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All Journal Semantik IJBE (Integrated Journal of Business and Economics) MBIA Dinasti International Journal of Management Science Jurnal Humanities Pengabdian Kepada Masyarakat APTIKOM Journal on Computer Science and Information Technologies (CSIT) Jurnal Nasional Ilmu Komputer International Journal of Community Service & Engagement Jurnal Pengabdian kepada Masyarakat Bina Darma Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Jurnal Pengabdian Mandiri Jurnal Ilmiah Ilmu Terapan Universitas Jambi Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah Atestasi : Jurnal Ilmiah Akuntansi
Sunda Ariana, Sunda
Universitas Bina Darma
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Nursing Public Health Social Sciences Other

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Journal : Jurnal Ilmiah Ilmu Terapan Universitas Jambi

 1 
EVOLVING TRENDS IN HUMAN RESOURCE MANAGEMENT RESEARCH WITHIN TOURISM: INSIGHTS FROM A BIBLIOMETRIC ANALYSIS Sunda Ariana; Sulaiman Helmi; Malik Cahyadin; Deshinta Arrova Dewi; Mashal Kasem Alqudah
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 9 No. 1 (2025): Volume 9, Nomor 1, March 2025
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v9i1.35392

Abstract

Human Resource Management (HRM) is a strategic approach to managing people effectively in tourism companies, providing a competitive edge. This study aims to reveal research trends from 2020 to 2022 through a bibliometric and content analysis of HRM-related articles in the tourism industry. A total of 1,086 Scopus-indexed articles were analyzed using R Studio with the bibliometric package. Key metrics such as countries, authors, and institutions contributing to HRM research were examined. The findings show that the United States and China were the most productive countries in article output, with Wang and Zhang identified as the most prolific authors and Netreported as the leading institution. Emerging themes and keywords were also identified, indicating significant areas of focus in HRM research. The results highlight that HRM remains a trending topic in the tourism sector, driven by its role in enhancing organizational performance. This study is one of the few to provide a comprehensive bibliometric analysis of HRM in tourism, offering insights into global research productivity and trends over three years. The findings have practical implications for both academia and industry, suggesting that future research should focus on specific HRM practices that can further improve competitiveness in the tourism sector. These insights can guide tourism companies in refining HRM strategies to enhance performance and adaptability.
EARLY DETECTION OF ACADEMIC DEPRESSION USING SMARTPHONE-BASED MACHINE LEARNING MODELS Edi Surya Negara; Latius Hermawan; Hastari Mayrita; Desy Arisandy; Mohamad Farozi; Rahmat Ramadan; Sunda Ariana; Ria Andryani
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 9 No. 3 (2025): Volume 9, Nomor 3, September 2025
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v9i3.46375

Abstract

Mental health in developing countries is a common and complex problem. The problem continues to increase and is closely related to low self-confidence, negative interpersonal relationships, and academic depression. This can affect students' ability to complete academic assignments on a university scale. An AI-based early detection application can potentially improve mental health services related to treatment access. This system can help identify users who may be depressed based on the language used, especially for those who are reluctant to seek professional solutions due to the negative stigma of mental health. This study uses a qualitative descriptive method involving observation, in-depth analysis of group conversations, and early detection of academic depression by identifying conversation patterns between students and counselors as the basis for developing a smartphone-based application. This study produced a dataset of 395 depression-level data entries used as training data to develop a machine-learning model. A prototype of an academic depression detection application has been developed as a mobile-based application.
Co-Authors Ade Kemala Jaya, Ade Kemala Amalia , Rahayu Amalia, Rahayu Andri Andri Andri Pranata Andries Anwar Andryani, Ria Antonius Setyadi Bintang, Muhammad Yuan Corly, Fery Dedi Irawan Deshinta Arrova Dewi Desy Arisandy Dewi Sartika Edi Surya Negara Fero Triando Ferry Kurniawan Firda Firdaus Fitriasuri, Fitriasuri Hadi Syahputra Hastari Mayrita Kurniawan, Tri Basuki Latius Hermawan Malik Cahyadin Margareta Andriani Marina Marina Mashal Kasem Alqudah Mohamad Farozi Muhamad Ariandi Muhammad Chaikal Surya Nugraha Muji Gunarto Nina Paramithya Noer Fadzri Perdana Dinata Nyimas Sopiah Oktarina, Tri Perdana, Bosya Rahmat Ramadan Ria Andriani Rosa, Alkirma Rosmaidar Rosmaidar Saputra, Dhimas Rosanto Setyani Dwi Lestari Steffanus Jordy Suharno Pawirosumarto Sulaiman Helmi Sulaiman Sulaiman Sundari Soekotjo Suryanto Suryanto Syahril Rizal Tri Oktarina Trievanni, Trievanni Trisninawati Trisninawati, Trisninawati Yanti Pasmawati YAYAN SOFYAN Yunata Kandias Akbar Yuwono Yuwono