APLIKASI PELAPORAN DATA OPERASIONAL SUMUR-SUMUR MINYAK DAN GAS DI PETROGAS ISLAND LIMITED SALAWATI BERBASIS WEB
Bibliographic record
Abstract
Tujuan melakukan penelitian ini untuk membantu para karyawan khususnya di divisi production maintenance dibagian clerck dalam melakukan pelaporan pendataan Sumur – Sumur Minyak dan Gas, sehingga dapat juga membantu meminimalkan penggunaan kertas secara berlebihan. Sistem ini menggunakan perangkat lunak XAMPP, PHP, dan MySQL. Xampp digunakan sebagai perangkat lunak web server, PHP digunakan sebagai bahasa pemograman script server-side yang didesain untuk pengembangan web, sedangkan MySQL digunakan sebagai perangkat lunak pembuatan database. Hasil dari penelitian ini adalah Aplikasi Pendataan Pelaporan Sumur – Sumur Minyak dan Gas yang sangat membantu pekerjaan admin Clerk dalam mengelola data Sumur Minyak dan Gas, dan data yamg dikelola admin clerk lebih teratur dan juga dapat meminimalakan penggunaan kertas secara berlebihan.bagi pemimpin lebih muda dalam mendapatkan dan melihat informasi data – data sumur dan Gas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 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".