cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Usman Ependi
Contact Email
dr.u.ependi@gmail.coom
Phone
+6281271103018
Journal Mail Official
journal@adsii.or.id
Editorial Address
Street AMD, Tanjung Harapan Alley, Taman Kavling Mandiri Sejahtera B11, Palembang, South Sumatra, Indonesia, 30151
Location
Unknown,
Unknown
INDONESIA
International Journal of Artificial Intelligence and Science
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : -     EISSN : 30642728     DOI : https://doi.org/10.63158/IJAIS
Core Subject : Science,
Computer Science & IT
The International Journal of Artificial Intelligence and Science (IJAIS) is independently organized and managed by the Asosiasi Doktor Sistem Informasi Indonesia (ADSII). IJAIS is an open-access journal designed for researchers, lecturers, and students to publish their findings in the fields of Artificial Intelligence and Science. IJAIS serves as a platform for sharing innovative and original research, showcasing the latest advancements and technological developments in Artificial Intelligence and Science.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "vol. 3 no. 1 (2026): march" : 5 Documents clear
AI-Driven Intrusion Detection in Healthcare Systems: A Systematic Review of Techniques, Evaluation Practices, and Real-World Deployment Challenges Belinda Ndlovu
International Journal of Artificial Intelligence and Science Vol. 3 No. 1 (2026): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v3i1.58

Abstract

Cyberattacks threaten healthcare data security and clinical continuity. Although artificial intelligence (AI) promises improved intrusion detection, high accuracy rarely translates into clinical deployment. This systematic review examines evaluation practices and deployment conditions in healthcare intrusion detection research. Following PRISMA 2020, searches across ScienceDirect, Scopus, PubMed, MDPI and SpringerLink covered studies published between 2020 and 2024. Eleven studies met the inclusion criteria. Findings were triangulated against subsequent large-scale reviews to assess consistency across broader evidence. Classical machine learning dominated, with limited adoption of hybrid, federated or transformer-based approaches. Reported accuracy averaged 98.66%, predominantly using benchmark or synthetic datasets under controlled conditions. False positive rates, alert volumes, inference latency and robustness to distribution shift were rarely reported. No included study demonstrated sustained operation in a live clinical network. The review introduces the Healthcare IDS Deployment Readiness Assessment (HIDRA), comprising five dimensions and five ordinal readiness levels, alongside a minimum reporting set and six testable propositions. No study exceeded readiness level 2 of 5. HIDRA offers authors, reviewers and procurement teams a reusable instrument for assessing deployment readiness and identifying gaps between experimental performance and clinical feasibility.
Awareness and Adoption of Big Data Technology in Public Sector Auditing in Tanzania: Mediation Effect of Knowledge Management Practices. Albert Raymond Moshi; Alfre Sife; George Matto
International Journal of Artificial Intelligence and Science Vol. 3 No. 1 (2026): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v3i1.60

Abstract

Big data technology enables auditors to identify patterns and anomalies across extensive datasets, supporting improved audit quality. However, its adoption in Tanzania’s public sector auditing remains limited. This study examines the mediating role of knowledge management practices in the relationship between awareness and readiness to adopt big data technology. Drawing on Diffusion of Innovation (DOI) theory and Knowledge Management Theory (KMT), it proposes that awareness provides the foundation for adoption readiness, while knowledge creation, sharing and application translate awareness into practical capabilities. Data were collected from 221 randomly selected respondents and analysed using descriptive statistics and binary logistic regression. The findings indicate that knowledge management practices mediate the relationship between awareness and adoption readiness, highlighting their role in developing skills for technological adoption. Descriptive results reveal high operational awareness but low technical awareness among auditors, indicating a technical skills gap. These findings suggest that awareness alone is insufficient without mechanisms for acquiring, sharing and applying relevant knowledge. Public sector auditing institutions in Tanzania should strengthen knowledge management through targeted training programmes and knowledge sharing platforms. The study contributes an integrated DOI–KMT perspective explaining how awareness and knowledge management jointly support readiness for big data adoption in public sector auditing.
Understanding Artificial Intelligence Ethics and Safety: Foundations, Challenges, and Governance Muhammad Sami Intizar; Aqsa Siddique; Maria Sikandar; Madiha Sikandar; Maria Farooq
International Journal of Artificial Intelligence and Science Vol. 3 No. 1 (2026): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v3i1.61

