PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI KEJADIAN DBD (DEMAM BERDARAH DENGUE) MENGGUNAKAN REGRESI LOGISTIK BINER UNTUK WILAYAH REGIONAL 2 INDONESIA (SUMATERA)
Bibliographic record
Abstract
Abstrak. Penelitian ini bertujuan untuk menjelaskan beberapa faktor yang mempengaruhikejadian Demam Berdarah Dengue (DBD) pada kabupaten atau kota diwilayah regional 2 Indonesia (Sumatera) tahun 2012. Faktor-faktor tersebut menggunakanmetode Regresi Logistik Biner yang merupakan salah satu teknik estimasi parameterdengan pendekatan likelihood. Pada penelitian ini diperoleh tiga variabel prediktoryang berpengaruh signikan terhadap kejadian demam berdarah dengue. Variabel tersebutadalah rumah atau bangunan bebas jentik nyamuk AEDES, rumah tangga ber-PHBSdan sumur terlindung. Dengan nilai Odds ratio untuk rumah atau bangunan bebas jentiknyamuk AEDES, rumah tangga ber-PHBS, dan sumur terlindung masing-masing sebesar0,968, 0,974, dan 0,980. Nilai hit ratio keakuratan model peluang logit sebesar 71,233%.Dengan demikian dapat disimpulkan bahwa model peluang logit yang terbentuk sudahlayak digunakan untuk mengetahui faktor-faktor yang mempengaruhi kejadian DBD.Kata Kunci: Model Regresi Logistik Biner, metode Maximum Likelihood, DemamBerdarah Dengue
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.026 | 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".