PENGARUH STEMMING TERHADAP EKSTRAKSI TOPIK MENGGUNAKAN METODE TF*IDF*DF PADA APLIKASI PDS
Bibliographic record
Abstract
Personal Digital Secretary (PDS) is a system that was developed to be a "personal secretary" who work alongside users digitally. PDS convey information to users in the form of email, social media and news. In order to know the information and news from the outside, it must be done by extracting user topics through email and social media, with the result that news information will have corresponding relationships with users. User topic extraction through email and social media in PDS is using modified weighting method in TF*IDF algorithm named TF*IDF*DF. In the further development, added stemming process in hopes of obtaining an appropriate topic. From the research that has been done, there are differences in terms obtained from the topic extraction without addition stemming process and with addition of stemming process. News information obtained by the addition of stemming process has more focused results than the news information obtained from the topics extraction without additional stemming process. With the addition of stemming process on the TF*IDF*DF algorithm indicates that the word (terms) results obtained from the extraction process has become the basic words because of stemming process. These Basic words are the basic form that an indication of a topicKeywords: User topic, topic extraction, TF*IDF, topic model, fiture selection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".