Prediksi Pengaruh Kegiatan MBKM terhadap Mahasiswa menggunakan Metode K-Nearest Neighbor
Bibliographic record
Abstract
The Merdeka Belajar Kampus Merdeka (MBKM) program provides students the opportunity to study for one semester outside of their major, aiming to develop the soft and hard skills required in the workforce. One key component of this program is internships or practical work, which gives students hands-on experience in the professional world and the chance to build professional networks. This research uses the K-Nearest Neighbor (K-NN) method to predict the impact of MBKM activities on undergraduate students at STMIK Kaputama. Using the RapidMiner application, student data was tested to obtain the accuracy of predicting students' engagement in the MBKM program in the future. The test results show that the K-NN model has an accuracy of 75.34%, indicating that the model is fairly good at predicting the impact of the MBKM program on students.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".