Korelasi Untuk Mengetahui Prestasi Siswa Terhadap Sosial Ekonomi Keluarga, Kegiatan Siswa Diluar Lingkungan Sekolah Dan Tingkat Motivasi Belajar Siswa Menggunakan Metode Apriori (Studi Kasus : SMP Negeri 2 Binjai)
Bibliographic record
Abstract
Learning achievement is every learning activity carried out by students which will result in a change in themselves. The learning outcomes obtained by students are measured based on differences in behavior before and after learning is carried out. The economic conditions of students' families at SMP Negeri 2 Binjai have a significant influence on student learning achievement. Many students who come from families with economically disadvantaged backgrounds face various challenges that hinder the learning process. Financial limitations often mean they do not have adequate access to educational resources, such as books, the internet, and additional tutoring which can help improve understanding of subject matter. This research uses the Apriori method as a problem solving method, namely to correlate between Family Socio-Economics, Activities Students Outside the School Environment and Level of Student Learning Motivation with Student Achievement in class. If data A, G, K → O with Support 30% and Confident 100% and S*C value 30%. So, if a student from a family with an income of less than Rp. 1,000,000 who take part in extracurricular activities outside of school, and have family-driven motivation, will have academic achievement with good report cards. This research indicates that family socio-economic conditions have a significant impact on student academic achievement. Through data analysis, it can be seen that factors such as family income, student activities outside the school environment and the level of student motivation to learn can influence the extent to which students can achieve higher academic achievement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".