Sistem Prediksi Pertumbuhan Ekonomi Kabupaten Musi Rawas, Kabupaten Musi Rawas Utara Dan Kota Lubuklinggau Dengan Metode Regresi Linier
Bibliographic record
Abstract
The economic condition of a region in each period can increase or decrease by looking at changes in goods and services. An increase in economic activity is a process of changing economic conditions that occur in an area on an ongoing basis to get to a better state for a certain period of time. Economic growth is a benchmark in achieving the development of economic conditions in a region so that it has an impact on increasing people's welfare. South Sumatra's economic growth in the first quarter of 2021 improved compared to the previous quarter. Similar to economic growth in South Sumatra Province, the districts and cities in it (Musi Rawas Regency, North Musi Rawas and Lubuklinggau City) also experienced ups and downs of economic growth. With the current ups and downs of economic growth, Musi Rawas Regency, North Musi Rawas and Lubuklinggau City need accurate information about the picture of economic growth in the future, this is intended to be able to prepare various policies or actions so that the level of the economy in Musi Rawas Regency, Musi North Rawas and Lubuklinggau City can be increased. Based on this problem, Musi Rawas Regency, North Musi Rawas and Lubuklinggau City need a prediction system in order to see a picture of economic growth in the future. The purpose of this study is to design a prediction system that can predict the rate of economic growth in Musi Rawas Regency, North Musi Rawas and Lubuklinggau City. The method used in the prediction system is a simple linear regression method, the use of a simple linear regression method in this study due to the limited time of the study and used to determine the direction of the relationship between the independent variable and the dependent variable, whether it has a positive or negative relationship and to predict the value of the dependent variable if the value of the independent variable increases or decreases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".