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International Transactions on Artificial Intelligence (ITALIC)
Published by Pandawan Sejahtera Indonesia
ISSN : 29636086     EISSN : 29631939     DOI : https://doi.org/10.33050/italic
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Library & Information Science
International Transactions on Artificial Intelligence (ITALIC) is an international, open-access journal established to publish groundbreaking research in the field of Artificial Intelligence (AI). ITALIC focuses on both theoretical and experimental AI research and explores its applications across various interdisciplinary fields. The journal places a strong emphasis on emerging technologies that contribute to sustainable development, in line with the United Nations Sustainable Development Goals (SDGs). ITALIC welcomes contributions that cover a wide range of AI applications, including machine learning, neural networks, natural language processing, AI in energy management, sustainability, and urban infrastructure. In addition to original research, the journal publishes reviews, mini-reviews, case studies, and commentaries, fostering dynamic discussions on the evolving role of AI in addressing global challenges. All submissions are rigorously reviewed through a double-blind peer-review process, ensuring high academic standards. As an open-access journal, ITALIC makes its content freely available to a global audience, enhancing the dissemination of critical insights. Each article is assigned a Digital Object Identifier (DOI), ensuring permanent access and easy referencing.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 68 Documents
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The Role of The Internet of things in Healthcare Transformation Sardar M. N. Islam
International Transactions on Artificial Intelligence Vol. 2 No. 1 (2023): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i1.380

Abstract

The internet of things (IoT) must now be enhanced in the eraof the industrial revolution 4.0 to simplify healthcare andincrease its effectiveness in monitoring patients' healthconditions remotely.The Internet of Things (IoT) is a globalnetwork of interconnected computing devices that includespeople, machines, and other living things. Each of thesedevices has been given a unique identifier and has the abilityto send and receive data over the network without the need fordirect human or machine-to-human communication.Connectivity, standards, smart analytics, smart actions,networks, sensors, smart analytics, devices, cloud, and userinterfaces are some of the elements of the Internet of Things.IoT transformation in personal healthcare can be classifiedinto in-clinic care and remote monitoring. IoT security systemsand patient data protection must be carefully considered toprevent data leakage or improper processing of health dataobtained through the use of IoT. To make the most of IoT, thegrowth of IoT in the healthcare industry must be matched bylaws and regulations governing personal data security.
The Role of Smart Automation in Tourism Industry Management using Smart PLS Oscar Jayanagara; Sri Watini
International Transactions on Artificial Intelligence Vol. 2 No. 1 (2023): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i1.385

Abstract

The main objective of this research is to critically examine existing research and the application of smart automation (Smart PLS) in the tourism industry. Additionally, this study proposes a new evaluation framework to identify vulnerabilities in the adoption of smart automation. This is a synthesis and evaluation study, where qualitative findings about the implementation of smart automation in the tourism industry are presented. The study uses a seven-dimensional evaluation framework based on Rogers' diffusion theory (2003) to assess the current implementation of intelligent automation. The vulnerability of adopting smart automation in the tourism industry depends largely on the type of smart automation used. Search/booking engines, virtual agents, and chatbots are three types of intelligent automation that have high vulnerability levels. The limitations and implications of this study are that it bridges the foundations of innovation diffusion theory and the application of smart automation. The findings from this research can help researchers, developers, and managers evaluate the vulnerability of adopting smart automation technology in the tourism industry. This paper is also one of the few papers that assess the vulnerability of adopting smart automation in the tourism industry, and it develops a theory-based evaluation framework to systematically evaluate smart automation innovation in the tourism industry.
For a CPS-IoT Enabled Healthcare Ecosystem Consider Cognitive Cybersecurity Anggy Giri Prawiyogi; Lista Meria
International Transactions on Artificial Intelligence Vol. 2 No. 1 (2023): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i1.398

Abstract

Cyber-Physical Systems (CPS)-Internet of Things (IoT) enable healthcare services and infrastructure to enhance human lives, but they are vulnerable to various emerging cyber attacks. Cybersecurity specialists struggle to keep up with increasingly sophisticated attack methods. An urgent requirement exists for inventive cognitive cybersecurity in CPS-IoT enabled healthcare ecosystems. This paper presents a framework for cognitive cybersecurity to simulate human cognitive behavior in anticipating and responding to new and evolving cybersecurity and privacy threats to CPS-IoT and critical infrastructure systems. The framework encompasses the conceptualization and description of a layered architecture that integrates Artificial Intelligence, cognitive methods, and innovative security mechanisms.
Application of RESTful Method with JWT Security and Haversine Algorithm on Web Service-Based Teacher Attendance System Suryari Purnama; Mustofa Kamal; Ahmad Bayu Yadila
International Transactions on Artificial Intelligence Vol. 2 No. 1 (2023): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i1.400

