Evaluasi Kinerja (Integrated Library Information System) IBRA sebagai Sarana Temu Kembali Informasi di Sekolah Dasar Muhammadiyah Sapen
Bibliographic record
Abstract
Di era teknologi informasi saat ini, sistem informasi perpustakaan menjadi hal yang sangat penting. Seperti halnya sistem informasi penelusuran informasi (Integrated Library Information System) IBRA yang dapat membantu pemustaka mencari informasi yang dibutuhkan. Penelitian berjudul Evaluasi Kinerja (Integrated Library Information System) IBRA sebagai Sarana Temu Kembali Informasi di SD Muhammadiyah Sapen ini merupakan penelitian kualitatif dengan menggunakan 6 kreteria evaluasi temu kembali informasi menurut Salton dan McGill yang dikutip oleh Chownhulury, yaitu covererage of the collection (Cakupan Koleksi), precesion, Respon Time’s, (rentan waktu), Effort atau upaya pengguna, covererage of the collection ( Cakupan Koleksi), Penyajian data. Hasil penelitian Tingkat keefktifan kinerja IBRA dan proses mengunakan pendekatan subjek dan judul hasil menunujukan bahwa nilai precision memiliki nilai efektif. Sistem tidak menampilkan waktu perolehan data, Sedangkan dalam segi upaya pengguna dalam penggunaan IBRA masih kurang, tidak ditemukannya menu help. Selain itu Tampilan sistem temu kembali perpustakaan SD Muhammadiyah Sapen cukup menarik, sehingga penguna akan merasa senang dan nyaman ketika memggunakan OPAC. Cakupan koleksi yang ditampilkan IBRA cukup lengkap lengkap. Terdapat pdf, tampilan sampul, data bibliografi. Namun belum menampilkan abstrak.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.020 |
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".