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All Journal JOISIE (Journal Of Information Systems And Informatics Engineering)
Fajri Arvandi
Universitas Nurdin Hamzah
Computer Science & IT

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TALENT QUALITY MONITORING EXECUTIVE INFORMATION SYSTEM PT. SURVEYOR INDONESIAN AT JAMBI REGION WITH GENERATIVE AI INTEGRATION Fajri Arvandi; Sukma Puspitorini; Ahmad Husna Ahadi
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol. 10 No. 1 (2026)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v10i1.6072

Abstract

Surveyor Indonesia for the Jambi Province Region face obstacles in monitoring the performance and quality of talent because data management is still carried out manually using Microsoft Excel. This research aims to develop the Talent Monitoring Executive Information System (TARING-EIS) application to facilitate the monitoring and evaluation of talent competencies by the human resource (HR) department and company executives. The approach used is a case study with descriptive analysis through observation, interviews, and documentation studies. Talent competencies are assessed based on five main aspects, namely morals, leadership, experience, hard skills, and soft skills. System input data includes user data (talent and executive), internal event data, certification data, and individual ability data. The main processes in the system include talent data management, event management and certification. The system output is in the form of an interactive dashboard that displays event information, attendance, leaderboard points, and various employee performance visualizations. The system was developed using Svelte, Hono.js, and PostgreSQL, and integrates Generative AI as an internal chatbot that helps provide information related to the company and employee self-development. The results of the implementation of 20 respondents showed a user satisfaction level of 89% (4.47/5.00) based on the End User Computing Satisfaction (EUCS) test, indicating that TARING-EIS is effective and efficient in supporting the digitalization of talent management.
Co-Authors Ahmad Husna Ahadi Sukma Puspitorini