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Heart Rate and Oxygen Saturation Internet of Things System (HROS-IoT) Uses Fuzzy Logic Hermansyah, Adi; Habibi, Ismail; Afifah, Nurul; Azhar, Iman Saladin B; Prasetyo, Aditya Putra Perdana; Maulida, Mutia Nadra
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4330

Abstract

Emergencies in hospitals, including in-hospital cardiac arrest (IHCA), necessitate effective response systems for monitoring vital signs such as heart rate and oxygen saturation. This study aims to develop and evaluate a Heart Rate and Oxygen Saturation Internet of Things (HROS-IoT) system utilizing the MAX30102 sensor and ESP32 microcontroller for real-time health monitoring. The system transmits data to the Blynk application via WiFi, enabling remote monitoring. Testing involved comparing the HROS-IoT system's performance against the commercial LK87 oximeter in measuring heart rate and oxygen saturation before and after meals with five participants. Results indicated that the HROS-IoT system produced heart rate measurements with an average error of 5.2 BPM before meals and 11.3 BPM after meals. Oxygen saturation readings showed an average error of 1% before meals and after meals. Despite minor discrepancies influenced by individual physiological differences and environmental conditions, the HROS-IoT system consistently delivered reliable data. The system's real-time monitoring capability and remote data access enhance proactive health management in hospitals. This study demonstrates the potential of the HROS-IoT system to improve patient outcomes and safety, suggesting further refinements for better accuracy and integration into healthcare settings.
Fake News Detection Using Optimized Convolutional Neural Network and Bidirectional Long Short-Term Memory Sari, Winda Kurnia; Azhar, Iman Saladin B.; Yamani, Zaqqi; Florensia, Yesinta
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

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

Abstract

The spread of fake news in the digital age threatens the integrity of online information, influences public opinion, and creates confusion. This study developed and tested a fake news detection model using an enhanced CNN-BiLSTM architecture with GloVe word embedding techniques. The WELFake dataset comprising 72,000 samples was used, with training and testing data ratios of 90:10, 80:20, and 70:30. Preprocessing involved GloVe 100-dimensional word embedding, tokenization, and stopword removal. The CNN-BiLSTM model was optimized with hyperparameter tuning, achieving an accuracy of 96%. A larger training data ratio demonstrated better performance. Results indicate the effectiveness of this model in distinguishing fake news from real news. This study shows that the CNN-BiLSTM architecture with GloVe embedding can achieve high accuracy in fake news detection, with recommendations for further research to explore preprocessing techniques and alternative model architectures for further improvement.
Security and Performance Evaluation of PPTP-Based VPN with AES Encryption in Enterprise Network Environments Heryanto, Ahmad; Setiawan, Deris; Audrey, Berby Febriana; Hermansyah, Adi; Afifah, Nurul; Azhar, Iman Saladin B.; Idris, Mohd Yazid Bin; Budiarto, Rahmat
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4818

Abstract

In the context of the current digital era, Virtual Private Networks (VPNs) serve a critical function in ensuring the confidentiality and integrity of data transmitted across public networks, particularly within corporate environments. This study presents a comprehensive analysis of VPN security and performance, with a specific focus on the Point-to-Point Tunneling Protocol (PPTP) and the implementation of encryption algorithms such as AES-128 and AES-256. Despite the widespread adoption of PPTP due to its simplicity and broad compatibility, it exhibits significant security vulnerabilities, primarily stemming from its reliance on the outdated RC4-based Microsoft Point-to-Point Encryption (MPPE) and the susceptible MS-CHAP authentication protocol, which is highly vulnerable to brute-force and dictionary attacks. Empirical findings indicate that, although AES-128 and AES-256 introduce minor performance trade-offs compared to unencrypted configurations, AES-256 demonstrates markedly enhanced security, achieving a 98.9% authentication success rate and a threat detection time of 122 milliseconds. Nevertheless, increased user load adversely impacts network performance, with throughput declining from 95 Mbps to 40 Mbps as the user count rises from 5 to 50, accompanied by elevated latency and packet loss. Comparative analysis across three encryption scenarios AES-128, AES-256, and MPPE-PPTP reveals a consistent degradation in network performance as user load increases, with AES-256 offering the strongest security at the cost of slightly reduced throughput and increased latency under high-load conditions. MPPE-PPTP, while providing better throughput, lacks adequate security, making it unsuitable for high-risk environments. Based on these observations, this study recommends the implementation of AES-256 encryption in enterprise networks requiring high security, supported by continuous performance monitoring and strategic capacity planning. Furthermore, the adoption of a secure site-to-site VPN architecture is proposed to facilitate reliable and secure communication between geographically distributed office locations.