Optimasi Rute Pengangkutan Sampah dengan Menggunakan Metode Nearest Neighbour (Studi Kasus: Kabupaten Manokwari, Papua Barat)
Bibliographic record
Abstract
Seiring dengan pertambahan jumlah penduduk, timbulan sampah yang harus diangkut pun akan meningkat di Kabupaten Manokwari. Sistem pengangkutan existing yang belum optimal mengakibatkan sampah belum seluruhnya terangkut tepat waktu dan menumpuk di beberapa titik Tempat Penampungan Sementara (TPS). Kabupaten Manowkari dipilih karena baru 3 distrik dari total 4 distrik yang mendapatkan pelayanan pengangkutan sampah. Penelitian ini dilakukan untuk mengoptimasi rute pengangkutan sampah existing di Kabupaten Manokwari dengan menggunakan metode Vehicle Routing Problem (VRP) sehingga mendapatkan rute tercepat. Dari hasil sampling, didapatkan bahwa rerata timbulan sampah di 3 (tiga) Distrik terlayani sebesar 0,334 kg/orang/hari. Dengan rute existing , pengangkutan sampah membutuhkan waktu 3.289 menit/hari untuk menyelesaikan seluruh pengangkutan dari 25 unit TPS ke TPA Masiepi yang berlokasi di Distrik Manokwari Selatan. Optimasi rute pengangkutan menghasilkan total waktu 3.038 menit/hari, yaitu 251 menit/hari lebih cepat dibandingkan kondisi existing . Jumlah truk pengangkutan sampah yang dibutuhkan pun lebih sedikit, yaitu 13 unit dibandingkan kondisi existing yang sebanyak 15 unit. Dari studi ini memperlihatkan bahwa optimasi rute pengangkutan sampah bukan hanya dapat menghemat waktu pengangkutan tetapi juga mengurangi kebutuhan truknya, sehingga dapat pula mengurangi kebutuhan biaya untuk pengelolaan sampah secara keseluruhan di Kabupaten Manokwari.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".