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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Buletin Udayana Mengabdi Jurnal SPEKTRUM Majalah Ilmiah Teknologi Elektro Jurnal Pendidikan Teknologi dan Kejuruan Jurnal Sains dan Teknologi PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate: Jurnal Ilmiah Indonesia SINTECH (Science and Information Technology) Journal Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Teknologi Informasi dan Pendidikan Journal of Electrical, Electronics and Informatics International Journal of Engineering and Emerging Technology Jurnal Ekonomi Manajemen Sistem Informasi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Industri Terintegrasi (JUTIN) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Teknik Informatika (JUTIF) Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) Majalah Ilmiah Teknologi Elektro Jurnal Teknik Elektro
Dewa Made Wiharta
Electrical Engineering Universitas Udayana
Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Other

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Ship Trajectory Prediction Based on Spatial-temporal Data Using Long Short-Term Memory Setiawan, Widyadi; Linawati, Linawati; Widyantara, I Made Oka; Wiharta, Dewa Made; Asri, Sri Andriati; Pawana, I Wayan Adi Juliawan
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3353

Abstract

The frequent exploitation of shipping lines by passengers increased traffic and exposed it to more significant dangers. Precise predictions for ship trajectory conditions at sea must be available to ensure safe navigation across the oceans. This article presents a trajectory prediction approach based on Long Short-Term Memory (LSTM) neural networks applied to time series Automatic Identification System (AIS) position data, expressed in spatial-temporal form. LSTM is highly suitable for ship trajectory predictions as it can capture long-term dependencies and spatial-temporal patterns existing in AIS data, since LSTM is targeted toward sequential data. The proposed model extracts ship trajectories from AIS data and utilizes an LSTM (Long Short-Term Memory) model to predict future ship movements based on historical patterns. The experiments demonstrate that it is effective in predicting where ships to navigate next, providing a valuable tool for enhancing traffic flow and improving navigation safety. The model with LSTM unit 500, tested on 3,478 ship trajectories, showed a median RMSE prediction error ranging from 0.0720 to 0.0841, with prediction M=8 coordinate a head having the highest error (0.0841) and M=2 and M=9 having the lowest (0.0720); the interquartile range (IQR) spanned from 0.0571 to 0.1006, and M=2 had the most outliers (302) while M=8 had the fewest (171), indicating varying prediction stability across different points. Despite these results, challenges remain in maintaining prediction stability across all points. Further optimization could enhance the model's performance and address these limitations by incorporating more complex spatial-temporal features or hybrid techniques.
Adaptation in IoT-Fog Data Transmission: SLR and Future Perspectives on Dynamic Frequency Control Firmansyah Maulana Sugiartana Nursuwars; Linawati; Nyoman Putra Sastra; Dewa Made Wiharta; Aceng Sambas
Jurnal Teknik Elektro Vol. 17 No. 1 (2025): Jurnal Teknik Elektro
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v17i1.30564

Abstract

The advancement of Internet of Things (IoT) and fog computing technologies has created significant opportunities for more efficient, faster, and proximity-based data management. However, IoT-Fog systems face considerable challenges related to device heterogeneity, traffic dynamics, and the complexity of network topologies that continuously change. This study conducts a Systematic Literature Review (SLR) of various research works covering dynamic scheduling, routing, context-aware data flow, offloading, and IoT-Fog systems without adaptive mechanisms. The findings indicate that most existing approaches still rely on relatively static topology assumptions, rendering them insufficiently adaptive to real-time changes in network conditions. One area identified as a research gap is dynamic frequency control, an adaptive mechanism capable of dynamically adjusting data transmission intensity based on network conditions. The main conclusion of this study emphasizes the necessity for developing adaptive systems that are topology-agnostic and supported by dynamic frequency control to maintain optimal performance even under significant topology changes. Such systems are anticipated to become a crucial foundation for future IoT-Fog applications, including smart cities, Industry 4.0, and intelligent healthcare services, which demand high reliability and low latency.
Co-Authors Aceng Sambas Adinda Hermawan, Salsabila Aggry Saputra Agus Permana Putra Agus Riki Gunawan Agus Supranartha Anak Agung Bagus Rama Windhu Putra Anak Agung Kompiang Oka Sudana Anggreni, Ni Komang Ayu Sri Anjeli Sitanggang, Feybe Anugrah Br. Ginting, Putri Arda Narendra, I Gusti Lanang Ari Wijaya I Kadek Asana, I Made Dwi Putra Bayu Bimantara Putra Bhaskara, I Made Adi Binti Dona, Sufiana Christanto Nadeak, Yobel Dd Hassel Putra Q Diafari Djuni H, I G A K Diafari, G A K dian krisnandari Doni Helmahera Duman Care Khrisne Eginna Gresia Br Purba Estry Nurya Savitri firmansyah maualana sugiartana nursuwars Frederik Nixon Gamantyo Hendrantoro Gede Sukadarmika Hendri Hendri I Gede Primanata I Gede Sudiantara I Gede Wira Darma I Gusti Ayu Garnita Darma Putri I Gusti Ayu Garnita Darmaputri I Gusti Ngurah Agung Jaya Sasmita I Kadek Dwi Gandika Supartha I Komang Leo Puja Artana I Made Adi Bhaskara I Made Arsa Suyadnya I Made Artana I Made Kris Widiantara I Made Oka Widyantara I Made Sastra Dwikiarta I Made Suartika I Made Widiartha I Nyoman Putra Maharddhika I Putu Ardana I Wayan Adi Juliawan Pawana I Wayan Krisna Saputra Ida Ayu Dwi Giriantari Ida Ayu Kaniya Pradnya Paramitha Ida Bagus Vidananda Agastya IGN. Agung Dwi Jaya Putra Ilham Ammarul Aziz Jayantari, Made Widya Kadek Teguh Purwanto Komang Budiarta Komang Oka Saputra Komang Sri Utami Komang Tania Paramecwari Lely Meilina Lie Jasa Linawati Linawati Linawati Luh Gede Astuti Made Sudarma Made Sudarma Mahardhika Tirta Naradhiya, Gede Naufal Muhajir Abidin Ngurah Indra ER Ni Kadek Diah Parwati Ni Komang Ayu Sri Anggreni Ni Made Ary Esta Dewi Wirastuti Ni Putu Diah Arista Ningsih Nicko Satrio Pambudi Nyoman Arun Wiratama Nyoman Putra Sastra Pawana P., I Gusti Ngurah Agung Pawana, I Wayan Adi Juliawan Putra, A.A.B. Rama Windhu Putra, Agus Permana Putra, Rio Juniyantara Putu Andhika Kurniawijaya Putu Ardana Putu Arya Mertasana Putu Dhiko Pradnyana Putu Krisna Adi, I Gusti Ngurah Putu Wirya Kastawan Rahadi, Putu Suta Adya Dharma Rio Juniyantara Putra Rukmi Sari Hartati Rukmi Sari Hartati Saputra, Komang Oka Sari Dewi Hartanto, Dessy Ratna Setiawan, Putu Ayu Citra Solly Aryza Sri Andriati Asri, Sri Andriati Widiadnyana, Putu Widyadi Setiawan Wikananda, I Gusti Ngurah Satya Wirawan Wirawan Yohanes Hendra Nugroho Yohanes Pracoyo Widi Prasetyo