International Transactions on Artificial Intelligence

The Role of Globalization in Indonesian Evolution Influence on Media Digital Literacy Language AI

2025-06-10T06:10:24+00:00

Globalization has brought significant changes in various aspects of life, including language use. As the official language and a symbol of national cultural identity, the Indonesian language is not immune to the effects of globalization, particularly in mass media, literature, and academic publications. This article analyzes the impact of globalization on the use of the Indonesian language, with a focus on two main dimensions: literature and digital media. In literature, globalization tends to introduce foreign elements that influence language style and narrative structure, while in digital media, the use of Indonesian is often mixed with foreign languages and slang influenced by global trends. Furthermore, in academic publications, globalization introduces new terms that often use international languages, such as English, which reduces the use of Indonesian in scholarly contexts. This study also highlights the challenges faced in maintaining the standardization of the Indonesian language amid the rapid flow of globalization, emphasizing the crucial role of language institutions and society in preserving the language's integrity. Through this analysis, it is hoped that strategies will be identified to preserve the richness and standardization of the Indonesian language, ensuring its relevance in an increasingly globalized world.

Application of Database Normalization in Increasing Data Storage Efficiency

2025-06-04T07:02:14+00:00

Database normalization is a key process in relational database design that reduces redundancy and ensures data integrity. As data volumes increase, maintaining efficient and consistent storage becomes critical. This study investigates the application of normalization techniques from First Normal Form (1NF) to Third Normal Form (3NF) on a sample inventory database to evaluate their impact on storage efficiency. The process focuses on eliminating data repetition and optimizing table structures to enhance performance. Experimental results show that normalization reduces database size by approximately 30%, significantly minimizing redundancy. Smaller, more organized tables improve storage utilization, especially in large-scale systems. However, normalization can introduce query complexity due to increased joins, potentially affecting execution time. Despite this, the trade-off is considered acceptable given the gains in data integrity and storage optimization. This research emphasizes the value of normalization for scalable and maintainable systems. It also aligns with Sustainable Development Goals (SDGs), particularly Goal 9 (Industry, Innovation, and Infrastructure) and Goal 12 (Responsible Consumption and Production), by promoting efficient digital infrastructure and responsible data management practices. These improvements contribute to more sustainable, cost-effective systems in industries relying on large-scale data, such as e-commerce, healthcare, and finance. In conclusion, normalization is an essential tool for optimizing storage and ensuring data consistency in relational databases. Although performance trade-offs exist, they can be mitigated through indexing and query optimization. The study offers insights for database designers seeking to balance efficiency and system performance in data-intensive environments.

Designing an AI-Based Expert System to Enhance Standard Language Use on Social Media

2025-05-26T17:27:42+00:00

The widespread use of social media has significantly transformed the way people communicate, often leading to informal, shortened, and mixed-language expressions that deviate from standard linguistic norms. This phenomenon raises concerns about the erosion of proper language usage, particularly among younger generations. This study aims to design an AI-based expert system to support and improve the use of standard Indonesian on digital communication platforms. By integrating language rules into a decision-support model, the system can provide real-time feedback on grammar, spelling, and appropriate word usage during online interactions. This research adopts a qualitative approach through literature review and system design framework, emphasizing the need for intelligent linguistic assistance tools. The proposed expert system will be capable of analyzing user-generated content, detecting deviations from formal language standards, and offering suggestions or corrections. The findings contribute to ongoing efforts to preserve national language identity in the digital age and promote linguistic awareness through technology. Ultimately, this study offers an innovative solution to bridge the gap between informal digital trends and the preservation of standard language, encouraging responsible and high-quality communication in the modern digital ecosystem.

Design and Implementation of a Relational Database for an Academic Information System

2025-06-04T07:23:49+00:00

This paper focuses on the design and implementation of a relational database for an Academic Information System (AIS), aiming to streamline data management and improve the efficiency of academic processes. The study highlights challenges faced by educational institutions in managing large volumes of student and academic data, often resulting in inefficiencies and errors. The objective is to create a relational database supporting student information, course registrations, faculty assignments, and academic records. The methodology includes developing an Entity-Relationship (ER) model, applying database normalization, and implementing the system using Structured Query Language (SQL). The result is a functional database that improves data retrieval speed, enhances integrity, and simplifies access for academic staff and administrators. The solution contributes to optimizing academic data management by ensuring consistency, reducing errors, and offering scalability for future growth. This research also includes system performance evaluations and stakeholder feedback from faculty, staff, and students. Findings reveal significant improvements in usability, accuracy, and system responsiveness compared to prior legacy systems. Integrated security measures, including role-based access and encryption, safeguard data and ensure compliance with institutional privacy policies. The relational database framework supports real-time access, centralized control, and efficient administrative workflows. Overall, this system strengthens digital infrastructure in educational institutions and aligns with broader digital transformation goals. It enhances data-driven decision-making and supports sustainable, scalable, and secure academic information management, making it a valuable contribution to improving operational performance and educational service delivery in higher education settings.

Analysis of the Effectiveness of Visual Language and Narrative in Conveying Value Propositions in Pitching Decks

2025-06-10T04:57:17+00:00

A pitching deck is a strategic communication medium that plays a crucial role in delivering value propositions to prospective investors in a concise yet persuasive manner. The effectiveness of message delivery through a pitching deck is highly determined by the quality of the visual language and narrative employed. This study aims to analyze how visual elements such as color, typography, infographics, and iconography interact synergistically with the narrative structure to convey complex business values more intuitively and emotionally. The methodology used is a literature study with a thematic analysis approach of scientific sources published within the last five years. The findings indicate that consistent visual integration and structured narrative not only enhance audience understanding but also strengthen memory retention and emotional engagement with the presented business ideas. Specifically, this combined approach opens significant opportunities for the development of intelligent models capable of adaptively evaluating and recommending pitching deck designs with personalization. The implications of this study point to the need for communication decision support systems that focus not only on business content but also on how data driven technologies and algorithms can improve the quality of visual narrative elements as persuasive tools in the startup and investment world.