Abstract

A teacher attendance system utilizing fingerprints has been implemented at Ponpes Daarul Muttaqien II. However, fingerprint recognition errors frequently occur if the scanner is unclean. Teachers fill out an attendance update form for various reasons, including forgotten attendance, damaged fingerprints, and unnoticed absences. The Human Resources Bureau receives updates from around 28 instructors within one week. Statistics reveal that 30% of instructors update due to finger injuries preventing recognition, and 70% due to forgetting. Amid the COVID-19 pandemic, organizations advise remote work. Similarly, only specific sections, like technical and security units, require attendance at Ponpes Daarul Muttaqien II. Due to the potential risks of linking fingerprints to virus transmission, this study proposes a web-based attendance system. It employs the RESTful API approach, JWT Security, and the Haversine Formula Algorithm. The system mandates employees to be within 100 meters of a designated coordinate point for attendance. The system tracks attendance status, arrival and delay times, and absence status. Future enhancements might involve facial recognition for more robust validation. In conclusion, this innovative approach addresses attendance challenges, offering adaptability, security, and potential fraud prevention.
AI Dialog: Utilization, Challenges, and Ethics in the Age of Artificial Intelligence Riya Widayanti; Tatik Mariyanti
International Transactions on Artificial Intelligence Vol. 2 No. 1 (2023): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i1.401

Abstract

In this writing, we will delve into every aspect of the utilization, challenges, as well as ethical considerations in harnessing Artificial Intelligence (AI) Technology, with a focus on ChatGPT, an exceptional language model. AI technology has become an inseparable element in modern life, manifesting across various sectors such as business, healthcare, governance, and several others. The capabilities of AI are capable of driving data-driven decision-making, reducing the potential for human errors, and enhancing process efficiency. However, its impact is also not devoid of potential risks, including data security and the replacement of human roles. To ensure intelligent AI usage, it is imperative to uphold AI ethics, protect user information and privacy, strive to prevent discriminatory practices, and ensure system safety stability. The questions raised in this study revolve around the benefits and challenges of implementing ChatGPT, optimizing its use, and its role in the realm of education. The mission of this study is to unravel the benefits, challenges, as well as ethical considerations in the utilization of ChatGPT in the AI era, while ensuring responsible steps in its application.
Stepping Forward: Enhancing Cognitive Learning Outcomes through Hybrid RCCR-Based Learning on Circulatory System Material Cicilia Sriliasta; Dewi Sri Surya Wuisan
International Transactions on Artificial Intelligence Vol. 2 No. 1 (2023): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i1.404

Abstract

The time constraints at the end of the semester often lead to suboptimal delivery of circulatory system materials, which consequently affect students' learning outcomes. Hence, this research aims to delve into the impact of applying the RCCR (Reflect-Collaborate-Convey-Record) approach based on Hybrid Learning on enhancing cognitive learning outcomes. This study utilizes a quasi-experimental design, encompassing a control group with pretests and posttests, though they are not equivalent. Evaluation of cognitive learning outcomes involves multiple-choice questions in both the pretest and posttest phases. The data from these evaluations are quantitatively analyzed in-depth. After the learning process, the average posttest score for the control group reached 71.61, while the experimental group achieved a score of 81.25. Statistical analysis indicates a significant difference in posttest results between the experimental and control groups. The analysis of change scores (N-gain) also shows a significant difference between the experimental and control groups (p<0.05). These findings highlight a strong connection between cognitive learning outcomes and metacognitive skills mastery (r=0.83). Consequently, this learning model presents a valuable alternative for overcoming classroom time constraints and effectively enhancing cognitive learning outcomes.
Playing Smart with Numbers: Predicting Student Graduation Using the Magic of Naive Bayes Shilpa Mehta
International Transactions on Artificial Intelligence Vol. 2 No. 1 (2023): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i1.405

