Perancangan User Interface Aplikasi Pemasaran Hasil Pertanian di Kabupaten Humbang Hasundutan
Bibliographic record
Abstract
Pada daerah Humbang Hasundutan 70% mata pencaharian penduduk nya adalah bertani. Berbagai jenis hasil pertanian setiap tahunnya dihasilkan seperti padi, kopi, tomat, cabai, bawang, kacang-kacangan dan sayur mayur. Dengan situasi pandemic (Covid-19), terjadi penurunan penjualan hasil produksi para petani yang mengakibatkan ekonomi para petani menurun. Selain itu, sistem pemasaran yang digunakan masih manual. Oleh karena itu, penelitian dilakukan untuk mengetahui dampak Covid-19 terhadap pertanian di Humbang Hasundutan. Metode penelitian yang digunakan adalah melalui wawancara langsung kepada 25 petani, 5 konsumen dan 5 agen yang berada di Kecamatan Lintongnihuta dan Paranginan, Kabupaten Humbang Hasundutan. Perancangan user interface dengan mock up yaitu membuat desain yang menarik dan mudah dimengerti oleh pengguna. Desain ini bertujuan untuk mendapatkan suatu rancangan desain terbaik, yang didasari oleh penelitian yang dilakukan. Perancangan aplikasi user interface diterima baik oleh para petani di Humbang Hasundutan. Perancangan ini efesien untuk dilakukan, guna membantu memasarkan hasil pertanian para petani. Perancangan aplikasi user interface ini digunakam sebagai media komunikasi yang efektif dan efesien antara pengguna dengan system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.092 | 0.029 |
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".