Pemetaan dan Analisis Faktor yang Mempengaruhi Persentase Usaha E-Commerce di Indonesia
Bibliographic record
Abstract
Perkembangan teknologi dan informasi membawa dampak pada pertumbuhan E-Commerce di Indonesia. Sebagai pasar E-Commerce besar di ASEAN, usaha E-Commerce di Indonesia masih terpusat di Pulau Jawa dan Sumatera. Hal ini mengindikasikan masih belum meratanya usaha E-Commerce di Indonesia. Pada penelitian ini dibahas terkait ada tidaknya pengaruh faktor spasial atau kewilayahan pada persentase usaha E-Commerce di Indonesia. Metode yang digunakan adalah Geographical Weighted Regression (GWR). Hasil analisis mengklasifikasikan 34 provinsi di Indonesia menjadi lima kelompok berdasarkan model signifikan yaitu (1) Enam provinsi di Indonesia signifikan terhadap pertumbuhan ekonomi, (2) Sembilan provinsi signifikan terhadap keahlian di bidang TIK, (3) Dua provinsi signifikan terhadap keahlian di bidang TIK dan ketersediaan BTS, (4) Tiga provinsi signifikan terhadap keahlian di bidang TIK dan pertumbuhan ekonomi, (5) Empat belas provinsi di Indonesia tidak signifikan terhadap variabel prediktor yang digunakan pada penelitian ini.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".