Implementasi MERN Stack pada Pengembangan Sistem Penerimaan Peserta Didik Baru
Bibliographic record
Abstract
Pengembangan aplikasi web membutuhkan arsitektur yang sederhana namun kuat dari sisi back-end sampai front-end. Berkaitan dengan hal tersebut framework MERN Stack menjadi populer digunakan. Teknologi ini merupakan kombinasi dari beberapa layer seperi MongoDB, ExpresJS, ReactJS dan NodeJS yang berfokus pada satu bahasa pemrograman yaitu JavaScript. Implementasi MERN Stack pada studi kasus ini adalah untuk pengembangan dan implementasi sitem Penerimaan Peserta Didik Baru (PPDB) berbasis web pada SMA Muhammadiyah 1 Program Khusus Kartasura. Evaluasi kualitas sistem dilakukan dengan tiga metode testing yaitu black-box testing, system usability scale (SUS), dan page speed test. Hasil pengujian black-box menunjukan sistem memiliki fungsionalitas yang sesuai dengan prosentase kesalahan 0%. Sedangkan pengujian SUS menghasilkan nilai rata-rata 78,98 yang berarti aplikasi berada pada level acceptable dan bisa digunakan untuk kasus riil. Pengujian performa kecepatan akses web menggunakan Google page speed test dan GTmetrix menunjukan performa yang baik dengan nilai mencapai 73 dan waktu load rata-rata 7 detik. Web application development requires a simple yet robust architecture. Thus, MERN Stack framework has gaining popularity. MERN Stack combines several layers like MongoDB, ExpressJS, ReactJS and NodeJS. The framework focuses on JavaScript programming language. The MERN Stack implementation in this case is for the development of a web-based Student Admissions (PPDB) system at SMA Muhammadiyah 1 Kartasura. System evaluation is carried out using three testing methods, namely black-box testing, system usability scale (SUS), and page speed test. The results of the black-box show that the system has perfect functionality with error percentage of 0%. Meanwhile, the SUS test shows an average value of 78.98 which means the application is acceptable and can be implemented. The performance of web access speed is evaluated using Google page speed test and GTmetrix. It shows good performance with a value reaching 73 and an average load time of 7 seconds.
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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 | 0.006 |
| 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 | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".