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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Simetris Kompak : Jurnal Ilmiah Komputerisasi Akuntansi E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Pixel : Jurnal Ilmiah Komputer Grafis Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Komunikasi REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat IAIC Transactions on Sustainable Digital Innovation (ITSDI) Walisongo Journal of Information Technology Advance Sustainable Science, Engineering and Technology (ASSET) Journal Peqguruang: Conference Series Jurnal Informatika Dan Tekonologi Komputer (JITEK) Jurnal Pengabdian Pada Masyarakat Indonesia (JPPMI) Jurnal Teknik Informatika dan Elektro (JURTIE) Journal of Management Nursing Jurnal Manajemen Informatika & Teknologi International Transactions on Artificial Intelligence (ITALIC) International Journal of Information Technology and Business Jurnal Pengabdian Kepada Masyarakat Indonesia Jurnal Informatika Dan Tekonologi Komputer Jurnal Manajemen, Bisnis dan Kewirausahaan Indonesian Journal of Infomatics Cyber Security and Network Management
Danang, Danang
Universitas Sains Dan Teknologi Komputer
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Privacy Protection and Trust in the Digital Era: A Systematic Review of Data Breach Impacts on SDG Progress Toni Wijanarko Adi Putra; Danang, Danang
International Journal of Information Technology and Business Vol. 8 No. 1 (2025): November : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.812025.24-34

Abstract

Objective – In the digital transformation era, the integrity of personal data has become essential for maintaining trust and ensuring the sustainability of digital services. This paper aims to systematically review how data privacy violations affect public trust and progress toward Sustainable Development Goals (SDGs), especially SDG 9 (infrastructure and innovation) and SDG 16 (strong institutions and justice). Methodology—This study adopts the Systematic Literature Review (SLR) approach based on Kitchenham’s framework. Relevant articles from 2021–2025 were retrieved from Scopus, IEEE, Springer, and ScienceDirect using a predefined search string aligned with PICOC. A total of 19,504 records were screened, and 36 high-quality studies were selected after applying inclusion/exclusion criteria and quality assessment tools (e.g., CASP, AMSTAR). Findings—The review reveals that sectors such as education, healthcare, and smart cities are increasingly adopting data protection technologies, including encryption, federated learning, differential privacy, and blockchain. However, many still face regulatory, infrastructural, and human literacy gaps. Breaches in personal data significantly reduce public trust, impair the exercise of digital rights, and pose ethical and operational risks for achieving SDGs. Limitations – The study is limited by the timeframe (2021–2025) and focuses primarily on peer-reviewed literature. Practical insights from developing countries may be underrepresented due to database indexing limitations. Contribution – This review contributes a cross-sectoral synthesis of technological and regulatory practices for data protection, identifies key challenges, and outlines a strategic roadmap for policymakers and technologists to integrate ethical data governance for sustainable digital futures.
Assessing Software Architecture Resilience Using Quantitative Metrics in Cloud Native Application Development Environments Eko Siswanto; Danang Danang; Ismi Kusumaningroem; Ilham Akhsani
Indonesian Journal of Infomatics Vol. 1 No. 1 (2026): February: Indonesian Journal of Infomatics
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/iji.v1i1.27

Abstract

Cloud native architectures are essential for modern software systems due to their ability to handle dynamic environments, scalability, and high availability. However, ensuring resilience in these systems remains a significant challenge, particularly under varying operational conditions such as high-load periods and failure scenarios. This study aims to assess the resilience of cloud native architectures using quantitative metrics that objectively evaluate key attributes such as availability, fault tolerance, recovery time, and scalability. Through the application of these metrics, the study identifies the strengths and weaknesses of the architecture, providing insights into how the system performs under stress and recovers from failures. The results show that while the architecture demonstrates strong availability and scalability under typical conditions, recovery time and scalability under extreme load conditions reveal areas for improvement. Specifically, issues with resource allocation and self-healing capabilities were identified as key weaknesses affecting the overall resilience of the system. These findings highlight the importance of using data-driven metrics to gain detailed insights into system resilience and to guide architectural improvements. The study also emphasizes the need for continuous monitoring and adaptation of the architecture to optimize fault tolerance and recovery processes. The implications of this research extend to cloud application developers and architects, offering actionable recommendations for improving system resilience. Future research could focus on integrating real-time monitoring systems, developing more advanced resilience metrics, and incorporating AI-driven scaling techniques to further enhance the adaptability and robustness of cloud native systems. By addressing these challenges, cloud native architectures can be better equipped to maintain high performance and reliability in dynamic, real-world environments.
Digital Forensics and Automated Incident Response Framework Leveraging Big Data Analytics and Real Time Network Traffic Profiling in Heterogeneous Cyber Environments Danang Danang; Zaenal Mustofa; Irlon Irlon
Cyber Security and Network Management Vol. 1 No. 1 (2026): February: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i1.15

Abstract

The increasing complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.
Co-Authors Ahmad Ashifuddin Aqham Aji Priyambodo Amad Maijun Amad Maijun Amalia Septi Reslili Anderson, Evelyn Andik Susdiyanto B Pujiati, Agnes Agnesita Basri Basri Brito Da Silva, Teodora Maria Fernandes budi hartono Budi Hartono Budi Hartono Budi Raharjo Dani Sasmoko Dani Sasmoko Duryono Duryono Eka Satria Wibawa Eko Siswanto Endang Swastuti Eni Dwifitri Astutiningtias Farhan Bin Mohamed Fatimah Indrawati Febryantahanuji Febryantahanuji Fujiama Diapoldo Silalahi Gufron Gufron Gunawan Wibisono Guruh Aryotejo Guruh Aryotejo, Guruh Hendri Rasminto Hudha Pratna Safiyan Ilham Akhsani Iman Saufik Suasana Indra Ava Dianta Inti Englishtina Irlon Irlon Ismi Kusumaningroem, Ismi Kasih Purwantini Katon Abdul Fatah Kumoro, Dwi Ferdiyatmoko Cahya Maya Utami Dewi Minarida Nova Yuspita Mufadhol Mufadhol Muh Tofik Muhammad Agus Kurniawan Muhammad Ryza Awwali , Sulartopo, Muhammad Ryza Awwali , Muhammad Sarjan Nandito Putra Prabawa Natalia, Elisa Ananda Ninda Lutfiani Nizar, Mochamad Fahrul Nur Cahyo Hendro Wibowo Nusril Nusril Padjar Setyo Budi Prihati Prihati Priyadi Priyadi Putranti, Honorata Rarnawati Dwi Ratnawati Dwi Putranti Honorata Ratri, Sanda Ramadhan Rawat, Bhupesh Rima Febriana Maryati Rizki, Soulenia Rahayu Ruli Supriati, Ruli S Siswanto Santoso, Nuke Puji Lestari Shobirin, Achmad Siti Nasekah Suprapti Suprapti Sutisna, Felix Suwardi Suwardi Syaidina, Musidiansyah Otto Tin Utami Toni Wijanarko Adi Putra Vivi Kumalasari, Vivi YAN ILMAS PUIMERA Zaenal Mustofa Zaenal Mustofa