Kajian Tingkat Pencapaian Penerapan Smart Government Menurut OPD dan Masyarakat di Kabupaten Sumedang
Bibliographic record
Abstract
Abstract. The Smart Government concept in Sumedang Regency has derivatives, including government administration, bureaucracy, and public services. The implementation of Smart Government in Sumedang Regency still has several weaknesses including public awareness of existing services, besides the need for improvement in optimizing digital services. So this research is to get the value of the achievement level of Smart Government implementation in Sumedang Regency. For this research method, namely qualitative, with observation and questionnaires. It can be seen that Smart Government Sumedang Regency has a final score of 61% - 80%, which means that the score is included in the Integrated category. where according to OPD there is still a lack in terms of sensors in Sumedang Regency, besides that according to the community, technological support in Sumedang Regency in implementing Smart Government is still lacking. Therefore, it is necessary to make adjustments to the needs of the community in providing services in order to facilitate the community in Sumedang Regency, then there is more maintenance in infrastructure to support services and socialization and training for the community and ASN in Smart Government in order to maximize services. Abstrak. Konsep Smart Government di Kabupaten Sumedang memiliki turunan, diantaranya penyelengaraan pemerintahan, birokrasi, dan pelayanan publik. Penerapan Smart Government di Kabupaten Sumedang masih terdapat beberapa kelemahan diantaranya mengenai kesadaran masyarakat terhadap layanan - layanan yang ada, selain itu perlunya peningkatan dalam mengoptimalkan layanan digital. Maka penelitian ini untuk mendapatkan nilai tingkat pencapaian penerapan Smart Government di Kabupaten Sumedang menggunakan metode Garuda Smart City Framework. Untuk metode penelitian ini yaitu mix method, dengan observasi dan kuesioner. Dapat diketahui Smart Government Kabupaten Sumedang memiliki skor akhir 61% - 80% yang artinya skor tersebut masuk kedalam ketegori Integrated. dimana menurut OPD masih kekurangan dalam segi sensor yang terdapat di Kabupaten Sumedang, selain itu menurut masyarakat dukungan teknologi pada Kabupaten Sumedang dalam menerapkan Smart Government masih kurang. Maka dari itu perlu adanya penyesuaian mengenai kebutuhan masyarakat dalam penyediaan layanan agar dapat memudahkan masyarakat di Kabupaten Sumedang, lalu adanya pemeliharaan lebih dalam infrastruktur untuk penunjang pelayanan dan adanya sosialisasi dan pelatihan kepada masyarakat dan ASN dalam Smart Government agar dapat memaksilmalkan pelayanan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".