Application of the K-Nearest Neighbor (KNN) Algorithm in Machine Learning to Predict the Selection of Undergraduate Study Programs Based on New KIP Lecture Students
Bibliographic record
Abstract
Higher education plays a vital role in shaping the future of individuals and society. Choosing the right study program is an important decision for every student, because it will affect their career path and personal development. The KIP Lecture program is present as a government initiative to provide higher education opportunities to students from underprivileged families. However, with the many options of study programs available, new students often have difficulty in determining the study program that suits their interests and abilities. On the other hand, the data of new students that is quite complete and varied opens up opportunities to use machine learning technology in helping the study program selection process. The K-Nearest Neighbor (KNN) algorithm as one of the simple and easy-to-implement machine learning algorithms has the potential to provide more accurate recommendations for the selection of study programs based on student data at STMIK Kaputama. Therefore, this study focuses on analyzing the use of the KNN algorithm in machine learning to predict the selection of undergraduate study programs. This research aims to identify existing problems, evaluate the effectiveness of KNN in this context, and provide solutions that can be implemented to improve the study program selection process for new students who receive KIP Lecture. It is hoped that it can provide recommendations for the selection of study programs that are more accurate and relevant for new students who receive KIP Lecture at STMIK Kaputama. In addition, this solution can also increase the effectiveness of academic guidance and assist students in achieving better academic and career success.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".