Guswanrinandi, Danang Haedar
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Design and Implementation of Blockchain in a Website-Based Electronic Medical Record System Using the Prototype Model Chotijah, Umi; Rosyid, Harunur; Sutaji, Deni; Guswanrinandi, Danang Haedar; Nabil , M. Rizaldi Zidan; Akbar , Muhammad Asad Muhibbin
Indonesian Journal of Information Systems Vol. 8 No. 2 (2026): February 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v8i2.13498

Abstract

The security and integrity of medical record data is a crucial issue in the era of healthcare service digitalization. Traditional systems still face risks of manipulation, information leaks, and issues with interoperability between healthcare institutions. Blockchain technology has emerged as a promising solution to address this issue thanks to its features of decentralization, openness, and difficulty in modification. One consensus method that can be applied is Proof of Work (PoW), which has proven to maintain the authenticity of transactions on a distributed network. This research aims to design and evaluate a blockchain-based medical record application using the PoW consensus algorithm to ensure the security, transparency, and reliability of medical data storage. The approach used is experimental, involving the development of a blockchain-based application prototype. The PoW algorithm is applied to ensure the validity of medical record data transactions. The evaluation was conducted by measuring the security aspect (resistance to data changes), performance (time to verify transactions), and scalability (number of transactions that can be handled). The results of the experiment show that implementing PoW in a medical record system can maintain data integrity with a high level of resistance to unauthorized changes. The average time for transaction verification is 2.4 seconds per block, with the ability to handle up to 150 transactions per minute. Although the performance of PoW requires significant computational resources, the level of security it offers suggests potential for implementation in larger healthcare systems. The application of blockchain with the PoW algorithm to medical records has proven to improve the security and transparency of medical information. This research successfully met the established objectives, although computational efficiency issues still need to be addressed. Further research is suggested to explore other consensus algorithms such as Proof of Stake (PoS) or Practical Byzantine Fault Tolerance (PBFT) to improve performance without sacrificing security aspects. Keywords: Blockchain, Electronic Health Records (EHR), Proof of Work (PoW), Smart Contract, Healthcare Information System
Identifikasi Citra Mengandung Api Menggunakan Algoritma Random forest dengan Segmentasi Warna dan GLCM Al-Ibham; Guswanrinandi, Danang Haedar
Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi) Vol. 3 No. 2 (2025): November 2025
Publisher : Fakultas Teknik Universitas Nahdlatul Ulama Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Fire is a disaster that can cause significant losses if not detected early. This study aims to develop a fire detection application based on digital image processing using a combination of color segmentation, texture feature extraction, and machine learning classification in MATLAB. Images in JPG and PNG format are processed by converting the RGB color space to HSV, followed by masking the fire-colored regions for segmentation. The segmented areas are then processed for texture feature extraction using the Gray Level Co-occurrence Matrix (GLCM) method, focusing on four main parameters: contrast, correlation, energy, and homogeneity. The extracted features are used as input for classification using the Random Forest algorithm with 100 decision trees. The system output is in the form of labeled images indicating either “FIRE DETECTED” or “NO FIRE.” The results show that the combination of HSV color segmentation, GLCM texture features, and Random Forest classification has strong potential as an effective approach for fire detection based on digital image.