REDESAIN UI/UX FAMI APPS MENGGUNAKAN METODE GOAL DIRECTED DESIGN DAN COGNITIVE WALKTHROUGH
Bibliographic record
Abstract
Fami Apps adalah salah satu aplikasi untuk mendapatkan barang dan jasa berupa aplikasi pemesanan makanan dan minuman secara online yang dikembangkan oleh PT. Fajar Mitra Indah. Di google playstore aplikasi ini mendapat rating 3 dan sudah di download lebih dari 100.000 pengguna. Dari survey di ulasan google playstore ternyata yang membuat rating ini kurang memuaskan adalah dikarenakan tampilan informasi aplikasi Fami Apps dan beberapa fungsinya yang dirasa kurang memadai. Tujuan dari penelitian ini adalah untuk merombak User interface (UI) dan User Experience (UX) aplikasi Fami Apps dengan menggunakan proses goal-directed design dan cognitive walkthrough. Aplikasi didesain ulang dengan menggunakan teknik goal-directed design, yang melibatkan 6 fase: penelitian pendahuluan, pemodelan, persyaratan, kerangka kerja, penyempurnaan, dan dukungan. Hasil pengujian dikumpulkan setelah desain UI selesai dibuat dengan metode cognitive walkthrough untuk nilai atribut usability sebelum dirancang ulang desain nilai atribut tersebut menghasilkan skor 56 dengan adjective ratings “Good” dan acceptable ratings “Low”, setelah dilakukan perancangan ulang desain nilai atribut tersebut menghasilkan skor 79 dengan adjective ratings “Exelent” dan acceptable ratings “High”.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".