Analisis Penggunaan Aset Dalam Mengukur Profitabilitas Pada CV. Indo Akebono Ohta Medan
Bibliographic record
Abstract
CV. Indo Akebono Ohta is the official agent of JNE located in JNE Branch area \nof Medan. The company is engaged in shipping and logistics headquartered in \nJakarta, Indonesia. This study aims to determine and analyze the relevance of \nactivity ratios and profitability on the CV. Indo Akebono Ohta Medan. \nThis research includes research with descriptive analytical approach that analyze \nthe relation of asset usage which represented activity ratio and profitability, by \nlooking at activity ratio growth and profitability when previous year compare it \nwith previous year. Sources of data in this study are primary and secondary data. \nTechnical data with study data, literature study. \nUse of assets on CV. Indo Akebono Ohta Medan is still unfavorable, because the \nactivity ratio in the last 3 years (2013 - 2015) tends to rise, and the profitability of \nthe company fluctuated in 2014, but decreased in 2015. This condition shows the \nrelationship of activity and profitability ratio in CV. Indo Akebono Ohta Medan, \nwhere the decline occurred in the ratio of activity in
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".