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All Journal Indonesian Journal of Electrical Engineering and Computer Science Journal of Evrimata: Engineering and Physics
Cahya Rahmad
State Polytechnic of Malang
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT

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Ensuring Data Integrity in Healthcare Records through Hybrid Storage and Blockchain-Backed Audit Trails Azmiansyah Azmiansyah; Yan Watequlis Syaifudin; Cahya Rahmad; Josafat Pratama Susilo; Triana Fatmawati; Yuri Ariyanto; Pramana Yoga Saputra; Indrazno Siradjuddin; Chandrasena Setiadi
Journal of Evrímata: Engineering and Physics Vol. 04 No. 01, 2026
Publisher : PT. ELSHAD TECHNOLOGY INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70822/journalofevrmata.vi.140

Abstract

Ensuring the integrity and auditability of Electronic Health Records (EHRs) is critical for patient safety, regulatory compliance, and trust in digital healthcare. Conventional audit mechanisms such as database logs, are inherently mutable and vulnerable to insider tampering, failing to meet stringent requirements for tamper-proof, independently verifiable data provenance. To address this, this paper proposes and implements a hybrid EHR integrity system that combines PostgreSQL for sensitive clinical data storage with a private Hyperledger Fabric blockchain for immutable audit logging. Only lightweight metadata and SHA-256 hashes of records (not full EHRs) are written to the blockchain, preserving privacy while enabling cryptographic verification. Deployed on modest, heterogeneous hardware and featuring a user-friendly web interface with batched audit workflows, the system achieves 100% tamper detection accuracy, 210 ms average write latency, and 45 TPS throughput that demonstrates feasibility for real-world, resource-constrained clinical environments. Our approach delivers strong, scalable data integrity without prohibitive overhead, bridging the gap between regulatory demands and practical healthcare IT deployment.
Automated drone-assisted detection system for rice leaf pathologies a deep learning approach Erfan Rohadi; Cahya Rahmad; Septian Enggar Sukmana; Aida Sartimbul; Kismet Anak Hong Ping; Dimas Rosiawan; Ahmad Afifuddin Zakki
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp148-156

Abstract

Early and accurate detection of plant diseases is vital for maintaining agricultural productivity. This study investigates an automated disease identification system specifically designed for the IR64 rice cultivar. By combining drone-captured aerial imagery (UAV) taken during the plant's vegetative stage with public datasets, we established a comprehensive training dataset. The study evaluates and compares four convolutional neural network (CNN) architectures, InceptionV3, ResNet50, EfficientNetV2S, and MobileNetV2, assessing their predictive accuracy and real-world computational efficiency. Our 10-fold cross-validation results indicate varying levels of inference speed and accuracy among the models. InceptionV3 and MobileNetV2 displayed the highest stability and minimal misclassification rates across multiple disease types. In contrast, the performance of ResNet50 and EfficientNetV2S fluctuated significantly depending on the detected pathogen. In conclusion, coupling UAV imagery with fine-tuned deep learning models provides a fast, scalable solution for continuous crop monitoring and precision agriculture.
Co-Authors Ahmad Afifuddin Zakki Aida Sartimbul Azmiansyah Azmiansyah Chandrasena Setiadi Dimas Rosiawan Erfan Rohadi Indrazno Siradjuddin Josafat Pratama Susilo Kismet Anak Hong Ping Pramana Yoga Saputra Septian Enggar Sukmana Triana Fatmawati Yan Watequlis Syaifudin Yuri Ariyanto