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All Journal Jurnal Mantik Tematik : Jurnal Teknologi Informasi Komunikasi Al-Hijr: Journal of Adulearn World Journal International Inspire Education Technology (JIIET) Computing Jurnal Informatika Journal Research of Social Science, Economics, and Management Kinanti : Jurnal Karya Insan Pendidikan Terpilih Journal of Humanities Research Sustainability Maneggio Technologia Journal Journal of Pedagogi INTERNATIONAL JOURNAL OF HUMANITIES, SOCIAL SCIENCES AND BUSINESS (INJOSS) Indonesian Journal of Education (INJOE) Scientechno: Journal of Science and Technology Journal of World Future Medicine, Health and Nursing Journal Emerging Technologies in Education International Journal of Educational Narratives International Journal of Language and Ubiquitous Learning
Rustiyana, Rustiyana
Universitas Bale Bandung, Indonesia
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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A SYSTEMIC AI AND CYBER-PHYSICAL FRAMEWORK FOR REAL TIME REMOTE PATIENT MONITORING IN INDONESIAN RURAL HEALTH CLINICS (PUSKESMAS) Ethan Tan; Ava Lee; Li Wei; Rustiyana Rustiyana
Scientechno: Journal of Science and Technology Vol. 4 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i2.2894

Abstract

Access to healthcare in rural Indonesia remains a significant challenge due to limited medical resources and healthcare personnel, leading to delayed diagnosis and suboptimal patient care. Remote patient monitoring offers a potential solution by enabling real-time health assessments and reducing the need for long-distance travel to healthcare facilities. This study aims to design and implement a systemic Artificial Intelligence and Cyber-Physical Systems framework for real-time remote patient monitoring in rural primary health clinics in Indonesia to enhance patient care, support early disease detection, and optimize healthcare resource allocation. The research employed a hybrid AI–CPS approach that integrated wearable health devices, Internet of Things sensors, and cloud computing infrastructure to continuously monitor patient vital signs. Artificial Intelligence algorithms were utilized to analyze health data and identify early signs of potential health anomalies. Data were collected from multiple rural Puskesmas where remote monitoring devices were installed, and system performance was evaluated using metrics including data accuracy, response time, and user satisfaction. The results indicated that the system achieved a high level of accuracy, with a 92 percent success rate in predicting potential health anomalies, while feedback from healthcare workers and patients demonstrated positive perceptions, particularly in terms of convenience, efficiency, and time savings. Overall, the findings confirm that the AI and Cyber-Physical Systems-based remote patient monitoring framework is effective in improving healthcare delivery in rural Indonesian clinics and holds strong potential as a scalable solution to enhance accessibility and quality of rural healthcare services.
AI-Powered Decision Support Systems in Clinical Nursing Practice: Benefits and Ethical Considerations Ali Khan; Ahmed Shah; Fatima Malik; Rustiyana Rustiyana
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i1.2804

Abstract

Rapid advancements in artificial intelligence (AI) have introduced new possibilities for enhancing clinical nursing practice through AI-powered decision support systems (DSS). Increasing patient complexity, rising workloads, and the demand for timely, evidence-based care underscore the need for technological tools that can assist nurses in clinical judgment, risk assessment, and care coordination. This study aims to examine the benefits and ethical considerations associated with the integration of AI-driven DSS in nursing practice, focusing on their impact on clinical efficiency, patient safety, and professional autonomy. A mixed-methods approach was employed, combining a systematic literature review with qualitative thematic analysis of documented nursing experiences across diverse healthcare settings. The findings reveal that AI-powered DSS improve accuracy in clinical decision-making, support early detection of patient deterioration, and reduce cognitive workload for nurses. The results also identify significant ethical challenges, including data privacy concerns, algorithmic bias, reduced human oversight, and potential shifts in nurse patient relational dynamics. The study concludes that while AI-driven DSS offer substantial benefits for clinical nursing practice, their implementation must be guided by robust ethical frameworks, transparent governance, and continuous professional training to ensure responsible and equitable integration.  
Big Data and Epidemiology: Predictive Models for Future Infectious Disease Outbreaks Munkhzul Ganbat; Baatar Tserendorj; Selenge Batbold; Rustiyana Rustiyana
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i2.2805

