Risk Management Analysis of SMK Telkom Makassar's Integrated Academic Information System in Compliance with ISO 31000 Standards
Bibliographic record
Abstract
This investigation seeks to analyze the security risks associated with the Integrated Academic Information System (iGracias) application at SMK Telkom Makassar, using the ISO 31000 standards as a benchmark.The study employs the ISO 31000:2018 Information Technology Risk Management methodology, encompassing stages of risk identification, risk analysis, risk evaluation, and risk treatment.This methodology enables the researchers to ascertain that risks have been accurately identified, thoroughly analyzed, and appropriately mitigated, minimizing their potential impact on the organization.The findings reveal security issues in the iGracias application at SMK Telkom Makassar, identified through scanning with NMAP Kali Linux, which exposed several open ports, including port 21/tcp, port 22/tcp, and port 25/tcp.Consequently, these open ports present potential opportunities for unauthorized access and cyber-attacks.Moreover, the Mobile Security Framework (MobSF) test results yielded a Common Vulnerability Scoring System (CVSS) of 6.1, indicating a medium security level for the iGracias application in the Android environment.User responses revealed process risk at 84%, system security risk at 62%, and incidental risk at 57%.The outcomes of this investigation may serve as a guide in formulating and implementing strategies to uphold the security and quality of the applications in use.
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.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".