KAJIAN PEMANFAATAN DATA GOOGLE MAPS DALAM OFFICIAL STATISTICS
Bibliographic record
Abstract
Publikasi statistik usaha penyediaan makan minum yang diterbitkan oleh BPS tidak bisa memfasilitasi pebisnis dalam mengidentifikasikan daerah yang berpotensi memiliki kemampuan untuk dikembangkan usaha pada sektor penyediaan makan dan minum. Selain itu, adanya keterbatasan waktu, biaya, dan tenaga dalam pengumpulan data oleh Subdirektorat Pariwisata BPS pada survei VREST sehingga, menyebabkan statistik penyediaan makan minum tidak bisa di terbitkan sesuai metodologi yaitu setiap tahun. Penelitian ini memanfaatkan metode web scraping untuk mendapatkan data usaha penyedia makan minum dari situs web google maps. Jumlah data yang terkumpul sebanyak 34.526 usaha penyedia makan minum di Pulau Jawa dan Bali. Hasil nilai pencocokan data hasil web scraping dengan data frame BPS menunjukkan persentase kemiripan (match) sebesar 68,22%. Provinsi Bali adalah daerah yang memiliki potensi untuk mengembangkan usaha penyediaan makanan minuman terkhusus pada Kota/Kabupaten Jembrana, Buleleng, Tabanan, Karangasem, dan Klungkung. Sedangkan, provinsi Jawa Tengah adalah daerah yang memiliki potensi untuk mengembangkan usaha akomodasi terkhusus pada Kota/Kabupaten Cilacap, Blora, Grobogan, Batang, dan Kendal.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.031 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.024 |
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".