PERANCANGAN APLIKASI PERHITUNGAN BIAYA PERAWATAN TANAMAN KELAPA SAWIT PADA PT. LANGKAT NUSANTARA KEPONG (LNK) KEBUN BEKIUN SEBAGAI SOLUSI EFISIENSI BIAYA PERAWATAN
Bibliographic record
Abstract
PT.Langkat Nusantara Kepong (LNK) Bekiun Gardens is one of the state owned enterprises (SOE) in North Sumatra, which is engaged in oil palm plantations, the calculation of plant maintenance costs of palm oil is a series of procedure for calculating the overall costs in plants Palm oil. In this case PT.Langkat Nusantara Kepong (LNK) Gardens Bekiun calculate plant maintenance costs of palm oil are still using Microsoft Excel with this state of affairs is not effective and efficient. With the implementation of the application system maintenance costs calculation may make it easier to perform calculations plant maintenance costs of palm oil to be more disciplined time and data security was more secure. The system is designed using the programming language Microsoft Visual Studio 2010 and SQL Sever 2005 database. Results from this study is the calculation process plant maintenance costs of palm where the design begins with entering data maintenance cost of oil palm trees next admin calculate plant maintenance costs of palm oil and displays them in the form of reports.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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; both teacher heads agree on what is shown here.
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".