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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JOIV : International Journal on Informatics Visualization WIDYABHAKTI Jurnal Ilmiah Populer
Sri Andriati Asri, Sri Andriati
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

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Ship Trajectory Extraction Using Python Vessel Tracking Interpolation Method Setiawan, Widyadi; Linawati, Linawati; Widyantara, I Made Oka; Wiharta, Dewa Made; Asri, Sri Andriati
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9567

Abstract

A deep understanding of ship trajectory movements has essential implications for maritime applications, including navigation, monitoring, and marine traffic analysis. Efficient and accurate extraction techniques are necessary to extract valuable information from ship trajectory data. One commonly used approach is the interpolation method, which allows the reconstruction of smooth trajectories from recorded data points. This research is focused on analyzing the extraction of ship trajectories using the interpolation method provided by the Python Vessel Tracking (PyVT) library. This method allows interpolation of ship trajectory data based on various algorithms available in the library. This research aims to evaluate the effectiveness and accuracy of interpolation methods from PyVT in reconstructing ship trajectories from incomplete or disturbed data. Within this research's framework, several test scenarios were implemented to examine different types of ship trajectory data, including data with missing points and speed variations. Evaluation metrics RMSE (root mean Squared Error), which includes reconstruction accuracy, will be utilized from the interpolation algorithm in PyVT.
Penerapan Sistem Informasi untuk Optimalisasi Pengelolaan UMKM dalam Meningkatkan Potensi Lokal Desa Sibetan Sudiartha, I Ketut Gede; Asri, Sri Andriati; Ambara, Made Pradnyana; Sentana, I Wayan Budi
WIDYABHAKTI Jurnal Ilmiah Populer Vol. 7 No. 1 (2024): Nopember
Publisher : Direktorat Penelitian, Pengabdian Masyarakat, dan HKI Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/widyabhakti.v7i1.795

Abstract

Potensi ekonomi lokal yang signifikan melalui Usaha Mikro, Kecil, dan Menengah (UMKM) yang berfokus pada produk-produk berbasis kearifan lokal dimiliki Desa Sibetan.  Berbagai jenis UMKM yang bergerak di sektor agroindustri khususnya produk berbasis salak, kerajinan tangan, serta pariwisata. Sebelumnya pendataan dan pengelolaan informasi di  desa Sibetan kepada masyarakat maupun pelaku UMKM masih bersifat konvensional, dimana informasi kepada anggota dilakukan secara konvensional dan juga pengelola UMKM harus dikontak secara konvensional jika ada permintaan informasi dari unit terkait mengenai keberadaan dan perkembangan produk UMKM. Begitu pula dalam pemasaran produk maupun layanan wisata yang disediakan di Desa Sibetan.Pelaksanaan pengabdian kepada masyarakat di Desa Sibetan, diharapkan peningkatan potensi  desa Sibetan dalam pengelolaan informasi UMKM menjadi lebih baik, serta dapat mendukung pengembangan potensi dan kemajuan desa. Pengabdian kepada masyarakat juga sudah memberikan pelatihan penggunaan sistem kepada pelaku UMKM. Dari hasil kuesioner terhadap pengabdian kepada masyarakat yang sudah dilakukan baik dari perencanaan, pelaksanaan dan evaluasi responden menyatakan 52% sangat puas dan 48%  responden menyatakan puas terhadap sistem informasi pengelolaan UMKM yang sudah dikembangkan. Dari pelaksanaan kegiatan pengabdian dapat dilihat adanya peningkatan Animo Warga dalam keikutsertaan meningkatkan potensi Desa, adanya penataan pembinaan UMKM, peningkatan pemasaran produk maupun sarana wisata yang dikembangkan.
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 Dewa Made Wiharta I Made Oka Widyantara I Wayan Budi Sentana, I Wayan Budi Linawati Linawati Made Pradnyana Ambara, Made Pradnyana Pawana, I Wayan Adi Juliawan Sudiartha, I Ketut Gede Widyadi Setiawan