HUBUNGAN KEJADIAN TANTRUM PADA FREKUENSI PENGGUNAAN GEDGET PADA ANAK PRASEKOLAH DI RA MASYITOH V, PAUD ANAK SHALEH, TK BINA ANAPRASA KARANGAYAR PAITON
Bibliographic record
Abstract
Perilaku tantrum ini salah satu bentuk ekspresi emosi yang dapat berupa kemarahan yang meledak-ledak sehingga orang tua menganggapnya sebagai perilaku yang tidak baik. American Academy of Pediatrics dan Canadian Association of Pediatricians menekankan bahwa anak usia 0-2 tahun tidak boleh terpapar teknologi sama sekali. Dan sampai saat ini masih banyak anak usia prasekolah yang emosi hanya karena tidak diperbolehkan bermain gadget, karena sangat berbahaya jika anak bermain gadget maka ia akan lupa dengan apa yang dipelajarinya di sekolah. Tujuan penelitian ini untuk mengetahui hubungan kejadian tantrum dengan frekuensi penggunaan gadget. Metode Penelitian korelasional dengan desain cross sectional digunakan pada penelitian ini. Sampel sejumlah 50 responden menggunakan purposive sampling. Pengumpulan data melalui kuesioner, data di analisis menggunakan uji Rank Spearman. Hasil membuktikan bahwa frekuensi tantrum 43% tinggi, dan frekuensi gedget 47% sering. Hasil tabulasi silang mendapatkan nilai p value 0,001, nilai p<0,05. Kesimpulan dari penelitian yang dilakukan, terdapat hubungan kejadian tantrum pada penggunaan frekuensi gedget. Dari output SPSS yang diperoleh angka koefesien korelasi sebesar .296** artinya tingkat kekuatan korelasi/hubungannya adalah hubungan yang cukup atau cukup kuat.
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.
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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".