Abstract

The quality of a higher education institution is often measured by the accreditation granted by the National Accreditation Agency for Higher Education (BAN-PT). In this context, one of the primary assessment criteria is the graduation rate of students. An intriguing study employs the Naive Bayes algorithm to forecast whether students will graduate on time or face delays. The resulting predictive outcomes offer valuable insights and input to universities for enhancing their educational standards. The Naive Bayes method brings its unique advantages, particularly in predicting graduation rates based on real-world data. This ensures that the generated predictions can be relied upon and utilized as guidelines for future projections. This predictive mechanism encompasses 14 pivotal factors. These factors include gender, student status, age, marital status, performance across semesters 1 through 8, as well as cumulative performance, culminating with the information of whether a student passed or not. Within this study, data from 302 students of the 2018 cohort were involved. Data processing was carried out using the Python programming language within the Jupyter Notebook environment. The results unveil an impressive accuracy rate, reaching 85%. In terms of precision, the prediction for delays achieved a value of 0.42, while timely graduation prediction scored 0.95. Furthermore, the accuracy in identifying delay cases reached 0.65, compared to 0.88 accuracy for timely predictions. The f1 score for delay predictions stood at 0.51, while timely graduation predictions reached 0.91. These results illustrate that this algorithmic approach is capable of providing accurate and well-balanced insights into student graduation predictions.
Exploring the Research on Utilizing Machine Learning in E-Learning Systems Harfizar; Edian Martin; Muhamad Abdul Aziz; Allif Pujihanarko; Noviesta Riani Pratiwi
International Transactions on Artificial Intelligence Vol. 2 No. 1 (2023): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i1.422

Abstract

Naturally, you are already familiar with the phrase "E-Learning" in a time when information technology rules and everything is digital. electronic learning, e-learning. Through e-learning, anyone, at any time, can participate in the teaching and learning process. Distance and time are no longer impediments to completing activities, including learning in this situation, just like other online activity concepts. Nearly all schools and institutions today use eLearning in some capacity. The COVID-19 pandemic and the rapidly changing globe necessitate that everything is done online in addition to the world being entirely digital. This study employed the systematic literature review (SLR) methodology. The outcomes, which can be employed in a variety of Machine Learning (ML) applications, are acquired. Artificial intelligence (AI) in the form of machine learning (ML) enables software applications to predict outcomes more accurately even when they are not expressly programmed to do so. To forecast new output values, machine learning algorithms use historical data as input.
Assessing Customer Satisfaction in AI-Powered Services: An Empirical Study with SmartPLS Achani Rahmania Az Zahra; Dendy Jonas; Ita Erliyani; Rosdiana; Natasya Aprila Yusuf
International Transactions on Artificial Intelligence Vol. 2 No. 1 (2023): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i1.432

Abstract

In the contemporary business landscape, the evaluation of customer satisfaction plays a pivotal role in assessing the effectiveness of AI-powered services. This empirical study, bolstered by the robust analytical tool, SmartPLS, systematically scrutinizes the intricate relationship between AI-powered services and customer satisfaction. With a rigorous and methodical service quality analysis, conducted with a sample of 189 respondents, we unveil the salient attributes and determinants that underpin customer satisfaction within the framework of AI-driven services. This research contributes substantially to a more profound comprehension of how organizations can strategically enhance customer satisfaction via the adept deployment of AI technologies. The ensuing findings, derived from the comprehensive analysis of 189 respondents, provide invaluable insights into the optimization of service quality within AI-powered ecosystems. These insights hold the potential to cultivate heightened levels of customer satisfaction and engender enduring loyalty, which is of paramount importance in the contemporary business landscape.
A Comprehensive Survey of Machine Learning Applications in Medical Image Analysis for Artificial Vision Alwiyah Alwiyah; Widhy Setyowati
International Transactions on Artificial Intelligence Vol. 2 No. 1 (2023): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i1.438

Abstract

This study presents a thorough survey of the applications of machine learning in medical image analysis for artificial vision,aiming to offer a comprehensive understanding of the evolving intersection between machine learning and medical imaging.With the rapid advancement of artificial vision technologies, the integration of machine learning algorithms has becomepivotal in revolutionizing medical image analysis. The survey explores a diverse range of machine learning applicationswithin the medical imaging domain, encompassing techniques such as convolutional neural networks (CNNs), support vectormachines, and decision trees. The focus lies in elucidating the role of machine learning in enhancing the accuracy, efficiency,and diagnostic capabilities of medical image analysis systems. Key topics addressed in the survey include image segmentation, classification, and detection, with a specific emphasis on applications in radiology, pathology, and ophthalmology. Additionally, the survey discusses challenges and opportunities in the integration of machine learning into medical image analysis, providing insights into current trends and future directions. This comprehensive survey serves as a valuable resource for researchers, practitioners, and healthcare professionals seeking an in-depth overview of the diverse applications and evolving landscape of machine learning in medical image analysis for artificial vision.

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