Audit Sistem Informasi Absensi Karyawan Pada PT. SALAPIAN INDO SAWIT (SIS) Menggunakan Framework Cobit 5
Bibliographic record
Abstract
Salapian Indo Sawit (SIS) merupakan perusahaan yang bergerak di bidang perkebunan kelapa sawit di Indonesia. PT. SIS ini berlokasi di Ujung Teran, Kec. Salapian, Kabupaten Langkat. Perusahaan ini terlibat dalam berbagai aspek industri kelapa sawit, mulai dari budidaya, pengolahan dan distribusi produk kelapa sawit. Penelitian ini bertujuan untuk membuat sistem informasi kehadiran karyawan di PT. Salapian Indo Sawit (SIS) menggunakan cobit 5 untuk estimasi efektivitas, efisiensi dan keamanan sistem. Sistem absensi yang digunakan perusahaan ini mempengaruhi proses pengelolaan data karyawan yang meliputi pencatatan kehadiran, penggajian dan waktu manajemen. Audit ini mencakup penilaian terhadap perangkat lunak yang digunakan, proses operasional yang ada, dan pengendalian internal yang diterapkan. Metode yang digunakan dalam penelitian ini adalah pendekatan deskriptif kualitatif, observasi dan analisis dokumenter. Hasil auditnya bantuan menunjukkan bahwa terdapat beberapa kelemahan dalam sistem, seperti ketergantungan pada prosedur manual dan kurangnya kontrol terhadap keakuratan data yang dimasukkan. Penelitian ini memberikan rekomendasi perbaikan sistem melalui penerapan teknologi yang lebih canggih dan perbaikan prosedur operasional yang mendukung manajemen perawatan yang lebih efektif dan efisien.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.012 |
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".