Rancang Bangun Aplikasi Front End Penjualan Berbasis Web Menggunakan Metode Agile
Bibliographic record
Abstract
Abstrak.Perkembangan teknologi digital mendorong usaha kecil dan menengah untuk memanfaatkan sistem berbasis teknologi informasi demi meningkatkan efisiensi. Butik April Purwakarta masih mengandalkan penjualan manual melalui WhatsApp, yang mengakibatkan pencatatan transaksi tidak optimal serta akses informasi produk terbatas bagi pelanggan. Untuk menjawab kendala tersebut, penelitian ini difokuskan pada pengembangan aplikasi penjualan berbasis web. Aplikasi ini dirancang agar pelanggan dapat melihat katalog produk, melakukan proses checkout, sementara admin dapat mengelola data barang dan memvalidasi pembayaran.Metode pengembangan yang digunakan adalah Agile, karena sifatnya iteratif dan fleksibel sehingga mampu menyesuaikan kebutuhan pengguna. Sistem dibangun dengan bahasa pemrograman PHP dan basis data MySQL, serta dimodelkan menggunakan UML. Pengujian dilakukan dengan pendekatan Black Box Testing untuk memeriksa fungsi, serta System Usability Scale (SUS) guna menilai aspek kegunaan.Hasil pengujian SUS memperoleh skor 76,25 yang termasuk dalam kategori “Bagus” (nilai B). Temuan ini menunjukkan bahwa aplikasi dapat mempermudah akses informasi produk, meningkatkan efisiensi proses transaksi, serta mendukung digitalisasi sistem penjualan di Butik April Purwakarta.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".