Time Series Forecasting of Nickel Sales in Nickel Mining Companies Listed on Indonesia Stock Exchange (IDX)
Bibliographic record
Abstract
This research aims to analyze nickel sales forecasting using time series forecasting with the help of Microsoft Excel and then compare the pattern between Nickel Mining Companies listed on IDX (Indonesia Stock Exchange). This research uses a quantitative descriptive study with a forecasting method. The data used is secondary data, which is sales data contained in the financial statements of nickel mining companies listed on the IDX (Indonesia Stock Exchange) from 2015-2023. There are a total of 43 data. The results of this study show that the highest sales forecast from PT Aneka Tambang Tbk's nickel sales is in quarter 4 of 2024 of IDR 16,4 trillion, and the lowest forecast is in quarter 1 of 2024 of IDR 3,4 trillion. On the other hand, the highest nickel sales forecast from PT Vale Indonesia Tbk is in the 4th quarter of 2024 of IDR 19,3 trillion, and the lowest forecast is in the 1st quarter of 2024 of IDR 4,3 trillion. The patterns formed on the forecasting plot graphs of the two companies tend to be the same. The sales trend and forecasting trend are also the same, there is an increase and show good business prospects and nickel sales.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".