Penerapan Metode Convolutional Neural Network Untuk Mendeteksi Wajah Yang Menggunakan Masker
Bibliographic record
Abstract
Pada tahun 2019 sedang terjadi pandemi virus bernama Coronavirus Disease 2019 (covid-19) yang sedang melanda banyak negara. Covid-19 sendiri berasal dari virus Severe Acute Respiratory Snydrome Coronavirus 2 (SARS-Cov-2) yang dapat menginfeksi sistem pernapasan manusia. Cara efektif untuk mengatasi penyebaran wabah tersebut adalah dengan menggunakan masker dan menaati protokol Kesehatan.Dengan program ini diharapkan mampu mendeteksi wajah yang menggunakan masker atau tidak. Program ini akan mendeteksi wajah secara realtime. Program tersebut nantinya akan berjalan dengan webcam laptop dengan cara mendeteksi wajah apakah menggunakan masker atau tidak. Program ini nantinya akan menggunakan bahasa pemrograman python dengan framework tensorflow yang mengadopsi konsep convolutional neural network. Convolutional Neural Network termasuk salah satu deep learning. Deep Learning merupakan sub-set dari machine learning. Di dunia machine learning akan terdapat dataset untuk program tersebut dapat mempelajari pola gambar. Dataset tersebut nantinya akan diisi oleh gambar wajah yang menggunakan masker dan yang tidak menggunakan masker. Nantinya gambar-gambar tersebut akan dipisah dipisah menjadi beberapa folder.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".