The Forecasting Analysis of Profit on Astra Companies List on Indonesia Stock Exchange (IDX)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research aims to analyze the profit forecasting using the weighted moving average method then compare the pattern between Astra companies listed on IDX (Indonesia Stock Exchange). The research method use is quantitative descriptive with secondary data of profit in the 2018-2022 period that provides quarterly. The result for this research shows the highest forecasting of profit on PT Astra International Tbk occurring in the third quarter in 2023 with the amount of 20,970. While the lowest forecasting occurred in first quarter in 2023 with the amount of 9,410. While the lowest forecasting occurred in first quarter in 2023 with the amount of 13,529. The highest forecasting of profit on PT United Tractors Tbk occurs in the third quarter in 2023 with the amount of 13,738,446. While the lowest forecasting occurred in first quarter in 2024 with the amount of 5,584,042. and the highest forecasting of profit on PT United Tractors Tbk occurs in the second quarter in 2023 with the amount of 50,707. While the lowest forecasting occurred in the third quarter in 2024 with the amount of 22,496.
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.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it