PEMANFAATAN DUA METODE CLUSTERING DAN ASSOCIATION RULE TERHADAP PRESTASI BELAJAR BERDASARKAN NILAI MATA PELAJARAN SISWA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data mining is a series of processes to extract new information from a pile of data. Student learning achievements are the results obtained by students after undergoing the learning process. There are quite a lot of data on student achievement in SMK Taman Siswa Binjai. But the student data has not been utilized to the maximum, making it difficult for the School to monitor the progress of students in the school. Therefore, it is necessary to create a system to find out the implementation of Data Mining based on the K-Means Clustering Method and to know the centroid distance between 1 group and other groups and to know the implementation of Data Mining based on Apriori Algorithm and to know the Support and Confidence of student learning achievement towards eye scores study, discipline, and majors. With this system can provide benefits to the school to be able to provide knowledge about student achievement while attending teaching and learning activities and to students to be able to know their learning achievements are good what needs to be improved again and can improve it again. By implementing k-means and a priori data mining of student achievement data in 2016 - 2018, there were 604 data, and from 100 data produced 3 clusters, where 1 48 data clusters, 2 24 data clusters, 3 28 data clusters, and with the algorithm a priori produce 16 rules that are formed and get the best rule, if someone has a good enough course value (70.00 - 76.99) and has enough discipline, then most likely will be in the Department of Motorcycle Engineering with a supporting value of 9% and 88% certainty value.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it