Prediction Of New Students Using The Exponential Smoothing Method (Case Study: STMIK Kaputama)
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
With this prediction system for the number of new students, it functions to determine the priority of how many new students will be accepted in the following year. For each new teaching according to the factual and quite accurate new student data reports by implementing a computerized system, the data processing will be more precise and reduce errors in predicting it. This prediction will provide an overview based on the trend of the number of new students at STMIK KAPUTAMA. The method used is the exponential smoothing method by looking for how big the error is with different alpha values, namely 0.1 to 0.9. Each alpha tested will give different results. The purpose of the above calculation is to find out (a) alpha which produces the smallest forecast error. By taking data on the number of new students in the previous period, forecasting by determining the value of weight (a) alpha. The value of weight (a) alpha depends on the number of new students, where the nature of this forecasting determination of the value that is closest to the actual conditions. The forecasting results above are the closest to the overall number of new students alpha 0.9 is 1,207.9 and forecast error is 15.75.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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