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All Journal Dinamik JURNAL PENGABDIAN KEPADA MASYARAKAT Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jiko (Jurnal Informatika dan komputer) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) METIK JURNAL Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Algoritma Data Sciences Indonesia (DSI) AT-TAKLIM: Jurnal Pendidikan Multidisiplin
Bayu Angga Wijaya
Universitas Prima Indonesia
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Physics Social Sciences Other

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COMPARISON OF ECDSA DAN EDDSA ALGORITHMS IN BLOCKCHAIN-BASED HEALTH RECORDS SECURITY Nicholas Alexander; Bayu Angga Wijaya; Rico Wijaya Dewantoro; Monalisa Monalisa
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 3 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10709

Abstract

The development of blockchain technology presents significant opportunities for the management of Electronic Health Records (EHR), owing to its decentralized, transparent, and tamper-resistant characteristics. However, security challenges remain, particularly regarding the use of the Elliptic Curve Digital Signature Algorithm (ECDSA), which, despite being compact and secure, has limitations in efficiency and potential vulnerabilities related to random nonce usage. This study aims to compare the effectiveness, efficiency, and security of ECDSA with the Edwards-curve Digital Signature Algorithm (EdDSA) in safeguarding the integrity and confidentiality of blockchain-based EHR systems. The research methodology involved simulations and evaluations of digital signature algorithms using an EHR dataset from Kaggle, focusing on performance testing, data validation, and the implementation of the Proof-of-Work (PoW) consensus mechanism. The results indicate that EdDSA outperforms ECDSA in terms of both speed and security. EdDSA achieved a signing time of 0.000180 seconds and a verification time of 0.000200 seconds, compared to ECDSA's 0.000962 seconds and 0.003204 seconds, respectively. While both algorithms successfully validated the data, neither was able to detect data alterations. From a blockchain perspective, PoW demonstrated high computational resistance, as evidenced by increased mining times—from 1,504 seconds for 4,000 blocks (difficulty target = 5) to 7,702 seconds for 20,000 blocks (difficulty target = 5)—thereby enhancing system integrity. Overall, EdDSA is considered more suitable for modern blockchain-based EHR implementations, although further research is needed to develop mechanisms for detecting data alteration.
Perbandingan Algoritma Decision Tree dan K-Nearest Neighbor untuk Klasifikasi Penyakit ISPA Marchell William Putra Pakpahan; Bayu Angga Wijaya; Marsaulina Lumbantoruan; Ambarsius Samosir; Mikhael Rafael
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 2 (2026): September (In Progress)
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i2.215

Abstract

Acute Respiratory Infection (ARI) is one of the most common respiratory diseases with diverse and overlapping clinical symptoms, making initial identification challenging and necessitating a systematic, data-driven classification approach. This study aims to compare the performance of the Decision Tree and K-Nearest Neighbor (KNN) algorithms in classifying ARI-related disease categories. The novelty of this research lies in the specific construction of ARI labels into five distinct categories from the Pediatric Respiratory Infections dataset, coupled with a rigorous feature selection process to handle mixed data types and address class imbalance using weighted evaluation metrics. The dataset consisted of 801 patient records with 91 initial attributes. The classification label was constructed from the Main diagnostic column and grouped into five categories: Asthma/Bronchospasm/Wheezing, Pneumonia/Pneumopathy, Bronchiolitis, Laryngeal/Upper Respiratory, and Other. After feature selection to remove noise and redundancy, 54 features were used, consisting of 24 numerical and 30 categorical features. The research stages included preprocessing, label construction, missing value handling, categorical encoding, KNN normalization, 80:20 train-test splitting, and model evaluation. The results show that Decision Tree achieved higher performance with 67.08% accuracy and 69.12% weighted F1-score, while KNN achieved 65.84% accuracy and 64.18% weighted F1-score. Thus, Decision Tree demonstrates superior performance and interpretability for this specific dataset.
Desain dan Implementasi Smart Contract untuk Pengelolaan Persetujuan Akses Data Pasien Berbasis Blockchain Anggie Ciecilia Saragih; Bayu Angga Wijaya; Jon Kevin Sihombing; Febryco Rives; Soeli Yanto Rotua Marbun
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3421

Abstract

This study aims to design and implement blockchain-based smart contracts for secure, transparent, and patient-oriented patient data access consent management. The research method employed a systems approach combining qualitative and quantitative methods through waterfall development stages. The system was developed using the Ethereum Sepolia Testnet blockchain and Solidity-based smart contracts. The implementation results demonstrate that blockchain technology is capable of permanently and transparently recording all patient data consent transactions. The smart contract successfully implemented a Role-Based Access Control (RBAC) mechanism, allowing patients to grant and revoke access permissions for doctors or healthcare institutions. The testing results indicate that the access validation mechanism functioned properly, although there are limitations related to scalability and gas costs on public blockchains. Security evaluation was limited to functional testing and access validation, indicating the need for further testing such as penetration testing and smart contract vulnerability analysis. Overall, this study proves that blockchain technology and smart contracts are capable of improving security and trust in digital healthcare data management, while also supporting the future development of artificial intelligence-based Decision Support Systems.
Co-Authors Adrian Julio Manalu Alvianus Sembiring Amar Nugraha Ambarsius Samosir Anggie Ciecilia Saragih Bayu Andreas Tarigan Berlian Fransisco Jhon Wau Brutu, Mahendra Christian Mitro Septiano Hutagalung Desniari Gea Dharsinni, N P Febryco Rives Gea, Areta Delano Halawa, Nestina Hanapi Hasan Harahap, M Hulu, Mesrawati Insidini Fawwaz Jimy Okinawa Jon Kevin Sihombing Jovan Kinoto Juandry Juandry Juliansyah Putra Tanjung Juliansyah Putra Tanjung K. Dilpani Laksmi Khairia, Anisa Lase, Yelita Kristiani Lovely Septian Silitonga Marchell William Putra Pakpahan Marsaulina Lumbantoruan Mikhael Rafael Monalisa Monalisa Monalisa Muhammad Ridho Muhammad Syahputra Novelan N P Dharshinni Nainggolan, Johannes Kristian Nazara, Leni Wati Nicholas Alexander Nicholas Alexander Perisman Jaya Gea Purba, Risky Pradipa Putri, Tansa Trisna Astono Resel, Resel Rico Wijaya Dewantoro Siboro, Sarwando Sihotang, Dohardo Dulisep Sinaga, Ferdinand Siregar, Afrahul Hidayah Siregar, Saut Dohot Siti Aisyah Sitorus, Andriani M. Soeli Yanto Rotua Marbun Tarigan, Angki Angkota Utami, Sekar Suryati Vijay Kumar Yennimar, Yennimar