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All Journal MIND (Multimedia Artificial Intelligent Networking Database) Journal International Journal Of Computer, Network Security and Information System (IJCONSIST)
Ariyono Setiawan
Politeknik Pelayaran Surabaya
Computer Science & IT

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Smart Shipping Route Optimization for Fuel Efficiency Using Big Data Analytics Ariyono Setiawan; Upik Widyansih; Abdul Razak Bin Abdul Hadi
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.129

Abstract

This research aims to optimize shipping routes by applying big data analytics to improve fuel efficiency. By leveraging real-time and historical data, the study identified the most efficient routes to minimize fuel consumption without sacrificing operational effectiveness. Based on maritime logistics theory, big data analytics, and fuel efficiency, this research combines route optimization models, weather forecasts, and ship performance analysis to support navigation decision-making. In addition, the impact of IMO MARPOL Annex VI regulations, especially EEDI and SEEMP, is also considered in efforts to optimize energy efficiency. The method used is a mixed approach, which combines quantitative analysis of AIS data, weather reports, and fuel consumption records with machine learning algorithms for route optimization. Pearson's correlation analysis evaluates the relationship between speed, distance, travel time, and fuel consumption. Case studies are used to validate the developed model. The results showed that fuel consumption was greatly affected by the speed of the ship, with higher speeds increasing fuel consumption. A negative correlation was found between travel time and daily fuel consumption, suggesting that slower cruising can improve efficiency. The study emphasizes the importance of real-time data processing in route adjustments based on weather, congestion, and energy efficiency. This research offers an innovative, data-driven approach to route planning, different from traditional methods that rely on static charts and experience. The integration of big data in maritime logistics can reduce emissions, reduce costs, and improve operational sustainability.
Prediksi Ekspor Jasa Transportasi Indonesia Menggunakan LSTM Berbasis Data Perdagangan Global Terbuka ARIYONO SETIAWAN; WISNU HANDOKO; ABDUL RAZAK BIN ABDUL HADI; CHOO WOU ONN
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 2 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i2.130-144

Abstract

ABSTRAKPenelitian ini memprediksi nilai ekspor jasa transportasi Indonesia menggunakan model Long Short-Term Memory (LSTM) berbasis data terbuka perdagangan global dalam mengatasi pola nonlinier dan ketergantungan temporal. Peneliti melatih model LSTM tiga lapis dengan aktivasi ReLU dan optimasi Adam menggunakan data ekspor tahunan (2005–2023) dari World Bank dan UNCTAD, dengan pembagian data latih-uji 80:20. Model mencapai MAPE 0,89% dan koefisien korelasi r = 0,999 (p < 0,0001), menunjukkan presisi tinggi. Model secara akurat menangkap gangguan akibat pandemi dan tren pemulihan, menawarkan alat prediksi berbasis AI untuk perencanaan ekspor dan kebijakan perdagangan. Ini merupakan studi pertama yang menerapkan LSTM pada ekspor jasa transportasi Indonesia dengan data terbuka, memberikan kontribusi metodologis dan praktis untuk negara berkembang.Kata kunci: kecerdasan buatan, peramalan ekspor, Indonesia, LSTM, layanan transportasiABSTRACTThis study forecasts Indonesia’s transport service export values using a Long Short-Term Memory (LSTM) model based on open global trade data in capturing nonlinear patterns and temporal dependencies. A three-layer LSTM model is trained using ReLU activation and Adam optimization on annual export data from 2005 to 2023 sourced from the World Bank and UNCTAD. The dataset is split into 80% training and 20% testing portions. The model achieves a MAPE of 0.89% and a correlation coefficient of r = 0.999 (p < 0.0001), indicating high precision.The model accurately reflects pandemic-induced shocks and subsequent recovery trends, provides an AI-driven forecasting tool for export planning and trade policy. This is the first study to apply LSTM to Indonesia’s transport service exports using open data, contributing methodological advancement and practical value for developing economies.Keywords: artificial intelligence, export forecasting, Indonesia, LSTM, transport services
Co-Authors ABDUL RAZAK BIN ABDUL HADI Abdul Razak Bin Abdul Hadi CHOO WOU ONN Upik Widyansih WISNU HANDOKO