Analisis Strategi Pengelolaan Sampah Berbasis Pengurangan (Reduksi) di Kecamatan Medan Helvetia Kota Medan
Bibliographic record
Abstract
This research aims to analyze reduction-based waste management strategies in Medan Helvetia District, Medan City. The method used is qualitative with a survey approach and using SWOT and AHP analysis. This research began by identifying waste reduction efforts that had been carried out by residents of Medan Helvetia District and then a Focus Group Discussion (FGD) was carried out which resulted in 3 activity targets to be developed, namely (1) strategies to increase community participation to join environmental care communities; (2) strategy to improve the performance of waste banks, and (3) strategy to implement the circular economy concept in waste management to support the improvement of the people's economy. Based on the results of the SWOT analysis, the appropriate strategy for realizing the first activity target is the integration of the waste management education program with the TP PKK work program as well as providing waste reduction information on social media and banners in each sub-district. The priority target for the second activity is placing outlets receiving segregated waste in strategic location in the sub-district as well as waste bank management training, and the third priority strategy target, namely partner collaboration and capital assistance as well as assistance with composter equipment to produce organic fertilizer. The results of the AHP analysis show that the priority strategy in waste management based on reduction is increasing community participation to join environmental care communities with a weight value of 0.446 with an inconsistency value of 0.00001. This research shows that to increase community participation, continuous education is needed to be able to sort organic and inorganic waste, as well as disseminate information and waste reduction campaigns on social media and banners in each sub-district.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".