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All Journal KALBISIANA Jurnal Sains, Bisnis dan Teknologi INOVTEK Polbeng - Seri Informatika
Edwin Lesmana Tjiong
Universitas Kalbis
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Social Sciences

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An Indonesian Chatbot for Disease Diagnosis Using Retrieval-Augmented Generation Muhammad Adrinta Abdurrazzaq; Edwin Lesmana Tjiong; Aulia Fasya; Michelle Hiu; Joses Tanuwidjaya
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/9nnkn955

Abstract

The rapid advancement of Large Language Models (LLMs) has enabled their use in medical information systems, although challenges such as hallucinations, domain mismatches, and the lack of a verified knowledge base remain significant, particularly in low-source languages ​​like Indonesian. This study introduces an Indonesian-language medical chatbot based on the open-source GPT-OSS-20B model enhanced through a Retrieval-Augmented Generation (RAG) pipeline. The system combines semantic retrieval using jina-embeddings-v3, lexical re-ranking with the BM25 algorithm, and a lightweight Logistic Regression-based domain filter as an initial filter to prevent out-of-domain LLM usage. Evaluation using Indonesian medical articles and annotated patient-doctor conversations shows that the domain filter works well on synthetic data but results in misclassification of natural queries. A hybrid weighted reranker (FAISS L2 + BM25) performed the best with a Top-30 accuracy of 0.699. Black-box testing indicates that the system flow functions as designed, although the response quality has not been validated by clinical experts. These findings suggest that RAG-based open-source LLMs can improve access to Indonesian-language medical information, but still have important limitations such as the lack of clinical validation, potential errors in scraped data, and suboptimal robustness of domain filters.
Implementasi Zero Trust Architecture pada Cloud Environment Menggunakan Microsoft Azure Vincencius Galvin Hermanto; Edwin Lesmana Tjiong
KALBISIANA Jurnal Sains, Bisnis dan Teknologi Vol. 12 No. 1 (2026): Kalbisiana
Publisher : UNIVERSITAS KALBIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53008/xjewhn02

Abstract

Dalam era transformasi digital yang semakin pesat, keamanan siber menjadi prioritas utama bagi organisasi yang mengadopsi layanan cloud computing. Salah satu pendekatan keamanan yang semakin banyak diadopsi adalah Zero Trust Architecture (ZTA), yang mengusung prinsip "never trust, always verify". Penelitian ini bertujuan untuk mengimplementasikan arsitektur Zero Trust pada lingkungan cloud menggunakan Microsoft Azure guna meningkatkan posturkeamanan infrastruktur. Studi ini mencakup identifikasi komponen utama ZTA, seperti kontrol identitas, kebijakan akses berbasis risiko, dan pemantauan berkelanjutan, serta bagaimana layanan Azure—seperti Azure Monitoring, Azure Identity and Access Management (IAM), Microsoft Defender for Cloud, Azure Policy, Azure Web Application Firewall (WAF), dan Azure VPN—dapat digunakan untuk membangun arsitektur tersebut. Hasil implementasi menunjukkan bahwapenerapan ZTA dalam Azure mampu memberikan kontrol keamanan yang lebih granular dan adaptif terhadap ancaman siber modern. Penelitian ini diharapkan dapat menjadi referensi teknis bagi organisasi yang ingin memperkuat keamanan infrastruktur cloud mereka melalui pendekatan Zero Trust.
Co-Authors Aulia Fasya Joses Tanuwidjaya Michelle Hiu Muhammad Adrinta Abdurrazzaq Vincencius Galvin Hermanto