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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Journal of Embedded Systems, Security and Intelligent Systems
Wijang Widhiarso
Universitas Teknologi Digital Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Secure Automated Reconnaissance Using LLM Agents and a Layered Cryptographic Protection Pipeline Ikhwan Ruslianto; Wijang Widhiarso; Hafiz Muhardi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.2621

Abstract

Purpose – This study aims to design and evaluate a secure reconnaissance platform that integrates Large Language Model (LLM) agents for dynamic tool orchestration with a layered cryptographic protection pipeline to accelerate penetration-testing information gathering while protecting sensitive artefacts. Design/methods/approach – The platform unifies Nmap, WHOIS, and theHarvester under an LLM controller that generates command-line parameters through schema-constrained orchestration. Each output is validated against a strict JSON schema before execution. The protection pipeline applies AES-256-GCM with envelope keys for confidentiality, HMAC-SHA256 hash chaining for tamper-evident logs, Ed25519 signatures for report-level non-repudiation, and Argon2id-derived session keys. Evaluation was conducted on three public domains across thirty runs each, measuring latency, cryptographic overhead, verification integrity, signature validation, and an internal CVSS-informed triage score. Findings - The prototype showed that automated reconnaissance and cryptographic auditability can be combined with limited performance cost. A full pass over untan.ac.id completed in 14.97 seconds and produced an internal triage-heuristic score of 78/100. Cryptographic operations added 312 ms on average, equal to about 2.08% of total latency. All hash-chain links were verified, and Ed25519 signatures were validated in 71 µs. Research implications/limitations – The findings support red-team and blue-team workflows requiring faster, auditable reconnaissance reporting. However, the evidence is limited to three public domains under one network condition; therefore, the results should be interpreted as feasibility evidence, not generalisable performance claims. The risk score is an internal prioritisation heuristic, not a validated severity instrument. Originality/value – The study contributes a secure LLM-orchestrated reconnaissance framework that integrates structured command orchestration with cryptographic safeguards for confidentiality, integrity, and non-repudiation.
Implementasi Algoritma Arima Untuk Optimasi Sistem Prediksi Pembayaran Impor Multi-Negara Berbasis Time-Series Wijang Widhiarso; Alfiarin; Deni Apriadi; Dytha Ananda Widhiarso
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1128

Abstract

Dalam ekosistem Keuangan Komputasi, peramalan arus kas sekuensial yang akurat merupakan tantangan komputasi yang signifikan karena tingginya volatilitas dan derau (noise) yang melekat pada data ekonomi global. Makalah ini bertujuan untuk mengimplementasikan algoritma Autoregressive Integrated Moving Average (ARIMA) sebagai solusi komputasi yang tangguh untuk memprediksi beban pembayaran impor internasional. Masalah utama yang diangkat adalah terbatasnya kemampuan sistem pendukung keputusan konvensional dalam menangani data tidak stasioner yang berasal dari transaksi 11 negara mitra antara tahun 2010 dan 2023. Kontribusi makalah ini terletak pada perumusan parameter (p, d, q) yang optimal melalui pendekatan statistik komputasi, menghasilkan model yang dicirikan oleh efisiensi tinggi (kompleksitas rendah) namun tetap mempertahankan akurasi tinggi. Dengan menggunakan kumpulan data 'Import Payments - by Country (1).csv', hasil eksperimen menunjukkan bahwa model ARIMA (1,1,1) mencapai Mean Absolute Percentage Error (MAPE) sebesar 14,89% pada data pembayaran impor Tiongkok, yang memiliki volatilitas tertinggi. Bukti ini menegaskan bahwa algoritma ARIMA dapat berfungsi sebagai mesin inti yang andal untuk sistem peramalan keuangan otomatis, terutama di lingkungan dengan sumber daya komputasi yang terbatas.
Co-Authors Alfiarin Deni Apriadi Dytha Ananda Widhiarso Hafiz Muhardi Ikhwan Ruslianto