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All Journal Journal of Information Systems Engineering and Business Intelligence International Journal of Artificial Intelligence Research Journal of Electronics, Electromedical Engineering, and Medical Informatics
Ahmad Hoirul Basori
Faculty of Computing and Information Technology Rabigh, King Abdulaziz University, Kingdom of Saudi Arabia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Social Awareness and Safety Assistance of COVID-19 based on DLN face mask detection and AR Distancing Tenriawaru, Andi; Basori, Ahmad Hoirul; Mansur, Andi Besse Firdausiah; Al-Qurashi, Qusai; Al-Muhaimeed, Abdullah; Al-Hazmi, Majid
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v5i2.230

Abstract

The outbreak of coronavirus disease (COVID-19) has forced major countries to apply strict policy toward society. People must wear a facemask and always keep their distance from each other's to avoid virus contamination. Government employ officers to monitor citizen and warn them if not wearing a face mask. The warning message also spread through SMS and social media to ensure people about safety and awareness. This paper aims to provide face mask detection using the Deep Learning Network(DLN) and warning system through video stream input from CCTV or images then analyzed. If people not wearing a mask are detected, they will alert them through the speaker and remind them about a penalty. AR distancing very useful to give position toward violator location based on the detected person in a certain area. The system is designed to work intelligently and automatically without human intervention. With the accuracy of 99% recognition, it's expected that the system can help the government to increase people awareness toward the safety of themselves and people around them.
Analyzing Variances in User Story Characteristics: A Comparative Study of Stakeholders with Diverse Domain and Technical Knowledge in Software Requirements Elicitation Trisnawati, Ersalina; Raharjana, Indra Kharisma; Taufik, Taufik; Basori, Ahmad Hoirul; Alghanmi, Nouf Atiahallah; Mansur, Andi Besse Firdausiah
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.110-125

Abstract

Background: In Agile software development, an essential initial stage is eliciting software requirements. This process engages stakeholders to achieve comprehensive results. However, a common issue is the variance in domain and technical knowledge among stakeholders, potentially impacting the quality of software requirements elicitation. Objective: Understanding the characteristics of user stories produced by stakeholders becomes crucial, particularly considering the differences in domain and technical knowledge. This study aims to compare the characteristics of user stories generated by stakeholders with varying backgrounds in domain and technical expertise. Methods: The initial step involves categorizing respondents into distinct stakeholder groups. Three stakeholders are involved in this study, constituting a combination of those with high and low technical and domain knowledge. Subsequently, data collection of user stories is conducted across various case studies. Finally, the acquired user stories are analyzed for further insights. Results: The analysis reveals variations in user stories generated by the three stakeholder categories across the three case studies. Stakeholders with domain knowledge tend to focus on 'what' aspects with task elements and 'why' aspects with hard-goal elements. Meanwhile, technical knowledge crafts user stories with capability elements in the 'what' aspect. Utilizing the QUS framework, it is evident that technical knowledge consistently produces a higher number of high-quality user stories across all quality categories, Conclusion: The contribution offered by this study lies in determining the distinct characteristics of user stories produced by different types of stakeholders, focusing on disparities in domain and technical knowledge. The study highlights the comparison of various characteristics of user story elements, such as hard-goals, soft-goals, tasks, or capabilities, and assesses the quality of user stories based on the user story framework. Additionally, it endorse the importance of process innovation in shaping the requirements gathering process and subsequently influencing the quality of user stories.   Keywords: User story, Agile Software Development, Requirements Elicitation, Stakeholder, Domain Knowledge, Process Innovation
EPR-Stego: Quality-Preserving Steganographic Framework for Securing Electronic Patient Records Safitri, Wardatul Amalia; Arsyad, Hammuda; Croix, Ntivuguruzwa Jean De La; Ahmad, Tohari; Batamuliza, Jennifer; Basori, Ahmad Hoirul
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1172

Abstract

Secure medical data transmission is a fundamental requirement in telemedicine, where information is often exchanged over public networks. Protecting patient confidentiality and ensuring data integrity are crucial, particularly when sensitive medical records are involved. Steganography, an information hiding technique, offers a promising solution by embedding confidential data within medical images. This approach not only safeguards privacy but also supports authentication processes, ensuring that patient information remains secure during transmission. This study introduces EPR-Stego, a novel steganographic framework designed specifically for embedding electronic patient record (EPR) data in medical images. The key innovation of EPR-Stego lies in its mathematical strategy to minimize pixel intensity differences between neighboring pixels. By reducing usable pixel variations, the framework generates a stego image that is visually indistinguishable from the original, thereby enhancing imperceptibility while preserving diagnostic quality. Additionally, the method produces a key table, required by the recipient to accurately extract the embedded data, which further strengthens security against unauthorized access. The design of EPR-Stego aims to prevent attackers from easily detecting the presence of hidden medical information, mitigating the risk of targeted breaches. Experimental evaluations demonstrate its effectiveness, with the proposed approach achieving Peak Signal to Noise Ratio (PSNR) values between 51.71 dB and 75.59 dB, and Structural Similarity Index Measure (SSIM) scores reaching up to 0.99. These metrics confirm that the stego images maintain high visual fidelity and diagnostic reliability. Overall, EPR-Stego outperforms several existing techniques, offering a robust and secure solution for medical data transmission. By combining imperceptibility, security, and quality preservation, the framework addresses the pressing need for reliable protection of patient information in telemedicine environments
Co-Authors Al-Hazmi, Majid Al-Muhaimeed, Abdullah Al-Qurashi, Qusai Alghanmi, Nouf Atiahallah Andi Tenriawaru Arsyad, Hammuda Batamuliza, Jennifer Croix, Ntivuguruzwa Jean De La Indra Kharisma Raharjana Mansur, Andi Besse Firdausiah Safitri, Wardatul Amalia Taufik Taufik Tohari Ahmad Trisnawati, Ersalina