5. ANALISIS PENGENDALIAN PERSEDIAAN SUKU CADANG MAIN TIRE PESAWAT C-130 HERCULES SKADRON UDARA 31 MENGGUNAKAN METODE ECONOMIC ORDER QUANTITY (EOQ)
Bibliographic record
Abstract
Penelitian ini dilakukan di Skadron Udara 31 Lanud Halim Perdanakusuma padasalah satu Skadron Udara yang dimiliki oleh TNI Angkatan Udara yang mengawaki pesawatangkut berat yaitu C-130 Hercules. Pesawat C-130 Hercules adalah pesawat buatanLockheed Martin, Amerika Serikat. Tujuan penelitian adalah untuk menentukan metodeperamalan yang sesuai untuk memenuhi kebutuhan suku cadang Main Tire pesawat yangoptimal. Selanjutnya menghitung jumlah persediaan pengaman (safety stock) suku cadangMain Tire yang seharusnya disediakan perusahaan. Dan yang terakhir menghitung tingkatbiaya persediaan suku cadang Main Tire pesawat yang optimum berdasarkan EconomicOrder Quantity (EOQ). Pesawat C-130 Hercules sering digunakan untuk mendukungoperasional TNI Angkatan Udara, namun sempat terjadi kekosongan Main Tire maka dariitu perlu adanya penanganan persediaan agar kegiatan operasional menjadi optimal. Untukmenjaga persediaan suku cadang Main Tire pesawat. Metode penelitian yang digunakanuntuk Pengendalian Persediaan Main Tire pesawat ini menggunakan metode peramalanMoving Average, Exponential Smoothing, dan Seasonal Indeks. Selanjutnya Menghitungpersediaan Main Tire pesawat, dengan menggunakan metode Ecconomic Order Quanitity.Agar dapat mengetahui berapa persediaan Main Tire pesawat yang optimal, mengetahuiberapa Safety Stock dan titi Re-Order Point nya. Hasil perhitungan MSE, didapatkan hasilyang paling terkecil yaitu 50 dengan menggunakan metode Single Exponential Smoothing.Economic Order Quantity (EOQ) yang mendapatkan hasil sebesar 30, Frekuensi Pemesanan(I) sebesar 2 Kali. Total Biaya Persediaan (TIC) sebesar Rp.16.280.000, safety stock (SS)sebesar 8, dan Reorder Point (ROP) sebesar 11.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".