Abstract

Intensifying global mobility, climate variability, and urban density have increased the frequency and complexity of infectious disease outbreaks, prompting the need for more accurate and timely epidemiological surveillance. Big Data analytics has emerged as a transformative approach capable of integrating heterogeneous datasets to detect patterns that traditional surveillance systems often miss. This study aims to examine the effectiveness of predictive modeling techniques leveraging Big Data sources such as social media activity, electronic health records, mobility data, and environmental indicators in forecasting potential infectious disease outbreaks. A mixed-methods analytical design was employed, combining machine learning based predictive modeling with retrospective epidemiological validation using multi-country datasets covering the past ten years. The results show that ensemble learning models, especially random forest and gradient boosting algorithms, significantly outperform conventional statistical models in predicting outbreak onset and trajectory, achieving higher accuracy, sensitivity, and early-warning lead time. The findings demonstrate that Big Data driven predictive models can enhance public health preparedness by providing earlier and more reliable outbreak alerts. The study concludes that integrating Big Data analytics into national and global epidemiological systems is essential for strengthening proactive disease prevention, although ethical governance and data privacy protections must be prioritized.
Mobile Health (mHealth) Applications: Transforming Preventive Health and Patient Engagement Aung Myint; Nandar Hlaing; Zaw Min Oo; Rustiyana Rustiyana
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i3.2930

Abstract

The rapid expansion of mobile health (mHealth) applications has reshaped contemporary healthcare by enabling continuous preventive health management and enhancing patient engagement beyond traditional clinical settings. This study aims to examine how mHealth applications contribute to preventive health behaviors and patient engagement, with particular attention to usage patterns, behavioral outcomes, and contextual influences. A mixed-methods research design was employed, combining quantitative analysis of user surveys and application usage data with qualitative insights from interviews and a community-based case study. The findings reveal that frequent and sustained use of mHealth applications is associated with improved preventive behaviors, including increased physical activity, better dietary adherence, and enhanced medication compliance. The results also show that mHealth applications strengthen patient engagement by fostering greater health awareness, self-efficacy, and interaction with healthcare providers. However, variations in outcomes are influenced by digital literacy, personalization features, and the level of contextual support available to users. The study concludes that mHealth applications function as effective tools for transforming preventive health and patient engagement, but their impact depends on user-centered design and integration within broader health systems. The novelty of this research lies in its integrated analysis of preventive health outcomes and patient engagement within a single empirical framework, highlighting mHealth as a foundational component of modern, participatory preventive healthcare.
Blockchain for Medical Records: Ensuring Security, Privacy, and Interoperability in Global Health Ali Reza; Leila Mahdavi; Fatemeh Hashemi; Rustiyana Rustiyana
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i4.2932

Abstract

Medical records play a critical role in healthcare delivery, yet existing digital health systems face persistent challenges related to data security, patient privacy, and interoperability across institutions and national borders. Blockchain technology has been proposed as a potential solution to these challenges, particularly within the context of global health systems characterized by fragmentation and regulatory diversity. This study aims to examine how blockchain technology is applied to medical record management in global health, with a focus on its capacity to ensure security, protect privacy, and support interoperability across healthcare systems. The study adopts a qualitative descriptive approach using systematic literature review and comparative case analysis. Secondary data are collected from peer-reviewed publications, policy reports, and documented blockchain-based medical record implementations across multiple regions. Data are analyzed through thematic synthesis and cross-case comparison. The findings reveal that blockchain adoption in medical records is unevenly distributed across regions and is strongly influenced by digital infrastructure readiness and regulatory environments. The study concludes that blockchain functions as an adaptive socio-technical infrastructure rather than a universal solution for medical records.  
Ethical Challenges of AI in Medicine: Balancing Innovation, Privacy, and Equity Ahmet Demir; Baran Akbulut; Sulastry Pakpahan; Rustiyana Rustiyana
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i6.3013

