Pelatihan Pemanfaatan Sistem Informasi Pelaporan Retribusi Sampah
Bibliographic record
Abstract
Sistem pelaporan retribusi sampah yang ada saat ini di Kecamatan Manggala Kota Makassar, masih menggunakan sistem pelaporan retribusi sampah secara manual, yaitu pada saat pelaporan retribusi sampah dilaporkan pada petugas penagih retribusi sampah, dan harus melakukan pelaporan langsung ke kepala seksi kebersihan dengan membawa catatan hasil laporan tagihan retribusi sampah yang ditulis secara manual. Hal ini akan mempersulit proses pelaporan terhadap penagih yang dilakukan setiap bulan, karena data tidak sesuai hasil dari tagihan yang dicatat dilapangan karena sering terjadi kehilangan data. Oleh karena itu perlu adanya sistem untuk memudahkan penagih retribusi sampah dan mengefisienkan waktu dan biaya, sehingga proses pelaporan retribusi sampah lebih efiseian dibandingkan dengan pelaporan secara manual. Metode yang digunakan dalam kegiatan ini adalah metode ceramah dan metode tutorial. Hasil dari pengabdian masyarakat ini yaitu para pegawai kebersihan & pertanaman sangat merespon dengan adanya sistem informasi pelaporan retribusi sampah yaitu mereka langsung mengimplementasikan di Kantor Kelurahan Manggala untuk pelaporan retribusi sampah.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.049 |
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".