Logistic Model Tree and Decision Tree J48 Algorithms for Predicting the Length of Study Period
Bibliographic record
Abstract
One point to be assessed in the accreditation process in an institution is the length of the student's study period. The Informatics department in XYZ college has been accredited by the national accreditation bureau for higher education (BAN-PT), but the accreditation has the potential to be improved. One thing that affects the accreditation value is many students did not graduate on time. Therefore, the current study used available student data, both academic and non-academic, using data mining. Two model classifications were used, i.e. Logistic Model Tree (LMT) and Decision Tree J48. The study was aimed to compare LMT and Decision Tree J48 algorithm in predicting the length of student’s study and to find out the influence factors. The data were Informatics Engineering students who have graduated in February 2018 to February 2019 (135 records). Results showed that the LMT algorithm produced an accuracy rate of 71% better than Decision Tree J48 (62.8% accuracy) in predicting the length of the student’s study. The factors influencing the length of study of students are temporary grade point average (GPA) of the first semester, temporary GPA of the second semester, organizational status, and employment status.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".