ASSESSMENT GREENSHIP NEIGHBORHOOD VERSI 1.0 PADA PERUMAHAN MENGGUNAKAN LOGIKA FUZZY
Bibliographic record
Abstract
Greenship neighborhood merupakan salah satu sertifikasi greenship yang dicanangkan oleh Green Building Council Indonesia yang menilai greenship untuk kawasan. Penerapan greenship neighborhood masih tergolong baru diantara sertifikasi greenship yang lain. Hal ini dibuktikan dengan belum adanya kawasan yang tersertifikasi greenship neihborhood. Tujuan dari penelitian ini adalah untuk menilai peringkat yang diperoleh Perumahan Kaliurang Green Garden, Kabupaten Jember yang nantinya dilakukan perencanaan peningkatan untuk mencapai peringkat gold. Penilaian dilakukan dengan menggunakan kuesioner dan wawancara kepada pihak pengembang. Hasil tersebut kemudian diolah menggunakan metode logika fuzzy melalui aplikasi MatLab yang mengacu pada panduan Greenship Neighborhood versi 1.0. Hasil MatLab menunjukkan Perumahan Kaliurang Green Garden belum mendapat peringkat greenship. Berdasarkan hasil tersebut kemudian dilakukan peningkatan untuk mencapai peringkat gold. Setelah dilakukan upaya peningkatan, hasil penilaian maksimal yang dapat dicapai Perumahan Kaliurang Green Garden adalah silver dengan poin 65,1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.042 | 0.006 |
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".