AUDIT SISTEM INFORMASI MENGGUNAKAN FRAMEWORK COBIT 5 PADA PERPUSTAKAAN SMP NEGERI 9 BINJAI
Bibliographic record
Abstract
Sistem informasi perpustakaan menjadi salah satu implementasi teknologi informasi penting di bidang pendidikan. SMP Negeri 9 Binjai telah menggunakan sistem informasi perpustakaan sejak 2019 untuk meningkatkan efisiensi layanan, namun masih menghadapi berbagai kendala seperti ketidaksesuaian data, antarmuka yang tidak ramah pengguna, serta kurangnya kebijakan manajemen risiko. Penelitian ini bertujuan untuk mengevaluasi kinerja sistem informasi perpustakaan di SMP Negeri 9 Binjai menggunakan framework COBIT 5, khususnya pada domain Deliver, Service, and Support (DSS). Penelitian ini menggunakan metode deskriptif dengan pendekatan kuantitatif. Data dikumpulkan melalui studi pustaka, wawancara, observasi, dan kuesioner. Analisis dilakukan pada lima proses utama domain DSS: Mengelola Operasi (DSS-01), Mengelola Permintaan Layanan dan Insiden (DSS- 02), Mengelola Masalah (DSS-03), Mengelola Keberlanjutan (DSS-04), dan Mengelola Layanan Keamanan (DSS-05). Hasil penelitian menunjukkan rata- rata tingkat kematangan sistem berada pada Level 3,46, dengan beberapa kesenjangan pada aspek pengelolaan operasi, keberlanjutan, dan keamanan layanan. Rekomendasi mencakup peningkatan pelatihan pengguna, penguatan manajemen risiko, penambahan fitur keamanan, dan perbaikan antarmuka sistem. Melalui rekomendasi berbasis framework COBIT 5, sistem informasi perpustakaan diharapkan dapat menjadi lebih andal, efisien, dan mendukung transformasi digital di dunia pendidikan.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.015 |
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".