Perencanaan Rumah Makan Vegetarian Ala Chinese Food di Kota Bekasi, Jawa Barat
Bibliographic record
Abstract
Restoran adalah salah satu bisnis paling populer di Indonesia. Khusus di wilayah Bekasi, Jawa Barat, data perkembangan bisnis restoran menunjukkan angka yang tinggi di wilayah tersebut. Selain itu, jumlah penduduk di kota Bekasi sudah mencapai 3,6 juta jiwa. Hal ini menjadi peluang bagi seorang pengusaha untuk membangun bisnis restoran. Untuk menciptakan nilai jual yang unik pada sebuah restoran, seorang pengusaha dapat membentuk restoran vegetarian dengan masakan khas China, karena banyak artikel berita yang mengatakan bahwa tren makanan sehat semakin populer. Rencana ini dibuat dengan tujuan utama memperjelas langkah awal pengusaha dalam membuat rencana bisnis restoran. Rencana tersebut menggunakan Business Model Canvas sebagai dasar pembuatan model bisnis yang mencakup perhitungan arus kas, resep makanan terperinci, dan desain restoran, serta model untuk mencapai titik impas modal. Hasil dari rencana ini adalah suatu bisnis model yang dapat dijadikan sebagai referensi atau diterapkan secara nyata.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".