PELATIHAN INDENTIFIKASI INDEKS SAHAM USA UNTUK MEMPREDIKSI FLUKTUASI IHSG: LINCOM ANALYSIS PADA NASABAH TYPE SWINGER RELIANCE SURAKARTA
Bibliographic record
Abstract
ABSTRACT Capital market practices in the type of stock instruments undergo a shift in the way of analysis. This is not in line with the theory of stock fundamental analysis which explains that fundamental factors become the main variable in determining stock investment decisions for the long term. Fundamental analysis of stocks takes into account various factors including company performance, macroeconomic analysis and the industrial sector. The fundamental variable of stock analysis is used as a consideration of the investment portfolio of the stock for the long term. Users of these variables are usually the owners of capital with the type of investor. Investor type is the owner of capital with the main purpose of buying shares by expecting stock valuations in the long run and dividends, not short-term capital gains. The need for the development of applied science of technical analysis and fundamental analysis of stock investors who have a form of trading activities on a daily basis generally only conduct transactions on the capital market using speculation from each investor. This of course in terms of education that novice stock investors do must have the same time in obtaining maximum income in the trading stock market This service is carried out on customers of PT Reliance Surakarta. There were 13 training participants, namely customers who became stock investors but did not trade every day for a short period of time. The service was held for 1 day. Expected outputs from the event are expected that after attending the training the participants are expected to be able to carry out fundamental and technical analysis of JCI fluctuations on a daily basisKeywords: Stock index, JCI Fluctuation Prediction, Swinger Type
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".