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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

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

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

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.

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), Indonesia

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

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

Spatiotemporal Dynamics of El Niño Modoki Impacts on East Java Rainfall Using EOF, BIRCH Clustering, and Wavelet Coherence Diah Ariefianty; Agung Budi Susanto; Arya Adhyaksa Waskita
Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i3.3782

El Niño Modoki is a variation of El Niño characterized by sea-surface-temperature warming in the central Pacific, flanked by cooling in the eastern and western Pacific, and can influence rainfall patterns in Indonesia, particularly in East Java. This region was selected because it is one of Indonesia’s major food-producing areas, has high rainfall variability, and remains highly vulnerable to drought. This study aimed to analyze the impact of El Niño Modoki on monthly rainfall anomalies in East Java during 1991–2024. The data consisted of the El Niño Modoki Index (EMI) and monthly gridded rainfall data. The analytical methods included rainfall-anomaly calculation, Pearson correlation, Empirical Orthogonal Function (EOF), BIRCH clustering, Wavelet Coherence (WTC), and composite analysis. BIRCH clustering based on the first three EOF modes formed four rainfall-pattern clusters in East Java. WTC analysis showed that the relationship between EMI and rainfall was more dominant at interannual periods of approximately 1–4 years. Composite analysis indicated that El Niño Modoki reduced rainfall in East Java starting from the JJA period and became stronger and more spatially extensive during ASO. Overall, the impact of El Niño Modoki on East Java rainfall was spatial, seasonal, dynamic, and non-homogeneous across regions. These findings provide preliminary information for drought mitigation, water-resource management, and climate early-warning strengthening in East Java.

Analisis Sentimen Pengguna X Terhadap Pemilihan Gubenur Dki Jakarta Tahun 2024 Dengan Algoritma Naïve Bayes, K-Nearest Neighbor Dan Decision Tree Syarif Hidayatullah; Arya Adhyaksa Waskita; Achmad Hindasyah
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 1 (2026): IKRAITH-INFORMATIKA Vol 10 No 1 Maret 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i1.6283

The 2024 DKI Jakarta Gubernatorial Election is one of the main political agendas that attracts public attention in Indonesia. In this context, sentiment analysis of the Gubernatorial and Vice Gubernatorial candidates is important to understand public opinion. With the advancement of technology, especially the internet and social media platforms like X (formerly Twitter), the public can freely express their opinions regarding the candidates. This study aims to analyze public sentiment towards the three pairs of Gubernatorial and Vice Gubernatorial candidates of DKI Jakarta through tweets collected using the crawling method, resulting in 6,120 rows of data, with each candidate pair obtaining 2,040 rows of data. The accuracy of sentiment analysis was calculated using Naïve Bayes, K-Nearest Neighbor, and Decision Tree algorithms to compare the accuracy of these three algorithms with an 80:20 testing-training data split and feature extraction using TF-IDF and Transformer, implemented using the RapidMiner software. Based on the analysis results, Naïve Bayes with TF-IDF representation showed the highest accuracy for Paslon 1 at 86.76%, followed by Paslon 2 at 76.96%, and Paslon 3 at 72.79%. Meanwhile, K-NN with TF-IDF achieved the best results for Paslon 1 (67.40%) and Paslon 2 (71.32%), while Decision Tree achieved the highest accuracy for Paslon 1 at 72.55%. For the Transformer representation, the overall accuracy was lower compared to TF-IDF, with Paslon 1 achieving 56.86%, Paslon 2 at 53.43%, and Paslon 3 at 51.23%. These results indicate that TF-IDF is more effective for sentiment analysis of tweets related to the Gubernatorial candidates, with Naïve Bayes being the most accurate algorithm.

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