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All Journal Jurnal Ilmiah Sinus Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) YASIN: Jurnal Pendidikan dan Sosial Budaya MASALIQ: Jurnal Pendidikan dan Sains Electronic Journal of Education, Social Economics and Technology JGHTT
Dimas Pamilih Epin Andrian
Politeknik Nest
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Veterinary Other

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Journal : MASALIQ: Jurnal Pendidikan dan Sains

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Dampak Teknologi Informasi Berbasis Artificial Intelligence terhadap Kepuasan dan Loyalitas Wisatawan: Tinjauan Systematic Literature Review Fahrurozi, Slamet Kurniawan; Epin Andrian, Dimas Pamilih
MASALIQ Vol 5 No 2 (2025): MARET
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/masaliq.v5i2.5040

Abstract

This study aims to analyze the impact of Artificial Intelligence (AI)-based information technology on tourist satisfaction and loyalty through a systematic literature review method. Following the PRISMA guidelines, researchers conducted a literature search in the ScienceDirect and SpringerLink databases using keywords such as "artificial intelligence," "tourist satisfaction," and "tourist loyalty." Of the 40 articles identified, 12 articles met the inclusion criteria and were analyzed thematically. The results showed that AI significantly improves tourist satisfaction through service personalization, operational efficiency, and better interactions. Applications such as recommendation systems, chatbots, and predictive analytics have proven effective in improving the tourist experience. However, the implementation of AI also faces challenges such as data privacy, algorithmic bias, and low technology adoption. The implications of this study are the importance of optimizing the use of AI by ensuring data security, reducing bias, and improving education for service providers and tourists. This study provides recommendations for further research, including exploring the impact of AI on specific tourist segments and investigating the long-term impact on loyalty.
From Development to Implementation: Usability and Performance Evaluation of an Internship System Andrian, Dimas Pamilih Epin; Massaty, Muhammad Hassan
MASALIQ Vol 5 No 2 (2025): MARET
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/masaliq.v5i2.5078

Abstract

This study aims to develop a web-based internship management system to enhance efficiency, transparency, and usability for students and university administrators. Traditional internship management methods often suffer from inefficiencies and limited accessibility, necessitating a more structured digital approach. By integrating User-Centered Design (UCD) and Feature-Driven Development (FDD), an Agile methodology, this research focuses on improving system usability and user experience. The method involved data collection through interviews with students and university administrators to identify key requirements. The platform was developed iteratively, incorporating continuous feedback to ensure alignment with user needs. Feature prioritization and incremental implementation allowed for a structured approach to system refinement, resulting in a more effective and adaptable solution. The results indicate that the developed platform successfully addresses key challenges in internship tracking, document management, and communication. Usability evaluations using User Experience Quality (UEQ) metrics show high satisfaction in accuracy, efficiency, and clarity, while engagement remains an area for further improvement. This research contributes to digital transformation in internship management, with future potential for AI-driven automation in feedback analysis and internship matching.
Co-Authors Dhomas Hatta Fudholi Didik Nugroho Muhammad Hassan Massaty, Muhammad Hassan Rachmawati, Annisa Noor Slamet Kurniawan Fahrurozi Sri Tomo Yudi prayudi