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Ronsen Purba
Department of Informatics Engineering, Faculty of Informatics, Universitas Mikroskil, Medan, North Sumatera, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Implementation of Blockchain Technology in E-Voting Using Smart Contract and ZK-SNARK Jimmy; Kenny Rimba; Vincent; Ronsen Purba; Darwin
Teknika Vol. 14 No. 3 (2025): November 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i3.1358

Abstract

E-voting systems are prone to challenges such as lack of transparency, risks of data manipulation, and dependence on centralized authorities, which can undermine trust in electoral processes. This research develops a blockchain-based e-voting system on the Polygon network, leveraging smart contracts and Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARK) to enhance security, transparency, and voter anonymity. The study employs an application development approach, implementing a structured methodology with initialization, registration, voting, and tallying phases. Smart contracts automate voter verification, vote casting, and result tabulation, while ZK-SNARK ensures voters can cast ballots anonymously without revealing their identities. The system’s transparency and immutability are tested using PolygonScan, demonstrating effective prevention of manipulations like double voting through cryptographic credentials (nullifier, commitment, and nullifier hash) and Merkle Tree structures. Results indicate that the system provides a secure, verifiable, and decentralized framework for elections. This implementation offers a robust foundation for future e-voting systems, promoting trust and integrity in digital voting processes.
An Integrated Framework for Automated Resume Screening Using RoBERTa, Random Forest and Explainable AI Kevin Frederick Yapiter; Alfin; Yoga Hasim; Ronsen Purba; Mustika Ulina
Teknika Vol. 14 No. 3 (2025): November 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i3.1359

Abstract

The resume screening process is a critical stage in recruitment, yet conventional methods and traditional applicant tracking systems (ATS) often rely on manual review or keyword matching, resulting in slow, biased, and less objective evaluations. This study proposes an integrated automated screening system that combines RoBERTa for contextual feature extraction, Random Forest for candidate classification, and SHAP-based Explainable AI for interpretable decisions, enhancing transparency, efficiency, and fairness beyond traditional ATS. The dataset consists of real resumes and synthetically generated ones designed to mimic the distribution of real data, with K-means clustering used to establish labeling thresholds. Experimental results show that RoBERTa achieved an F1 Score of 81.08% in feature extraction, while Random Forest reached 96% accuracy in suitability classification. SHAP-based explanations provide insights into feature contributions for each prediction, offering an actionable understanding for recruiters. This integrated framework not only improves the efficiency and fairness of resume screening but also demonstrates a practical application of explainable AI in recruitment.
Co-Authors Alfin Darwin Jimmy Kenny Rimba Kevin Frederick Yapiter Mustika Ulina Vincent Yoga Hasim