Implementation of Cloud-Based Information Management to Enhance Work Efficiency in Information Technology Companies

2024-05-16T07:17:58+00:00

In the era of rapid digital transformation, Information Technology (IT) companies are increasingly challenged to manage large and complex datasets efficiently. This study investigates the implementation of cloud-based information management systems as a strategic approach to improving work efficiency within IT companies. A mixed-method approach was adopted, combining quantitative data from system performance metrics and employee productivity reports with qualitative insights obtained from surveys and in-depth interviews with employees and managers. The results show a significant improvement in work efficiency, with a 30% decrease in system response time and a 25% increase in employee productivity. Additionally, qualitative findings indicate enhanced employee satisfaction, better collaboration, and more effective decision-making due to improved data accessibility and streamlined workflows. The integration of cloud-based systems with existing IT infrastructure is also identified as a critical factor for the success of such implementations. These findings highlight that cloud technology not only addresses operational challenges but also supports broader strategic goals. The study recommends IT companies invest in employee training, robust data security measures, and seamless integration strategies to fully leverage the potential of cloud-based information management in optimizing performance and maintaining competitiveness.

Foundations for AI Driven Communication Models Qualitative Analysis of Indonesian Language Adaptation E-Commerce

2025-06-02T07:30:51+00:00

In the rapidly evolving digital economy, effective communication between sellers and customers on e-commerce platforms plays a pivotal role in shaping user experience and satisfaction. This study explores the adaptation of the Indonesian language within these interactions, focusing on the linguistic styles, usage patterns, and communication challenges faced by sellers and customers. Employing a qualitative descriptive approach, data were collected from direct conversations and product descriptions on leading Indonesian e-commerce platforms. Findings reveal a dominant use of semi-formal and informal language styles, enhanced by abbreviations, emojis, and popular digital jargon, which collectively foster a sense of familiarity and responsiveness. However, balancing language standardization with the demands for fast and engaging communication remains a significant challenge. The results underline the critical need for communication models that can adapt to the dynamic nature of digital discourse while maintaining clarity and politeness. This research lays the groundwork for developing intelligent communication systems powered by artificial intelligence, which can effectively interpret and generate contextually appropriate language in e-commerce settings. The insights gained here offer valuable foundations for future work in creating AI-driven tools aimed at enhancing digital customer engagement and satisfaction through culturally and linguistically aware communication strategies.

Transformation of Indonesian Language in Social Media Using AI Expert Systems and Machine Learning

2025-06-04T08:17:51+00:00

This study explores the transformation of the Indonesian language on social media platforms by integrating advanced artificial intelligence techniques, including expert systems and machine learning algorithms. The rapid evolution of communication on platforms such as TikTok, Twitter, and Instagram has led to a dynamic shift in language use, characterized by increased creativity in slang, abbreviations, and code-mixing, alongside challenges in maintaining compliance with standard language norms. By applying intelligent systems capable of analyzing large volumes of social media data, this research aims to identify patterns of linguistic innovation and deviations from conventional language rules. The expert system framework supports automated detection and classification of language variations, while machine learning models enhance the accuracy and adaptability of language analysis over time. Findings indicate that AI-driven approaches can effectively balance the dual needs of fostering linguistic creativity and preserving language standards, providing valuable insights for language educators, policymakers, and digital content developers. The study underscores the potential of intelligent technologies to facilitate language monitoring and support efforts in maintaining the cultural identity of the Indonesian language amid the fast-paced digital communication landscape.

Innovating Communication Adaptation on Social Media Using AI-Based Decision Support Systems

2025-05-26T17:41:28+00:00

In the rapidly evolving landscape of social media, effective communication adaptation is crucial for enhancing user interaction and engagement. This study explores the role of AI-based decision support systems in innovating how social media users adapt their communication styles to diverse contexts and audiences. Leveraging intelligent algorithms, the research investigates mechanisms that enable dynamic analysis and personalized recommendations to improve message clarity, relevance, and impact. By integrating data riven insights and machine learning models, the system supports users in crafting adaptive and persuasive communication strategies that resonate with their network. The findings highlight the potential of combining expert systems with advanced computational techniques to facilitate real-time decision making in social interactions online. This approach not only enhances the quality of communication but also fosters more meaningful connections across heterogeneous user groups. The study contributes practical implications for developers and practitioners aiming to design intelligent platforms that empower social media users with innovative tools for communication adaptation, ultimately driving more effective and engaging digital experiences.

AI Driven Support Networks for Reducing Emotional Isolation Among Infertile Couples

2025-04-16T04:59:01+00:00

Infertility is a global health issue that often leads to social stigma and emotional isolation, profoundly affecting the well-being of individuals and couples. This study explores the role of AI-driven support networks in alleviating these challenges by providing accessible, anonymous, and personalized emotional support. Utilizing a mixed-methods approach, data was collected through surveys and interviews with infertile couples, alongside an analysis of existing AI mental health platforms. The findings reveal significant reductions in stress, anxiety, and depression levels among participants, with anonymity and scalability being the most valued features of these platforms. This study underscores the transformative potential of AI in bridging the gaps in traditional support systems, offering a scalable and inclusive solution for mental health care. The implications for healthcare providers, technology developers, and infertile couples are profound, paving the way for innovative approaches to addressing stigma and emotional well-being.