Penerapan Metode P-Median dalam Penentuan Lokasi Optimal Tempat Penampungan Sementara (TPS) Sampah di Kabupaten Klaten
Bibliographic record
Abstract
Kabupaten Klaten yang memiliki luas wilayah 655,56 km2, terbagi menjadi 26 kecamatan, jumlah total penduduk 1.469.253 jiwa (2014) yang terbesar se-karesidenan Surakarta dan dengan tingkat kepadatan 2,241 jiwa/km2. Produksi sampah yang banyak dipengaruhi oleh jumlah penduduk. Ketika pengelolaan sampah tidak berjalan dengan baik akan menimbulkan bencana bagi wilayah sekitar. TPS resmi di Kabupaten Klaten berjumlah 161 TPS yang tersebar di 26 kecamatan. Agar pengelolaan sampah di Kabupaten Klaten bisa terkelola dengan baik, maka penentuan lokasi TPS harus tepat. Penentuan lokasi dan alokasi agar upaya pengolalan sampah di Kabupaten Klaten berjalan dengan baik dengan menggunakan metode P-Median merupakan bagian dari mixed integer liniear programming yang bertujuan untuk meminimumkan total waktu tempuh rata – rata. Hasil dari perhitungan P-Median untuk menentukan alokasi optimal pada tahun 2017 menunjukan jumlah TPS yang terpilih sebanyak 73 TPS untuk melayani 101 sumber sampah yang tersebar di Kabupaten Klaten dengan total kapasitas sebesar 675,5 m3 dan volume sumber sampah total sebanyak 440,6 m3/hari. Dengan demikian tidak terjadi penumpukan sampah di TPS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".