Analisa Price to Earning Ratio (PER) dalam Pengambilan Keputusan Investasi
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 study aims to analyze investment decisions using the Price to Earning Ratio (PER) analysis method in state-owned banking companies. The research method used is descriptive quantitative method with annualized first quarter financial report data for 2021. The results show the Price to Earning Ratio (PER) of BUMN banking stocks in that period is an average of 12.6x and based on Price to Earning Ratio analysis (PER) using the 12-month consensus share price divided by annualized net profit per share, the author makes the decision to invest in shares with a priority scale on BBTN, BBNI, BMRI and BBRI shares due to the low Price to Earning Ratio (PER) value and the potential for an increase in the next 12 months.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it