Pre Trip Inspection Skid Tank pada PT. Pertamina Patra Niaga Integrated Terminal Cilacap Berbasis Website
Bibliographic record
Abstract
PT. Pertamina Patra Niaga Integrated Terminal Cilacap merupakan salah satu perusahaan yang bergerak dalam bidang bisnis Bahan Bakar Minyak (BBM) dan Liquefied Petroleum Gas (LPG) setiap harinya melaksanakan pendistribusian dengan banyak faktor bahaya melibatkan manusia, peralatan dan lingkungan yang dapat menimbulkan potensi kecelakaan kerja didalam proses kerjanya. Pemeriksaan mobil skid tank di PT. Pertamina Patra Niaga Integrated Terminal Cilacap masih dilakukan secara manual menggunakan kertas. Hal itu menyebabkan kurang efektif dan efisien dalam pelaksanaan, Oleh karena itu diperlukan sebuah website untuk membantu proses pemeriksaan dan penyajian informasi yang dibutuhkan dari hasil pemeriksaan mobil skid tank. Dengan menggunakan metode penelitian System Development Life Cycle (SDLC) waterfall yang meliputi tahap analisis, desain, pengembangan, implementasi menggunakan database Firebase dengan menggunakan database Mysql dan pemrograman Laravel Berdasarkan Penelitian ini diperoleh Website Pemeriksaan Mobil Skid Tank di PT. Pertamina Patra Niaga Integrated Terminal Cilacap untuk input data pemeriksaan, penyimpanan dan penyajiian hasil pemeriksaan tiap kendaraan yang telah dilaksanakan sebelum beroperasi. Website telah di uji Black Box dengan hasil seluruh menu yang tersedia dapat berjalan lancar sesuai harapan dan uji User Acceptance Test (UAT) dengan hasil diperoleh bahwa sistem yang dibuat memenuhi tujuan pembuatanya. Kata kunci : Website, Inspection, Skid Tank, System Development Life Cycle
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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 | 0.000 | 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 | 0.002 | 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) | 0.311 | 0.153 |
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".