ANALISIS FUNDAMENTAL DENGAN PENDEKATAN PER PENGARUH \nTERHADAP HARGA SAHAM SEBAGAI DASAR UNTUK MENILAI \nKEWAJARAN HARGA SAHAM \n(Studi Pada Perusahaan Sektor Industri Manufaktur Yang Terdaftar di BEI Periode \nTahun 2008-2011)
Bibliographic record
Abstract
This research aims at measuring the appropriateness of share prices on shares of manufacture \ncompanies registered in the Indonesian Capital Market Directory (ICMD) in the period of 2008- \n2011. The normal PER found in a company is compared to Price-Earning Ratio PER) in another \ncompany using the share growth at is variable. In that case, those shares are classified into two \ngroups, appropriate shares group and inappropriate shares group to determine the influences of \nthose shares toward the price of the shares, the researcher used multiple linear regression. In \nmeasuring the appropriateness of the share prices, the reasearcher applied profit growth \nvariable approach with the formula: PER = 4 + 2,3 (profit growth) adapted by Suad Husnan. \nThis formula is used because a company which still undergoes profit growth or that which is \nrelatively new will have bigger PER as a result of its low profit level. The data collected are the \nlatest data on the annual-fourth quarter period from ETRADINGHOTS. The results of this \nresearch conducted by collecting data on the last quarter in 2008-2011 showed that most of the \nshares can be said annually inapproapriate. However, there are some of them can consistently \nmaintain the share prices during the period of 2008-2011. This condition provides investors with \ngood opportunity to buy those shares.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".