Penerapan Metode CVP Sebagai Alat Bantu Analisis Perencanaan Laba Dalam Mencapai Target Perusahaan ( Studi Kasus Mebel Bocah Angon Di Dusun Kalianyar Deket , Lamongan )
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui penerapan metode analisis cost. Analisis penelitian ini adalah analisis titik impas (Break Event Point / BEP) untuk menghitung margin of Safety (MoS). Diketahui Laba bersih sebesar 206.193.000.BEP Toko Mebel Bocah Angon adalah sebesar 382.758.621. Margin Kontribusi yang diperoleh Toko Mebel Bocah Angon bulan Januari-Desember 2018 adalah sebesar 314.799.000 sedangkan Ratio Margin adalah sebesar 29%.Margin of Safety Toko Mebel Bocah Angon adalah sebesar 711.841.379 ini berarti bahwa jika penjualan nyata produk berkurang atau menyimpang lebih besar dari 711.841.379 (dari penjualan yang direncanakan). Degree of operating leverage merupakan ukuran pada tingkat penjualan tertentu. Jadi dapat dikatakan bahwa operating leverage Toko Mebel Bocah Angon adalah sebesar 1,53 atau 15,3 % yang berarti setiap 1% kenaikan pendapatan penjualan akan mengkibatkan 15,3% kenaikan laba bersih. Laba target perencanan laba pada bulan januari-desember 2018 adalah sebesar 265.988.970 dengan harus mencapai target penjualan sebesar 1.299.962.000. Total Penjualan pada bulan Januari-Desember 2018 adalah sebesar 1.094.600.000 dengan biaya tetap sebesar 111.000.000 dan biaya variabel sebesar 779.801.000. Kata kunci: biaya, volume, laba, perencanaan laba.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".