Implementasi Artificial Intelligence untuk Analisis Attack Surface dan Rekomendasi Remediasi Kerentanan Keamanan Berbasis Vulnerability Assessment pada Website UMKM

Willy Wijayanto; Yuma Akbar; Mesra Betty Yel

doi:10.63447/jimik.v7i3.2005

Vol. 7 No. 3 (2026) • Articles

Open Access

Implementasi Artificial Intelligence untuk Analisis Attack Surface dan Rekomendasi Remediasi Kerentanan Keamanan Berbasis Vulnerability Assessment pada Website UMKM

Willy Wijayanto

Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Yuma Akbar

Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Mesra Betty Yel

Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

DOI: https://doi.org/10.63447/jimik.v7i3.2005

Published: September 10, 2026 Pages: 462-468

Original Full-Text Article

Download published version for reading and archiving

Download PDF

Abstract

The rapid digitalization of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia has raised the need for adequate website security, yet limited technical resources leave many MSME owners unaware of their platform's security posture. This research designs and implements VulnScope, a web-based vulnerability assessment (VA) system that integrates a Large Language Model (LLM) to analyze the attack surface and generate contextual remediation recommendations. The system is built on the FastAPI framework with eight core scanning modules, an aggressive scanner, and a VAPT Engine that classifies findings based on OWASP Top 10:2021 and CVSS v3.1. Artificial intelligence is integrated through the Claude and Nous Hermes models using an agentic tool-use pattern. Black box functional testing confirmed that all features work as specified. Applied to the MSME website ibadahterpanjang.com, the system detected four vulnerabilities (one Critical, one Medium, two Low) with an overall CRITICAL risk level (score 38/100). The results show that integrating VA with artificial intelligence effectively helps MSME owners independently identify and remediate website security vulnerabilities.

Keywords:

Vulnerability Assessment Large Language Model Attack Surface OWASP Top 10 CVSS MSME Website Security

Article Metrics & Downloads Graph

Monthly Download Trends:

Author Biographies

Willy Wijayanto Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

Yuma Akbar Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

Mesra Betty Yel Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

How to Cite

Wijayanto, W., Akbar, Y., & Betty Yel, M. (2026). Implementasi Artificial Intelligence untuk Analisis Attack Surface dan Rekomendasi Remediasi Kerentanan Keamanan Berbasis Vulnerability Assessment pada Website UMKM. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 7(3), 462-468. https://doi.org/10.63447/jimik.v7i3.2005

License

Creative Commons Attribution 4.0 International License (CC BY 4.0)

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License .

Share: You are free to copy, distribute, and transmit the work in any medium or format.
Adapt: You are free to remix, transform, and build upon the work for any purpose, even commercially.
Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

Copyright & Retention: Authors retain copyright without restrictions and grant this journal the right of first publication under an open-access model. The journal retains non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

References

Total: 14 References

Achmad, F. K., Mulyana, D. I., & Akbar, Y. (2022). Implementasi algoritma Dijkstra pada routing protokol OSPF menggunakan perangkat Juniper. Informatics for Educators and Professionals: Journal of Informatics, 7(1), 1–14.
Arif, S. C., Surapati, U., Akbar, Y., & Hidayat, A. Z. (2025). Optimasi Access Control List (ACL) jaringan dalam menangkal akses ilegal jaringan Cisco. Jurnal Indonesia: Manajemen Informatika dan Komunikasi (JIMIK), 6(3).
Armadani, A., Nofriansyah, N., & Ibnutama, I. (2022). Analisis keamanan untuk mengetahui vulnerability pada web menggunakan penetration testing. Jurnal SAINTIKOM.
Author, A., & Author, B. (2022). Kesadaran keamanan siber pada pelaku UMKM di Indonesia. Jurnal Sistem Informasi, 10(2).
Fahlevi, M. R., & Putri, D. R. D. (2021). Analisis monitoring dan kinerja keamanan jaringan menggunakan Nmap. IT (Informatic Technique) Journal, 9(1).

Fang, R., Bindu, R., Gupta, A., & Kang, D. (2024). LLM agents can autonomously exploit one-day vulnerabilities. arXiv preprint arXiv:2404.08144.
Kaspersky. (2022). Cyber threats to small and medium businesses. Kaspersky Security Bulletin.
Kementerian Koperasi dan UKM Republik Indonesia. (2023). Perkembangan UMKM dan transformasi digital di Indonesia. Jakarta.
Ollama. (2024). Run large language models locally. GitHub Repository.
Ramesh, S., & Author, T. (2025). Machine learning for cybersecurity applications. Computers & Security, 154.
Riadi, I., Prayudi, Y., & Author, A. (2020). Analisis keamanan website menggunakan metode vulnerability assessment. Jurnal Teknologi Informasi, 9(1).
Sai, S., Yashvardhan, U., Chamola, V., & Sikdar, B. (2024). Generative AI for cyber security. IEEE Access, 12.
Sasmito, G. W., & Nishom, M. (2020). Testing the population administration website application using black box testing boundary value analysis. Dalam Proceedings of the 2020 IEEE International Conference on Open Systems (ICOS).
Supangat, A., Amna, D., & Rochman, F. (2025). Penetration testing and vulnerability analysis to strengthen data protection. Journal of Information Technology and Cyber Security.