PKM PENGENALAN ANDROID DAN PEMBUATAN VIDEO STREAMING DENGAN ANDROID STUDIO PADA SMK TELEKOMUNIKASI TELESANDI BEKASI
Bibliographic record
Abstract
Pada era Covid-19 saat ini semakin banyak pengguna atau user yang menggunakan teknologi. Dari orang tua hingga anak kecil menggunakan teknologi sebagai alat media pembelajaran online dimasa pandemi Covid-19. Android saat ini menjadi pilihan bagi perusahaan teknologi yang menginginkan sistem operasi berbiaya rendah, bisa dikustomisasi, dan ringan untuk perangkat berteknologi tinggi tanpa harus mengembangkannya dari awal. Faktor di atas telah memberikan kontribusi terhadap perkembangan Android, menjadikannya sebagai sistem operasi telepon pintar yang paling banyak digunakan di dunia. Metode dalam PkM ini dengan sasaran utama adalah siswa-siswi jurusan Rekayasa Perangkat Lunak kelas XII pada SMK Telekomunikasi Telesandi Bekasi. PkM dilakukan dengan memberikan pelatihan dan demonstrasi kepada peserta dalam pembuatan video streaming dengan Android Studio dengan dukungan media online berupa zoom, YouTube dan Link Google Drive. Adapun hasil umpan balik yang diberikan oleh peserta yang disampaikan oleh narasumber dengan tingkat kepuasan secara keseluruhan 55.3% memberi respon puas terhadap kegiatan pelatihan.Kata kunci—android studio, pengabdian masyarakat, video streaming.AbstractIn the Covid-19 era, more and more users are using technology. From parents to young children using technology as an online learning media tool during the Covid-19 pandemic. Android is currently the choice for technology companies who want a low-cost, customizable, lightweight operating system for high-tech devices without having to develop it from scratch. The above factors have contributed to the development of Android, making it the most widely used smartphone operating system in the world. The method in PkM with the main target is students majoring in Software Engineering class XII at SMK Telekomunikasi Telesandi Bekasi. PkM is carried out by providing training and demonstrations to participants in making video streaming with Android Studio with online media support in the form of zoom, YouTube and Google Drive links. The results of the feedback given by participants were delivered by sourceswith an overall satisfaction level of 55.3% giving a satisfied response to the training activities. Keywords—android studio, community service, video streaming.
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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 | 0.003 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 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".