Abstract

The deployment of artificial intelligence (AI) in safety-critical domains has made ethics and safety priorities. This paper examines AI ethics and safety by synthesizing technical, philosophical, and governance perspectives. It analyzes ethical principles, including fairness, transparency, privacy, accountability, and safety, while examining tensions in their implementation. Technical approaches, including robustness mechanisms, adversarial training, and interpretability methods, are considered alongside frameworks for value alignment and corrigibility. The discussion highlights the challenges of translating values into operational requirements, when competing objectives and contextual differences complicate implementation. The paper also assesses the governance landscape, comparing regulatory approaches across jurisdictions and evaluating binding and non-binding instruments for AI development. It argues that governance requires integrating technical safeguards, ethical reflection, and institutional accountability throughout the AI lifecycle. Moving beyond principle-based declarations, the paper emphasizes auditable and enforceable practices that connect ethical commitments with implementation and oversight. This perspective supports responsible innovation while addressing the risks associated with safety-critical AI deployment.
Artificial Intelligence in Developing Countries: A Systematic Review of Cross-Sector Applications and Implementation Challenges in South Asia Md. Farhan Hossan Tamin; Dr. Md. Ruhul Amin; Md. Salim; Morium Akter Shefa
International Journal of Artificial Intelligence and Science Vol. 3 No. 1 (2026): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v3i1.51

Abstract

In South Asia countries like Bangladesh, India, Pakistan, Sri Lanka and Nepal, artificial intelligence (AI) is rapidly changing several sectors. AI has aided solar energy forecasting, disaster prediction, healthcare diagnostics, agriculture precision, HRM, financial compliance in Bangladesh. This discourse identifies various applications of AI/ML in the field of environmental studies, healthcare, education, agriculture, and smart energy systems, with a focus on the Indian context, aiming to augment predictive accuracy and personalization. AI is used in Pakistan, especially in, personalized e-learning, covid-19 detection and prediction, breeding plants, and data-driven business decisions by advanced deep learning models. AI for Good: Sri Lanka showcases Health Misinformation Detection and Digital Drones, Technology for the Public Sector, Chatbots for Education, and presents a strong position on governance and ethical issues Nepal — Nepal is also showing the early signs of AI adoption in healthcare, education, and fintech space. However, amid critical opportunities to boost productivity and decision-making, poor data quality, inadequate infrastructure, ethical considerations, skill gaps, and rural access problems call for tailor-fitting policies, as well as capacity bolstering.
IoT in Healthcare: Infrastructure Challenges and Technical Feasibility for Adoption in Resource-Constrained Settings Alton Mabina
International Journal of Artificial Intelligence and Science Vol. 3 No. 1 (2026): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v3i1.72

Abstract

The integration of Internet of Things (IoT) technologies into healthcare is increasingly recognized as vital for improving patient outcomes, enhancing system efficiency, and addressing global health challenges. However, in resource constrained settings, adoption faces significant barriers that are often overlooked in existing literature. While prior studies emphasize IoT’s broad benefits, they provide limited empirical evidence on how devices perform under unstable connectivity, how they integrate with legacy hospital systems, and how they remain dependable amidst frequent power outages. This study therefore aimed to critically examine the infrastructure challenges and technical feasibility of IoT adoption in healthcare within such environments. Using the PRISMA methodology, a systematic review of peer reviewed articles published between 2021 and 2025 was conducted across IEEE Xplore, PubMed, Scopus, and Web of Science, focusing on connectivity, interoperability, and reliability. The findings revealed that unstable internet connectivity, poor system integration, and limited fault tolerance and power resilience are the most pressing challenges. The results underscore the need for context specific frameworks that strengthen connectivity resilience, enable seamless interoperability with fragmented systems, and ensure robust reliability safeguards. In conclusion, while IoT holds transformative potential for healthcare delivery, its sustainable adoption in resource constrained settings depends on innovative, resilient, and context driven solutions tailored to infrastructural realities.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 1 (2026): March Vol. 2 No. 2 (2025): September Vol. 2 No. 1 (2025): March Vol. 1 No. 1 (2024): September