Strategi Pengembangan Bank Sampah Sahdu Skala Kelurahan di Desa Tanimulya Kabupaten Bandung Barat
Bibliographic record
Abstract
Out of four waste banks in the Tanimulya Village, Sahdu Waste Bank is the only proper waste bank in operation. Based on the 2019 West Bandung District’s Environmental Agency (Dinas Lingkungan Hidup Kebersihan) Strategic Plan, which stated the target in 2022, that it needs to reduce 10%, from the current 8% reduction of the waste generation through the solid waste management and waste bank facilites, prior to the landfill. One of the efforts that can be done is through the development of the Sahdu Waste Bank from the hamlet scale to the urban village scale by using the Waste Bank Indexing Method. Assessing the existing condition with the waste bank index, identification and compiling the recommendation towards parameters that need improvement. Based on the results of the assessment, a score of 53.2 (out of 100, and is considered as fairly good category), reveals 14 sub indicator that can be improved, which consist of 6 Sub Indicator of Management System, 6 Sub Indicator of Operational System, and 2 Sub Indicator of Waste Bank Facility. The value of the Sahdu Waste Bank can be increased to 88.3 (out of 100, and is considered as very good), which generates the reduction of 266.67% of waste, equal to 4 ton/month from 150 kg/month. That would make Sahdu Waste Bank contributes 1.6% from the reduction target of 10% for the West Bandung District waste.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".