Evaluasi Implementasi Government Chief Information Officer (GCIO) dalam Transformasi Digital Pemerintah Kota: Studi Kasus di Kota Batam
Bibliographic record
Abstract
Penelitian ini menganalisis peningkatan peran Government Chief Information Officer (GCIO) pada Dinas Komunikasi dan Informatika Kota Batam dengan menggunakan pendekatan kualitatif deskriptif. Berdasarkan kerangka teori enam dimensi GCIO menurut Supangkat (2008), penelitian ini mengevaluasi implementasi peran GCIO melalui wawancara mendalam dengan informan kunci, observasi partisipatif, dan studi dokumentasi. Temuan penelitian mengungkap variasi pencapaian yang signifikan antar dimensi, dengan tingkat pencapaian tertinggi pada dimensi mengorganisasi (90%) dan terendah pada dimensi penyediaan (70%). Analisis mendalam mengidentifikasi tantangan kritis dalam koordinasi dengan Organisasi Perangkat Daerah (OPD), dimana 65% OPD merespons permintaan informasi dalam waktu lebih dari tiga hari. Penelitian ini juga mengungkap disparitas antara keberhasilan pembangunan infrastruktur digital dengan optimalisasi tata kelola kelembagaan. Secara teoritis, penelitian ini memperkaya literatur e-government dengan memberikan bukti empiris tentang implementasi GCIO di tingkat pemerintah kota. Secara praktis, temuan penelitian merekomendasikan penguatan model koordinasi GCIO-OPD, capacity building yang transformasional, dan pengembangan arsitektur governance yang adaptif untuk meningkatkan efektivitas transformasi digital pemerintah daerah. Kata Kunci: Government Chief Information Officer, Transformasi Digital, Tata Kelola Informasi, Pemerintah Daerah, E-Government
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".