Intelligent Decision Support Systems for Maritime Human Resource Planning: Predictive Analytics for Seafarer Workforce Supply and Demand Forecasting

Authors

  • Nurindah Dwiyani Sekolah Tinggi Ilmu Pelayaran Jakarta
  • Mukhlas Hamdani Sekolah Tinggi Ilmu Pelayaran Jakarta
  • Derma Watty Sihombing Sekolah Tinggi Ilmu Pelayaran Jakarta
  • Rosna Yuherlina Siahaan Sekolah Tinggi Ilmu Pelayaran Jakarta
  • Nazilul Hamidi Sekolah Tinggi Ilmu Pelayaran Jakarta

DOI:

https://doi.org/10.55123/ijisit.v2i2.53

Keywords:

Decision Support Systems, Maritime Human Resources, Predictive Analytics, Seafarer Supply, Workforce Planning

Abstract

Intelligent decision support systems leveraging predictive analytics and artificial intelligence offer transformative capability for maritime human resource planning through accurate forecasting of seafarer workforce supply and demand dynamics, enabling proactive training capacity adjustment, recruitment strategy optimization, and policy intervention to address emerging labor market imbalances. This study investigates AI-powered workforce forecasting system implementation for Indonesian maritime education planning through convergent mixed-methods research combining quantitative predictive model development with qualitative stakeholder consultation. Historical workforce data spanning 2010-2023 were analyzed using ARIMA time series, Random Forest regression, and LSTM neural networks to forecast officer supply-demand dynamics. Focus Group Discussions with maritime education administrators (n=11), industry employers (n=14), and government workforce planning officials (n=8) explored forecast utilization barriers and integration mechanisms. Findings demonstrate that ensemble machine learning models achieve 86.2 percent accuracy at 3-year horizons and 80.4 percent accuracy at 5-year horizons in predicting seafarer workforce gaps, providing actionable intelligence for strategic planning. However, institutional inertia, data infrastructure inadequacy, and planning horizon misalignments constrain forecast adoption despite technical capability. The study proposes an Intelligent Maritime Workforce Planning Framework integrating predictive analytics platforms, unified maritime workforce data systems, multi-horizon forecast generation, and accountability structures for forecast-responsive decision-making.

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Published

2025-12-30

How to Cite

Nurindah Dwiyani, Mukhlas Hamdani, Derma Watty Sihombing, Rosna Yuherlina Siahaan, & Nazilul Hamidi. (2025). Intelligent Decision Support Systems for Maritime Human Resource Planning: Predictive Analytics for Seafarer Workforce Supply and Demand Forecasting. IJISIT: International Journal of Computer Science and Information Technology, 2(2), 144–152. https://doi.org/10.55123/ijisit.v2i2.53

Issue

Vol. 2 No. 2 (2025): December 2025

Section

Articles

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Copyright (c) 2026 Nurindah Dwiyani, Mukhlas Hamdani, Derma Watty Sihombing, Rosna Yuherlina Siahaan, Nazilul Hamidi

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