Arya Adhyaksa Waskita
Universitas Pamulang

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Prediction of Five Elements Imbalance and Acupuncture Point Recommendations Using Health-LLM Agent Method for Symptom Diagnosis Based on Traditional Chinese Medicine (TCM) Theory at Acumastery Clinic Iwan Muttaqin; Arya Adhyaksa Waskita; Choirul Basir
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5775

Abstract

Traditional Chinese Medicine (TCM) is a medical system that has been historically proven effective in diagnosing and managing various symptoms through the concepts of the Five Element imbalance, Yin-Yang, and acupuncture points. In the era of artificial intelligence, the utilization of Large Language Models (LLMs) specifically designed for the healthcare domain, referred to as Health-LLM Agents (AI-based health agents powered by LLMs), holds great potential in supporting TCM practices with greater efficiency and precision. This study aims to design and evaluate the performance of a Health-LLM Agent in predicting imbalances among the Five Elements (Wood, Fire, Earth, Metal, Water) based on patient symptoms, while also recommending appropriate acupuncture points for therapy. The methodology involves fine-tuning an LLM model with prompt engineering tailored to TCM terminology and principles, along with integrating symptom data in semi-structured text format. Evaluation is conducted using expert validation and classification metrics such as diagnostic accuracy, relevance of acupuncture point recommendations, and result interpretability. The findings indicate that the Health-LLM Agent achieves an 81% accuracy in predicting Five Element imbalances and receives 92% positive validation from TCM practitioners regarding acupuncture point recommendations. These results demonstrate that the Health-LLM Agent can serve as a promising tool to support the digitalization and personalization of TCM diagnosis through AI-based systems
ANALYSIS OF THE EFFECTIVENESS VALUE OF IMPLEMENTING THE TWO-TIER DECEPTION-DRIVEN SECURITY MODEL IN CYBER SECURITY SYSTEMS Sahrul Ramadhan; Agung Budi Sutanto; Arya Adhyaksa Waskita
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 2 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/h9n89079

Abstract

The increasing complexity of cyber attacks, especially Brute Force and SQL Injection, poses a significant risk to production environments. Conventional reactive security measures are often unable to provide sufficient understanding regarding the behavior of attackers. This study designs and analyzes a "Two-Tier Deception Architecture" aimed at improving early warning capabilities without sacrificing the integrity of the production system. This architecture physically and logically separates the production environment as Tier 1 and the deception-based laboratory environment as Tier 2. By utilizing a combination of Fail2Ban and NFTables, the system stealthily redirects traffic from detected malicious actors to a separate environment hosting the Cowrie and DVWA honeypots. All security logs are collected and analyzed using a centralized ELK Stack SIEM. Evaluation using a curated dataset of 100 samples (consisting of 60 legitimate activities and 40 malicious activities) achieved a detection and redirection accuracy of 95%. The system demonstrates minimal resource usage on the production server while providing precise threat intelligence. This research shows that the inclusion of a deception tier within standard infrastructure substantially strengthens proactive defense and incident response effectiveness.
SIMULASI MITIGASI ZERO-TOUCH PADA SERANGAN BRUTE FORCE SSH DAN RDP BERBASIS ORKESTRASI SIEM WAZUH Putu Dedi Juliana; Arya Adhyaksa Waskita; Ferhat Aziz
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 2 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/w7g4ba46

Abstract

Administrasi server berbasis Secure Shell (SSH, port 22) dan Remote Desktop Protocol (RDP, port 3389) pada infrastruktur layanan publik, khususnya Layanan Pengadaan Secara Elektronik (LPSE) Kabupaten Mahakam Ulu, secara inheren memperluas permukaan serangan terhadap Teknik brute force (MITRE ATT&CK T1110). Model mitigasi manual yang bergantung pada analis memperpanjang jarak antara deteksi kegagalan logon berulang dan kontainmen, sehingga membuka jendela eksploitasi yang dapat dimanfaatkan penyerang. Penelitian ini merancang dan memvalidasi prototipe simulasi mitigasi otomatis berbasis semantic Active Response Wazuh dalam kerangka Security Information and Event Management (SIEM). Arsitektur klien–pelayan (React/Vite pada sisi klien; Node.js/Express dengan persistensi JSON pada sisi pelayan) menjalankan tiga skenario pengujian fungsional: serangan brute force bersumber tunggal pada SSH, bersumber tunggal pada RDP, dan multi-sumber dengan tiga alamat IP berotasi. Setiap skenario menggunakan ambang 10 kegagalan autentikasi untuk memicu aturan deteksi 5710 (SSH) dan 60122 (RDP) beranotasi T1110, dilanjutkan eksekusi Active Response berupa firewall-drop (Linux) dan netsh.exe (Windows). Hasil pengujian menunjukkan Mean Time to Respond (MTTR) diskret sebesar satu tick simulasi pada ketiga skenario, dengan rasio keberhasilan isolasi alamat IP mencapai 100% terhadap himpunan sumber yang dimodelkan. Prototipe yang tervalidasi berfungsi sebagai cetak biru konseptual bagi perencanaan penerapan SIEM pada infrastruktur publik tanpa risiko gangguan layanan produksi.