Penerimaan Pemerintah Desa Terhadap Penerapan Aplikasi Penanganan Bencana Berbasis Android (SIDINA) di Kabupaten Pati
Bibliographic record
Abstract
ENGLISHPati regency frequently experiences natural disasters resulting in economic losses and environmental destruction. The recent advancements in information and communication technology, in particular smartphones, can be utilized effectively reducing disaster risk. SIDINA is an android-based smartphone application to mitigate both natural and man-made disasters. The aim of this study is to investigate public acceptance toward the use of an android-based application for disaster management through a technology acceptance model. It is essential to understand public acceptance to ensure that the app is truly useful and can be implemented successfully in disasters since using apps in disaster is still a novel idea. A cross-sectional survey is conducted by distributing questionnaires to village apparatus in 30 villages in Pati regency that are highly vulnerable to flooding. The result shows that the level of public acceptance is fair, as proven by the average score of the respondents’ response of 63.02%. The study finds that the respondents find it difficult to operate SIDINA app and it reduces their intention to use the app. It is important for app developers to improve immediately the app and make SIDINA as a user-friendly app. INDONESIABencana alam sering terjadi di Kabupaten Pati menyebabkan kerugian ekonomi dan kerusakan lingkungan. Perkembangan teknologi komunikasi dan informasi khususnya telepon pintar (smartphone) dapat dimanfaatkan secara efektif untuk mengurangi rrisiko bencana. SIDINA merupakan aplikasi kebencanaan yang dijalankan pada smartphone android yang dapat digunakan untuk keperluan mitigasi bencana baik bencana alam maupun bencana akibat ulah manusia. Tujuan studi ini adalah untuk mengetahui penerimaan masyarakat terhadap penggunaan aplikasi bencana SIDINA dengan menggunakan technology acceptance model (TAM). Sangat penting untuk mengetahui penerimaan publik untuk memastikan bahwa aplikasi tersebut benar-benar berguna dan dapat diimplementasikan dengan sukses dalam bencana karena menggunakan aplikasi dalam bencana masih merupakan ide baru. Cross-sectional survei dilakukan dengan menyebarkan kuesioner kepada perangkat desa di 30 desa rawan banjir di Kabupaten Pati. Hasil penelitian menunjukkan bahwa tingkat penerimaan masyarakat pada kategori cukup, terbukti dengan rata-rata skor tanggapan responden sebesar 63,02%. Hasil penelitian menemukan bahwa responden merasa kesulitan dalam mengoperasikan aplikasi SIDINA dan hal tersebut mengurangi niat mereka untuk menggunakan aplikasi SIDINA. Penting bagi pengembang aplikasi untuk segera memperbaiki aplikasi dan menjadikan SIDINA sebagai aplikasi yang mudah digunakan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".