Data Mining Predicts the Number of Scout Enthusiasts in Binjai City Using the Linear Regression Method
Bibliographic record
Abstract
This study aims to predict the number of scout enthusiasts in Binjai City using a linear regression method. The data used includes the number of active scout members from the Alert, Fundraiser, and Enforcer levels during the 2018-2024 school year. The linear regression method was chosen because of its ability to predict future trends based on historical data. This research implements a linear regression algorithm through the Python programming language for the development of prediction systems. The problem in this study is that scout education in Binjai City has many impacts that can affect the number of scout enthusiasts in an educational institution. Some of the factors that can affect the quality of the teacher can be a determining factor in the interest in participating in scouting. With the existence of qualified scout teaching staff and creativity, students will be more in demand to participate in scouting.and the achievement of providing facilities provided from the School Level to the Branch Committee can also be a factor in attracting interest in participating in scouting and the support of school principals and the surrounding government can also be a determining factor in the development of scouting in the region so that activities from the school level to the international level are carried out which will be more in demand by scouting students. This finding is expected to help the Branch of the Binjai City Scout Movement in preparing and optimizing the number of scouting students in the future. This value means that the relationship between the free variable / predictor X and the bound variable / response Y is a low increase, the percentage is 93%. With the overall interest of 3191 scout members active scout members in the coming year with the number of Standby (1756 people), Fundraisers (662 people), Enforcers (773 people) So, the interest of scouts in the city of Binjai is influenced by the number of active Scout members this year.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".