HUBUNGAN DURASI DAN FREKUENSI BERMAIN GAME ONLINE DENGAN MASALAH MENTAL EMOSIONAL REMAJA DI SMP NEGERI 1 JATIPURO KABUPATEN KARANGANYAR
Bibliographic record
Abstract
Game online menjadi permainan yang sangat marak dan sangat digemari. Game online memungkinkan pemain untuk bertemu dengan berbagi orang dari berbagai wilayah dan dari berbagai kategori usia. Bermain game online yang dilakukan dengan durasi dan frekuensi tinggi dapat mempengaruhi mental emosional pada penggunanya. Tujuan: Mengetahui hubungan durasi dan frekuensi bermain game online dengan masalah mental emosional remaja di SMP Negeri 1 Jatipuro Kabupaten Karanganyar. Metode: Penelitian kuantitatif dengan desain penelitian analitik korelasi dengan rancangan Cross Sectional. Sampel penelitian adalah 66 siswa/siswi kelas VII SMP Negeri 1 Jatipuro Kabupaten Karanganyar dengan teknik pengambilan sampel menggunakan purposive sampling. Instrumen penelitian menggunakan kuesioner durasi, frekuensi bermain game online, serta penilaian masalah mental emosional mengunakan kuisioner SDQ (Strength Difficulties Questionnaire). Analisis data dilakukan dengan menggunakan uji Kendall tau dan regresi linier berganda. Hasil: Sebanyak 27 orang (40,9%) bermain permainan game online dengan durasi sedang. sebanyak 27 orang (40,9%) bermain permainan game online dengan frekuensi sedang. 30 orang (45,5%) mempunyai masalah mental emosional kategori borderline. Hasil uji Kendall tau durasi dan frekuensi bermain game online dengan masalah mental emosional masing-masing diperoleh nilai p = 0,001. Hasil uji regresi linier berganda diperoleh nilai p = 0,001. Kesimpulan: Ada hubungan durasi dan frekuensi bermain game online dengan masalah mental emosional remaja di SMP Negeri 1 Jatipuro Kabupaten Karanganyar
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 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".