Implementasi MERN Stack pada Pengembangan Sistem Penerimaan Peserta Didik Baru
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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 it