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International Journal of Applied Artificial Intelligence and Robotics (IJAIC)
Published by PT. Literasi Teknologi Informatika
ISSN : 31247520     EISSN : 31241212     DOI : http://dx.doi.org/10.67745/ijaic.v2i1
Core Subject :
The International Journal of Applied Artificial Intelligence and Robotics (IJAIC) (E-ISSN: 3124-1212) is a peer-reviewed journal that focuses on the advancement and application of artificial intelligence (AI), machine learning, and robotics across various sectors of society. This journal serves as a platform for researchers, academics, and industry practitioners to publish original research, reviews, and theoretical works that address current challenges and innovations in intelligent systems, autonomous machines, and human-robot interaction. With an interdisciplinary scope, the journal encourages contributions that bridge the gap between theoretical AI frameworks and real-world implementations in fields such as healthcare, manufacturing, education, transportation, and smart environments. Applied Artificial Intelligence and Robotics Society is committed to fostering impactful scientific exchange by upholding rigorous peer-review standards and embracing open-access principles that promote transparency, accessibility, and global collaboration in the field of intelligent technologies.
Arjuna Subject : -
Articles 12 Documents
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Personalized Café Menu Recommendation Using Hybrid Collaborative and Content-Based Filtering Based on Location and User Interaction amirullah; fika adilah
International Journal of Applied Artificial Intelligence and Robotics Vol 2 No 1 (March 2026)
Publisher : PT. Literasi Teknologi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67745/ijaic.v2i1.19

Abstract

The culinary industry, particularly cafes, has experienced rapid growth with the increasing number of cafes in various regions. Intense competition has driven café owners to innovate in attracting and retaining customers. A personalized menu recommendation system has become an effective solution to provide relevant services for each customer. This study employs a hybrid method that combines Collaborative Filtering and Content-Based Filtering to address this issue. Three cafes are included in this study: Station Coffee Premium in Kuta Blang, Ocean Coffee in Kampung Jawa Lama, and Bagi-Bagi Coffee in Lancang Garam. The research utilizes three main parameters: menu data, order history data, and café location data. By using these three parameters, the system will display menu recommendations at the cafes according to the customer's preferences. The research results indicate that the recommendation system performed well in black box testing, achieving a 100% success rate in all tested scenarios. Furthermore, method testing shows that the Content-Based Filtering method provides consistent results with stable precision, recall, and MAP across various scenarios. However, the Hybrid Filtering method proved to be the most accurate and relevant, combining the strengths of Content-Based Filtering and Collaborative Filtering to deliver menu recommendations that align with customer preferences based on their order history and location.
AI in Cybersecurity: Ethical Challenges of AI-Based Intrusion Detection Systems: A Comparative Analysis of EU, U.S., and South Korea rihab karoud
International Journal of Applied Artificial Intelligence and Robotics Vol 2 No 1 (March 2026)
Publisher : PT. Literasi Teknologi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67745/ijaic.v2i1.21

Abstract

The concept of artificial intelligence (AI) has been increasingly used in the field of cybersecurity, especially in the development of intrusion detection systems (IDS). The conventional approach to developing intrusion detection systems involves the use of rule-based systems as well as signatures of attacks. However, this approach has been criticized for its inability to deal with emerging threats such as zero-day exploits. This has led to the development of intrusion detection systems that incorporate the concept of machine learning in the analysis of network activities. This approach has been effective in the detection of anomalies that could be indicative of malicious activities. However, the use of this approach in the development of intrusion detection systems has raised ethical issues. This paper seeks to discuss the ethical issues surrounding the application of AI-based intrusion detection systems by first presenting an overview of the technical basis of the systems and the technical benefits they offer. Secondly, the paper will discuss the ethical issues surrounding the application of the system. Some of the ethical issues discussed will include the lack of explainability of machine learning algorithms, the possibility of biased decision-making by the system, and the ethical issues surrounding the collection of large amounts of personal information. Additionally, the paper will discuss the technical solutions to the ethical issues surrounding the application of the system. Some of the technical solutions to the ethical issues will include the application of explainable AI, federated learning, differential privacy, and synthetic data. Lastly, the paper will discuss the regulatory frameworks adopted by the European Union, the US, and South Korea regarding the application of the system. The analysis identifies the gaps in the regulations as well as the technical limitations in the ethical use of AI-based IDS. The study offers recommendations to improve the design of AI-based IDS. The study emphasizes the need to strike a balance between the increasing technical capabilities of AI systems and the need to have proper ethical and regulatory checks in the use of AI in cybersecurity.

Page 2 of 2 | Total Record : 12

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