Building an Android-Based Application for Schedule Reminders for Students' Assignments at SMKS Sri Langkat Tanjung Pura with Encryption and Decryption Processes Using the RSA Algorithm
Bibliographic record
Abstract
This research aims to solve the problems faced by students of SMKS Sri Langkat Tanjung Pura in organizing their schedules and assignments, while protecting their personal data. An Android-based schedule reminder application was created as a solution to better manage assignments. To ensure the confidentiality and security of sensitive data such as assignment files, this research uses the RSA (Rivest-Shamir-Adleman) cryptographic algorithm in the encryption and decryption process. This application is designed so that students can manage their assignments in an orderly and efficient manner, with the hope of improving the quality of education and reducing the level of negligence. The methodology applied includes system requirements analysis, system design using flowcharts and UML, and system testing to ensure the application functions properly. The implementation of this application is carried out using Android Studio with the Java programming language and the phpMyAdmin database. As a result, this application not only provides a practical solution for students in managing assignments, but also contributes to the field of computer science in the development of mobile applications and data security.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".