Analisis Peramalan Volume Ekspor Melon di PT Bumi Sari Lestari Temanggung Jawa Tengah
Bibliographic record
Abstract
Export is an activity of sending goods abroad carried out by a company to increase profits and obtain a better selling price. Companies can optimize profits by minimizing uncertainty in the future by calculating sales forecasting which is useful for planning product inventory to be marketed. PT. Bumi Sari Lestari is one of the exporters in Central Java which exports one of the vegetable and fruit horticultural commodities, namely melons. The purpose of this study was to determine how much the forecast value of the volume of melon exports for the first quarter and second quarter of 2020 at PT. Bumi Sari Lestari uses the trend analysis method. This research was conducted on January 13, 2020 - February 9, 2020 at PT. Bumi Sari Lestari, Temanggung, Central Java. Determination The location of the study was determined intentionally (purposive). The research method used in this research is a case study. The data used are PT Bumi Sari Lestari's melon export sales data in the period of 2017-2019 (time series), monthly data analyzed quarterly from January 2017 - December 2019 with a total of 12 observations. The data analysis method uses the quadratic trend analysis method. The data stationarity test results show that the data is stationary. Melon export volume forecasting results at PT. Bumi Sari Lestari using the quadratic trend method gets results for forecasting in the first quarter of 2020 amounted to 15,767,427 kg and in the second quarter of 2020 amounted to 9,916,788 kg.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".