Bibliographic record
Abstract
Errors in the application of Ejaan Bahasa Indonesia (EBI) in students' final assignments (TA) have the opportunity for ambiguity to occur so that the contents of the writing are less communicative. The purpose of this study was to describe errors in the application of EBI and the relationship between error variables in TA students of D-4 Study Program of Building Maintenance and Repair Engineering and Road and Bridge Design Engineering Study Program, Civil Engineering Department, Bandung State Polytechnic. This research is qualitative and quantitative with thirty TA data as samples taken at random in the department's library. The results showed that there were 544 spelling errors consisting of: 180 capital letter writing errors, 43 word writing errors, 38 numeric errors, and 7 cursive letter errors. There were 276 punctuation errors, consisting of: 142 comma errors, 82 period errors, 40 colons, and 12 semicolon errors. The results of the quantitative analysis show that the number of errors between the variables is relatively the same. The highest number of spelling errors was caused by errors in the use of capital letters, which was 64.4%. Most other errors are caused by sign point and word writing errors.
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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".