IDENTIFIKASI TEMPAT TINGGAL, BEKERJA/DOMISILI DAN TUJUAN BERLIBUR PENDUDUK DENGAN DATA TWITTER
Bibliographic record
Abstract
Twitter merupakan salah satu jejaring sosial yang sangat populer dan banyak diminati kalangan masyarakat di seluruh dunia. Dengan menggunakan informasi pada tweet akun-akun di sosial media ini kita dapat memperkirakan letak tempat tinggal serta tempat domisili dari penduduk di suatu wilayah, khususnya Jabodetabek. Penelitian ini memperkirakan jumlah penduduk yang tinggal di suatu wilayah serta tempat domisilinya di wilayah Jabodetabek berdasarkan tweet yang dilakukan. Pengolahan data untuk peneletian ini menggunakan software berikut bahasa pemrograman R, serta aplikasi lai sebagai penunjang. Berdasarkan data yang ada dapat diketahui bahwa pengguna twitter pada bulan februari 2014 paling banyak bertempat tinggal di daerah Bekasi diikuti oleh jakarta timur dan jakarta pusat. Penduduk bekasi sendiri paling banyak berdomisili atau melakukan aktivitasnya di daerah Jakarta terutama Jakarta pusat.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".