Pengelolaan yang dilakukan adalah komposting sampah organik dengan sistem open windrow dan pemanfaatan kembali sampah anorganik yang mempunyai nilai jual. Komponen MRF yang dibutuhkan adalah lahan pemilahan, lahan penempungan sampah organik, lahan pencampuran sampah organik dengan EM4 (biostater), lahan pengomposan, tempat penyimpanan sampah kering (barang sortir), tempat penyimpanan kompos serta kantor administrasi. Lahan yang dibutuhkan untuk bangunan pengolahan sampah (MRF) adalah 12031,5 m2 dan rencana anggaran biaya yang diperlukan untuk biaya pembangunan dan penyediaan peralatan MRF sebesar Rp 10.563.047.380,00. Berdasarkan analisis kelayakan ekonomi dengan berbagai alternatif pembiayaan baik itu dengan pembiayaan dari pemerintah ataupun dengan pinjaman lunak menunjukkan pembangunan MRF ini layak untuk direalisasikan. Kata kunci: Analisa Ekonomi, Banjarbaru, Komposting, Material Recovery Facility
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".