Abstract

Rapid advancements in artificial intelligence (AI) have transformed medical practice, offering unprecedented capabilities in diagnosis, treatment planning, and predictive analytics. These innovations, however, introduce complex ethical challenges related to patient privacy, algorithmic transparency, equity, and accountability. Growing reliance on AI in clinical environments has heightened concerns about data governance, bias in machine learning models, and uneven access to AI-enabled healthcare tools. This study aims to analyze the ethical tensions arising from AI integration in medicine and to identify strategies that balance technological innovation with the protection of fundamental ethical principles. A qualitative meta-synthesis approach was employed, drawing on peer-reviewed literature, policy documents, and real-world case analyses to examine patterns of ethical risk and mitigation frameworks. The findings reveal that privacy vulnerabilities, inequitable algorithmic performance, and opacity in decision-making processes represent the most frequent ethical concerns. The results also show that robust governance structures, transparent AI design, and inclusive dataset practices significantly reduce ethical risks. The study concludes that responsible AI in medicine requires a multidimensional ethical framework that integrates patient rights, algorithmic fairness, and institutional accountability.  
Artificial Intelligence in Personalized Learning: Enhancing Student Engagement through Adaptive Learning Systems Arief Hidayat; Maryana Maryana; Rustiyana Rustiyana; Triyugo Winarko
Journal Emerging Technologies in Education Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jete.v3i4.2508

Abstract

Background. Advancements in artificial intelligence (AI) have transformed educational practices by enabling personalized learning experiences that adapt to individual student needs. Traditional instructional methods often fail to accommodate diverse learning paces, preferences, and competencies, leading to disengagement and suboptimal learning outcomes. Purpose. This study investigates the effectiveness of AI-based adaptive learning systems in promoting personalized learning and increasing student engagement across multiple educational contexts.   Method. A mixed-methods research design was employed, combining quantitative analysis of engagement metrics and academic performance with qualitative exploration through student interviews and teacher observations. Results. Results indicated significant improvements in engagement, motivation, and learning outcomes, with adaptive feedback and personalized content contributing to sustained participation and deeper comprehension. Students reported higher satisfaction and perceived control over their learning processes, while educators noted more efficient monitoring and instructional planning. Conclusion. The study concludes that integrating AI into personalized learning systems can substantially enhance engagement and academic performance.
Gamification and Student Well-being: Exploring the Psychological Impact of Game-Based Learning Adiyati Fathu Roshonah; Nong Chai; Rustiyana Rustiyana; Dito Anurogo
Journal Emerging Technologies in Education Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jete.v3i4.2573

Abstract

Background. The increasing integration of digital learning environments has accelerated the adoption of gamification as a pedagogical approach to enhance engagement and student well-being. Contemporary education faces growing challenges related to academic stress, low motivation, and diminished emotional resilience among learners. Purpose. This research investigates the psychological effects of game-based learning, focusing on how gamification elements such as rewards, competition, collaboration, and feedback affect students’ emotional states, motivation, and perceived well-being.   Method. The study aims to bridge the gap between cognitive engagement and affective learning outcomes by empirically examining the psychological dynamics fostered by gamified educational experiences. A mixed-method design was employed, combining quantitative data from 320 undergraduate students across four universities and qualitative interviews with 24 participants. Results. Instruments included validated scales measuring intrinsic motivation, emotional engagement, and academic well-being. Data analysis was conducted using structural equation modeling (SEM) to identify causal relationships between gamification components and psychological indicators. Conclusion. The study concludes that gamification serves not only as a motivational tool but also as a psychological support mechanism in learning environments.
Cybersecurity Challenges in Educational Technology: Protecting Student Data in Digital Learning Platforms Rafiullah Amin; Jamil Khan; Shazia Akhtar; Rustiyana Rustiyana
Journal Emerging Technologies in Education Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jete.v3i4.2797

