Evaluasi katalog online perpustakaan menggunakan pendekatan Salton and Mcgill
Bibliographic record
Abstract
Kajian ini bertujuan untuk evaluasi katalog online perpustakaan FIP UNY dalam perspektif manajemen dan pengguna perpustakaan menggunakan pendekatan Salton and McGill. Pendekatan penelitian yang digunakan adalah kualitatif, dengan jenis penelitian deskriptif-analitis. Data yang digunakan adalah data primer dan data sekunder. Hasil kajian menunjukkan bahwa 1) Nilai recall menunjukkan angka 30.61%, sedangkan nilai precision menunjukkan angka 75.92% untuk subjek pendidikan anak, dengan penggunaan pada simple search dan advanced search. 2) Sistem mampu menemukan informasi yang dibutuhkan dalam kurun waktu 0,4 detik. 3) Pada poin upaya pengguna, belum ada modul khusus yang disediakan untuk memudahkan pengguna dalam mengakses katalog online. Terutama pada bagian menu pengguna, di mana belum ada menu HELP, tentang tata cara menelusur. Perpustakaan hanya menyediakan fasilitas “Live Chat” dan “Ask Librarian”. 4) Tampilan katalog online sesuai dengan standar SLiMS Meranti. Kendala pengguna adalah apabila pengguna tidak menekan menu “detil cantuman”, maka pengguna tidak akan mengetahui keberadaan koleksi yang tersebar di tiga kampus. 5) Koleksi yang dihimpun perpustakaan FIP masih didominasi oleh buku teks. This study aims to evaluate the library's online catalog of UNY FIP in perspective of management and library users use the approach of Salton and McGill. The research approach used is qualitative, with a type of descriptive-analytical research. The data used are the primary data and secondary data. The results of a study indicates that 1) the value of recall demonstrated numbers 30.61%, while the value of precision showed numbers 75.92% at subject of child’s education, with usage on simple search and advanced search. 2) the system is able to find the needed information within 0.4 seconds. 3) On the point of user effort, there is no special module provided to facilitate users in accessing the online catalog. Especially in the user menu section, where there is no HELP menu, about how to search. The library only provides "Live Chat" and "Ask Librarian" facilities. 4) Display online catalog in accordance with SLiMS Meranti standard. The constraints of the user if the user is not pressing the menu "listing details", then the user will not know the existence of the collection dispersed across three campuses. 5) Collections compiled by the library FIP still dominated by textbooks.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".