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All Journal Telematika Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Putra, Guntur Dharma
Universitas Gadjah Mada
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Rancang Bangun Identity and Access Management IoT Berbasis KSI dan Permissioned Blockchain Guntur Dharma Putra; Sujoko Sumaryono; Widyawan Widyawan
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1118.359 KB)

Abstract

Blockchain offers several technological break-throughs, ranging from monetary solutions to healthcare systems. Some approaches have proposed blockchain implementation in IoT for providing better performance and scalability. However, massive scale implementation of IoT devices suffers from several issues in identity and access managements of interconnected devices. The present study proposes the combination of permissioned blockchain and Keyless Signature Infrastructure (KSI) as a means of governing the identity and access management of IoT devices. KSI is known for its ability to offer digital signature services without the need of public or private key but hash trees updated in a regular basis. With the decentralization fashion of blockchain, KSI can be implemented more efficiently. The results may give an identity and access management with high scalability.
Toward a Modular, Low-Latency Architecture with BERT-based Big Media Data Analysis Widyawan, Widyawan; Murti, Handoko Wisnu; Putra, Guntur Dharma; Nurmanto, Eddy; Affandi, Achmad
Telematika Vol 18, No 2: August (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i2.3151

Abstract

The significant growth of digital and social media platforms has introduced massive streams of unstructured media data. However, current big data approaches are not specifically tailored to the high volume and velocity of media data, which consists of unstructured and lengthy full-text messages. This study proposes a modular and stream-oriented big data architecture for media data. The proposed architecture consists of data crawlers, a message broker, machine learning modules, persistent storage, and analytical dashboards, with a publish-subscribe communication pattern to enable asynchronous, decoupled data processing. The system integrates IndoBERT, a transformer-based model fine-tuned for the Indonesian language, enabling real-time semantic tagging within the streaming pipeline. The proposed solution has been implemented as a prototype using open-source technologies in an on-premise cluster. As such, the primary novelty is the successful integration and operationalization of a large, transformer-based language model (IndoBERT) within a low-latency streaming pipeline. The experimental results underscore the feasibility of deploying scalable, vendor-neutral media analytics platforms for institutions with high sensitivity to privacy and cost. Architectural quality is quantitatively evaluated through Martin's Instability Metric and Coupling Between Objects (CBO), confirming high modularity across components. The system demonstrates an end-to-end latency of 3.121 seconds, deep learning latency of 2.333 seconds, and processes 32,102 messages per day, making an explicit trade-off where the 2.333-second deep learning inference provides advanced semantic depth. This study presents a reference architecture for scalable, intelligent real-time media analytics systems that support public sector and academic deployments, requiring data privacy and control over infrastructure.
Co-Authors Achmad Affandi Murti, Handoko Wisnu Nurmanto, Eddy Sujoko Sumaryono Widyawan