Penerapan E-Government Dan Kinerja Aparatur Sipil Negara Pada Badan Penanggulangan Bencana Daerah Kota Makassar
Bibliographic record
Abstract
Penelitian bertujuan untuk menganalisis pengaruh penerapan E-Government, budaya kerja, motivasi, serta sarana dan prasarana terhadap kinerja Aparatur Sipil Negara (ASN) di Badan Penanggulangan Bencana Daerah (BPBD) Kota Makassar. Metode penelitian yang digunakan adalah kuantitatif dengan pendekatan survei melalui kuesioner yang disebarkan kepada 29 responden ASN di BPBD Kota Makassar. Data dianalisis menggunakan regresi linier berganda dengan bantuan program SPSS. Hasil penelitian menunjukkan bahwa penerapan E-Government berpengaruh positif dan signifikan terhadap kinerja ASN dengan nilai signifikansi 0,000. Selain itu, budaya kerja, motivasi, serta sarana dan prasarana juga berpengaruh signifikan terhadap kinerja dengan nilai signifikansi masing-masing sebesar 0,000; 0,000; dan 0,014. Uji F membuktikan bahwa keempat variabel tersebut berpengaruh secara simultan terhadap kinerja ASN. Temuan ini mengindikasikan bahwa optimalisasi E-Government, peningkatan budaya kerja, motivasi, dan penyediaan sarana-prasarana yang memadai dapat menjadi strategi efektif untuk meningkatkan kinerja ASN di BPBD Kota Makassar. Penelitian ini memberikan kontribusi praktis bagi pengambil kebijakan dalam merancang program peningkatan kinerja aparatur di sektor penanggulangan bencana. This study aims to analyze the influence of E-Government implementation, work culture, motivation, and facilities on the performance of Civil Servants (ASN) at the Regional Disaster Management Agency (BPBD) of Makassar City. A quantitative research method was employed, utilizing a survey approach with questionnaires distributed to 29 ASN respondents at BPBD Makassar. Data were analyzed using multiple linear regression with the assistance of SPSS. The results indicate that E-Government implementation has a positive and significant effect on performance, with a significance value of 0.000. Additionally, work culture, motivation, and facilities also significantly influence performance, with significance values of 0.000, 0.000, and 0.014, respectively. The F-test confirms that these variables collectively impact ASN performance. These findings suggest that optimizing E-Government, enhancing work culture, motivation, and providing adequate facilities can be effective strategies to improve ASN performance at BPBD Makassar. This study provides practical contributions for policymakers in designing programs to enhance civil servant performance in the disaster management sector.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 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".