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Artificial Intelligence that Exists in the Human Mind Muhammad Rehan Anwar; Fitra Putri Oganda; Nuke Puji Lestari Santoso; Muhamad Fabio
International Transactions on Artificial Intelligence Vol. 1 No. 1 (2022): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v1i1.87

This article asserts that some general AIs are incomprehensible to humans and that computers are innately incapable of performing certain human cognitive activities, including 1) Misconceptions of Godel's theorem and related statistics and mathematical results are at the root of objections to using AI; 2) Reductionism and objections to monistic reductionism, as well as a minimal appreciation of responses to different levels of discussion; 3) Concerns raised by a thorough understanding of computers or computing Developing a model in which "cognitive" functions can be created independently and referred to as "wetware" is pragmatic, according to a psychologist. The cognitive and essential assumptions for individuals involved in the usefulness of AI are pragmatic. From this point of view, AI is heavily invested in the future of cognitive psychology, if only because of their similar fate.

Boarding House Search Information System Database Design Muhammad Rehan Anwar; Suryari Purnama
International Journal of Cyber and IT Service Management (IJCITSM) Vol. 2 No. 1 (2022): April
Publisher : International Institute for Advanced Science & Technology (IIAST)

Technological advancements are causing humans to produce several inventions and developments to accomplish anything. Which one utilizes internet technology to obtain information as quickly and simply as possible? For example, if someone moves to a new area for work or school, they will need boarding houses. However, this information is difficult to obtain because it is traditionally passed down from person to person, and the boarding house owner finds it difficult to promote his boarding house. As a result, individuals require a computerized system to resolve this issue. This study aims to create a database design for a boarding home information system. This study employs the DBLC (Database Life Cycle) technique, which illustrates the life cycle of a database that may be developed at any time. Database design is separated into three parts: conceptual, logical, and physical database design.

Boarding House Search Information System Database Design Muhammad Rehan Anwar; Suryari Purnama
International Journal of Cyber and IT Service Management Vol. 2 No. 1 (2022): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v2i1.89

Technological advancements are causing humans to produce several inventions and developments to accomplish anything. Which one utilizes internet technology to obtain information as quickly and simply as possible? For example, if someone moves to a new area for work or school, they will need boarding houses. However, this information is difficult to obtain because it is traditionally passed down from person to person, and the boarding house owner finds it difficult to promote his boarding house. As a result, individuals require a computerized system to resolve this issue. This study aims to create a database design for a boarding home information system. This study employs the DBLC (Database Life Cycle) technique, which illustrates the life cycle of a database that may be developed at any time. Database design is separated into three parts: conceptual, logical, and physical database design.

Emotional Well-Being and Psychological Support in Infertility A Multi-Modal AI Approach Ninda Lutfiani; Dhea Ayunanda Astrieta; Viedya Wildan; Hasta Sulistyaningrum; Muhammad Rehan Anwar; Eka Dian Astuti
International Journal of Cyber and IT Service Management Vol. 5 No. 1 (2025): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v5i1.188

Infertility affects millions of couples worldwide, often leading to significant emotional distress. Despite advancements in medical treatments such as IVF, the psychological challenges associated with infertility remain under-addressed. This study introduces a multi-modal AI system that integrates natural language processing (NLP), sentiment analysis, and voice interaction to provide personalized psychological support for individuals and couples experiencing infertility. A Randomized Controlled Trial (RCT) was conducted with 200 participants, comparing the AI intervention group to a control group receiving standard care. The AI system demonstrated significant reductions in anxiety and depression levels (GAD-7 and PHQ-9), as well as improvements in emotional well-being (PANAS). The intervention group reported higher user satisfaction (85%) and engagement, with participants using the system an average of four times per week. The AI system ability to offer empathetic, real time emotional support was highly rated by users. However, challenges such as cultural sensitivity and voice interaction accuracy were noted. This study highlights the potential of AI in mental health, particularly in addressing the often overlooked psychological needs of individuals facing infertility. The findings suggest that AI-driven solutions can bridge gaps in psychological care by providing scalable, cost-effective, and accessible support. Further research is needed to refine the system capabilities and explore its long-term impact on emotional well-being.

The Future of Adaptive Machine Learning Algorithms in High-Dimensional Data Processing Anne Wilson; Muhammad Rehan Anwar
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.656

This study investigates the potential of adaptive machine learning algorithms for processing high-dimensional data across various fields, directly supporting the advancement of the United Nations Sustainable Development Goals (SDGs) such as healthcare, economic growth, and sustainable cities. The core objectives are to critically review existing methods, tackle the challenges posed by large datasets, and project future developments in adaptive machine learning technologies. Through a comprehensive analysis of diverse algorithms including autoencoders, deep learning, reinforcement learning, and ensemble methods this research evaluates their efficacy in managing the complexities of large-scale data. Results demonstrate that while deep learning models provide the highest accuracy, they also demand considerable computational resources. Conversely, ensemble methods and autoencoders show competitive performance with greater efficiency, although reinforcement learning exhibits adaptability at the cost of reduced scalability. The findings advocate for enhanced focus on improving the efficiency, generalization capabilities, and interpretability of these algorithms to better accommodate the increasing complexity of data-driven environments. Promising applications identified include enhancing diagnostic accuracy in healthcare, optimizing financial analytics, and advancing autonomous system technologies. The study concludes that significant progress in adaptive machine learning will be crucial for achieving SDGs by enabling more effective and efficient data analysis solutions, thereby fostering sustainable development across multiple domains.

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