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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Sadly Syamsuddin
Dipa University Makassar
Computer Science & IT Electrical & Electronics Engineering

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Performance evaluation of a novel blockchain consensus mechanism (PoDIPA) for decentralized microgrid networks Sadly Syamsuddin; Salama Manjang; Muhammad Bachtiar Nappu; Ady Wahyudi Paundu
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9857

Abstract

The increasing demand for sustainable and decentralized energy systems has driven the adoption of blockchain technology in microgrid networks. However, conventional consensus mechanisms, such as proof of work (PoW) and proof of stake (PoS), suffer from high energy consumption, limited adaptability, and fairness issues, which hinder their suitability for dynamic microgrid environments. This paper proposes a novel consensus mechanism, proof of dynamic influence and participation activity (PoDIPA), which integrates prosumers’ real-time participation activity and historical influence into the validator selection process. The proposed mechanism is evaluated through deterministic simulations and compared with PoW and PoS in terms of energy efficiency, transaction processing time, and security resilience. Simulation results demonstrate that PoDIPA significantly reduces average energy consumption and adapts more rapidly to network dynamics while maintaining security performance comparable to existing consensus mechanisms under majority attack scenarios. Although PoDIPA exhibits higher short-term variability due to its adaptive nature, the overall efficiency–stability trade-off remains favorable. These results indicate that PoDIPA is a promising consensus solution for supporting fair, energy-efficient, and decentralized energy trading in future microgrid systems.
SS-ANFIS: a semi-supervised neuro-fuzzy model for offline signature verification Sadly Syamsuddin; Jufri Jufri; Suci Rahma Dani Rachman; Suryani Suryani; Wilem Musu; Salmiati Salmiati; Yesycha Arun Mangopo
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1985-1997

Abstract

Signature verification remains a critical authentication mechanism in academic and administrative environments, yet manual verification is vulnerable to forgery and subjective judgment. This study proposes SS- ANFIS, a semi-supervised neuro-fuzzy model for offline signature verification under limited labeled data conditions. The proposed model integrates pseudo-label-based self-training into a Takagi-Sugeno-Kang adaptive neuro-fuzzy inference system (ANFIS). Static image-based features were extracted from offline signature images and transformed using principal component analysis (PCA) before classification. Experiments were conducted on 800 offline signature samples collected from Dipa University Makassar, consisting of 400 genuine and 400 forged signatures. The proposed model achieved an accuracy of 90.5%, precision of 98.8%, recall of 82.0%, and F1-score of 90.0%. The high precision indicates that SS- ANFIS is effective in minimizing false positive predictions, which is important for academic document verification. The results show that the proposed model provides a practical, interpretable, and computationally efficient approach for offline signature verification, particularly in institutional settings with limited labeled data.
Co-Authors Ady Wahyudi Paundu Jufri . Muhammad Bachtiar Nappu Salama Manjang Salmiati Salmiati Suci Rahma Dani Rachman Suryani Suryani Wilem Musu Yesycha Arun Mangopo