Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Jurnal Pustaka Indonesia Vifada Management and Social Sciences
Sinung Suakanto
Telkom University, Bandung, Indonesia
Humanities Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Physics Social Sciences Other

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Developing a Personality-Aware Agentic AI Framework for ‎Academic and Career Recommendation in Higher Education: ‎A Systematic Literature Review Friska Andalusia; Sinung Suakanto; Sang Dara Parameswari
JPI: Jurnal Pustaka Indonesia Vol. 6 No. 1 (2026): January-April
Publisher : Yayasan Darussalam Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62159/jpi.v6i1.2084

Abstract

Artificial intelligence-based academic advising systems are increasingly used in higher education to support course selection, academic planning, and career guidance. However, existing recommender systems often prioritize academic records, course histories, and behavioural data, while students’ psychological characteristics, particularly personality traits, remain insufficiently integrated into recommendation logic. This study aims to examine how personality traits can support personalized academic and career guidance and to propose a personality-aware agentic AI framework for higher education. Using a systematic literature review guided by PRISMA 2020, this study searched Scopus-indexed publications related to personality traits, artificial intelligence, recommender systems, academic advising, and career guidance. From 199 initial records, 45 studies were screened, 27 reports were assessed for eligibility, and 21 studies were included in the qualitative synthesis. Data were analysed through thematic synthesis and organized into five evidence clusters: personality and career development, AI-based academic advising, agentic AI architecture, cross-domain personality-aware recommender systems, and ethics and explainability. The findings reveal three major gaps: personality traits are mostly used as explanatory rather than operational variables; AI-based advising systems remain dominated by performance-driven data; and integrated frameworks combining psychological modelling, agentic reasoning, and recommendation delivery are still limited. In response, this study proposes a conceptual personality-aware agentic AI framework consisting of personality modelling, psychological profiling, agentic AI processing, intelligent recommendation generation, and decision-support interfaces. Although the framework has not yet been empirically validated, it offers a structured foundation for future prototype development, ethical implementation, and human-centred academic advising in higher education.
IT Governance Evaluation of Hospital Information Systems Using Framework: Case Study of RSUD Welas Asih Muhammad Hanif Zahran; Sinung Suakanto; Basuki Rahmad
Vifada Management and Social Sciences Vol. 4 No. 1 (2026): January - June
Publisher : Yayasan Vifada Cendikia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70184/jkcdbz70

Abstract

Purpose: This study evaluates the governance capability of the Hospital Management Information System (Sistem Informasi Manajemen Rumah Sakit/SIMRS) at RSUD Welas Asih using COBIT 2019 and formulates improvement directions to strengthen service reliability. Research Design and Methodology: This study applies an evaluative case study with a mixed-method approach. Data were collected through a structured questionnaire completed by ten respondents from the SIMRS Installation, an interview with the Head of SIMRS Installation, direct observation, and limited document analysis. COBIT 2019 was used to assess selected APO, BAI, and DSS processes, while triangulation was applied to interpret questionnaire results against interview and documentary evidence. Findings and Discussion: The questionnaire results show generally positive perceptions of SIMRS governance, especially in strategic alignment, user-driven development, and operational support. However, triangulation identifies improvement needs in periodic training, user adaptation after feature changes, change documentation, external system dependency, incident handling, manual fallback, data re-entry, BPJS claim validation, and infrastructure resilience. Implications: The study recommends strengthening competency development, formal change logs, incident escalation, fallback procedures, data reconciliation, infrastructure continuity, and continuous governance monitoring.  
Co-Authors Basuki Rahmad Friska Andalusia Muhammad Hanif Zahran Sang Dara Parameswari