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Journal : JOIV : International Journal on Informatics Visualization

Multi-Document Summarization Using Tuna Swarm Optimization and Markov Clustering Widiartha, I Made; Hartati, Rukmi Sari; Wiharta, Dewa Made; Sastra, Nyoman Putra; Astuti, Luh Gede
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3365

Abstract

The Internet contains a large number of documents from various sources with similar content. The contents of documents that are almost identical will lead to news redundancy, making it difficult for readers to distinguish between factual information and opinions. Multi-document summarization has been designed to enable readers to easily understand the meaning of news documents without needing to read multiple documents. Multi-document summarization aims to extract information from several texts written about the same topic. The resulting summary report enables users to obtain a single piece of information from multiple similar pieces of information sourced from various locations. Various approaches have been used in creating multi-document summaries. Issues regarding accuracy and redundancy are still a significant focus of research. In this paper, a new multi-document summarization model was built using Tuna Swarm Optimization (TSO) and Markov Clustering (MCL) methods. The dataset of this research is Indonesian language news from various online media sources. Based on hyperparameter tuning using training data, the best TSO model performance was obtained at variable values a = 0.7, z = 0.9, and the optimal number of tuna fish > 80. From the research results, it was found that TSO outperformed other swarm intelligence methods. The use of MCL has proven to be effective, as evidenced by the performance results, where TSO achieved an average ROUGE value 7.95% higher when MCL was applied. In this performance test, four standard evaluation metrics of the ROUGE toolkit were used.
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.
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