Abstract

Background. The widespread adoption of educational technology has transformed teaching and learning, yet it has simultaneously exposed students to heightened cybersecurity risks due to increased data collection, online interactions, and dependence on digital learning platforms. Purpose. This study aims to analyze the key cybersecurity challenges faced by educational institutions and to evaluate the effectiveness of current protection mechanisms in safeguarding student data.   Method. A mixed-method approach was employed, combining a quantitative assessment of security vulnerabilities across 15 widely used learning platforms with qualitative interviews involving IT administrators, teachers, and cybersecurity specialists. Results. The results reveal significant inconsistencies in data encryption standards, inadequate authentication protocols, and limited cybersecurity awareness among platform users. Findings further indicate that institutional policies often lag behind technological advancements, creating systemic exposure to privacy threats. Conclusion. The study concludes that strengthening student data protection requires an integrated framework that combines technological safeguards, user training, and continuous policy updates. These insights underscore the urgency for educational institutions to adopt proactive cybersecurity governance aligned with emerging digital learning demands.
Digital Storytelling for Inclusive Education: Voices of Students with Special Needs in the Classroom Ni Wayan Karmini; Nong Chai; Siri Lek; Rustiyana Rustiyana
International Journal of Educational Narratives Vol. 3 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijen.v3i5.2550

Abstract

Background. The integration of digital storytelling in inclusive education offers new possibilities for amplifying the voices and experiences of students with special needs. This study investigates how digital storytelling functions as a medium of expression, participation, and empowerment in inclusive classroom environments. Purpose. The research aims to explore how students with diverse learning abilities use digital narratives to communicate their perspectives, develop self-confidence, and engage collaboratively with peers. Method. A qualitative case study design was employed, involving 20 students with special needs and 5 inclusive education teachers across three schools. Data were collected through classroom observations, interviews, and analysis of student-created digital stories. Results. Thematic analysis revealed that digital storytelling enhanced students’ sense of agency and belonging by enabling them to represent their identities through multimodal expression combining voice, images, and text. Teachers reported that storytelling projects fostered empathy, cooperation, and deeper understanding among all students. Conclusion. The study concludes that digital storytelling provides an inclusive pedagogical approach that bridges cognitive, emotional, and social learning, promoting equitable participation and self-advocacy for students with special needs in mainstream education.
Co-Authors Aaraf Sharma Achmad Sanusi Ahmed Shah Ahmet Demir Ali Khan Ali Reza Aom Thai Arief Budi Pratomo Arief Hidayat Asep Didik Setia Permana Aung Myint Ava Lee Baatar Tserendorj Baran Akbulut Charlotte Thomsen COMPUTING INFORMATIKA Dadan Herdiansah Daeng Arifin Diana Salendra Dito Anurogo Dyah Maharani Elchin Mammadov Eliagus Telaumbanua Ethan Tan Fatemeh Hashemi Fatima Malik Haji Ahmad Makie Iin Almeina INFORMATIKA, COMPUTING Jamil Khan João Lima Karmini, Ni Wayan Khilda Nistrina Kusnadi Leila Mahdavi Li Na Li Wei Loso Judijanto Lucas Lima Lucas Wong Marcus Tan Maryana Maryana, Maryana Ming Kiri Muh. Safar Munkhzul Ganbat N Dede Khoeriah Nandar Hlaing Nenden Sri Rahayu Nong Chai Novianti Indah Putri Nurul Saila Park Jihoon Peter Wapun Puja Permana, Ridwan Rafiullah Amin Razia Khan Rit Som Rithy Vann Rohan Kumar Roshonah, Adiyati Fathu Rosmalina ROSMALINA ROSMALINA, ROSMALINA Roya Zahir Rusdi Rusdi Sandhy Dwi A Sari Mariahma Nova Sipayung Sauca Ananda Pranidana Selenge Batbold Shazia Akhtar Shela Tansyah, Lorenza Siri Lek Sofia Lim Sokha Dara Suharya, Yaya Sukiman Sulastry Pakpahan Sun Wei Thabo Mokoena Tiago Costa Triyugo Winarko Vann Sok Wirdatul Khasanah Yang Xiang Yudi Herdiana Yusuf Muharam Zara Ali Zaw Min Oo Zen Munawar