Sertipikat Tanah VS Sertifikat Tanah: Analisis Data Penggunaan Istilah Produk Hukum di Media Sosial
Bibliographic record
Abstract
The land title (in Indonesian: sertipikat) is a legal product issued by the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency, while “sertifikat” (spelled with 'f' instead of 'p'), which is the standard term according to KBBI (Indonesian Dictionary). The number of social media users increases every year, resulting in a growing amount of data generated. Big data derived from social media can encompass public perception or societal behaviors. The significant benefits derived from social media analytics provide opportunities to explore and analyze data sources within social media. This study analyzes data from the social media platforms Twitter, Facebook, and Instagram to determine common terms used by the public regarding "sertipikat tanah" (land certificate) and "sertifikat tanah" (land certificate). These platforms were chosen because they provide open data. The use of uncommon terms among the public potentially hinders the government's intended objectives. The final results indicate that despite "sertipikat" being the official legal term, the public is more accustomed to using "sertifikat tanah." The comparison of usage between "sertipikat tanah" and "sertifikat tanah" on Facebook is 11% : 89%, on Instagram 38% : 62%, and on Twitter 70% : 30%, with the majority of "sertipikat tanah" usage on Twitter originating from government accounts.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".