Application of Design Thinking Method in Redesigning UI/UX Website of Agriculture and Plantation Service of West Nusa Tenggara Province
Bibliographic record
Abstract
Teknologi informasi memainkan peran penting dalam meningkatkan efisiensi dan kecepatan dalam pengambilan keputusan serta mendukung transformasi di berbagai sektor. Sesuai dengan Undang-Undang ITE No. 11 Tahun 2008 dan Inpres No. 3 Tahun 2003, setiap instansi publik, termasuk Dinas Pertanian dan Perkebunan Provinsi Nusa Tenggara Barat, diwajibkan untuk mengimplementasikan e-government guna meningkatkan efisiensi, transparansi, dan akuntabilitas. Meskipun Dinas Pertanian dan Perkebunan NTB sudah memiliki sistem informasi, hasil analisis menunjukkan bahwa desain Antarmuka Pengguna (UI) dan Pengalaman Pengguna (UX) masih memiliki banyak kekurangan, desain yang tidak mengikuti tren modern, struktur navigasi yang tidak intuitif, serta pemilihan warna yang kurang mencerminkan identitas sektor pertanian dengan jelas. Untuk itu, dilakukan perancangan ulang UI/UX menggunakan metode Design Thinking yang meliputi tahapan empathize, define, ideate, prototype, dan testing. Hasil dari perancangan ini akan diuji menggunakan System Usability Scale (SUS) untuk mengevaluasi kemudahan penggunaan, efisiensi, dan kepuasan pengguna. Hasil pengujian SUS menunjukkan skor sebesar 79,4 dengan kategori B (Good) menandakan bahwa desain website baru berhasil menciptakan tampilan dan pengalaman pengguna yang lebih baik dibandingkan dengan versi sebelumnya.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".