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All Journal Journal of Information Systems and Informatics Journal Of Artificial Intelligence And Software Engineering
Setijohatmo, Urip Teguh
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Computer Science & IT

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Development of the Shortest Path Navigation Feature in a 360° Virtual Campus Tour Using Dijkstra's Algorithm Alifi, Muhammad Riza; Hodijah, Ade; Setijohatmo, Urip Teguh; Wulan, Sri Ratna; Hayati, Hashri
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6839

Abstract

A 360° virtual campus tour allows users to independently explore all available scenes in the form of 360° panoramic photos through a self-guided navigation feature. However, not all navigation tools provided are capable of generating route recommendations for users to follow. This presents a challenge, as users may feel overwhelmed when deciding where to begin and end the tour—particularly when the number of scenes reaches into the hundreds. In certain scenarios, prolonged interaction within a virtual reality environment may lead to discomfort due to motion sickness. Implementing a shortest path algorithm offers a potential solution by guiding users through recommended routes, thereby improving exploration efficiency and reducing interaction time. This study integrates a shortest path-based navigation feature into a virtual campus tour using Dijkstra’s algorithm, consisting of: (1) a front-end navigation component for the user interface of route searching, and (2) a back-end routing component that processes pathfinding using a graph-based structure. The implemented navigation feature demonstrates high efficiency, with an average execution time of only 4.94 ms and low memory consumption, as measured by a resident set size of 710.47 KB and used heap memory of 668.61 KB.
Enhancing News Similarity with Chunking Strategy and Hyperparameter Setting on Hybrid SBERT - Node2Vec Model Permadi Supriyo, Reza Ananta; Setijohatmo, Urip Teguh; Maspupah, Asri
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1180

Abstract

The proliferation of online news necessitates accurate article similarity systems to combat information overload, yet models based solely on semantic content often ignore crucial structural context like news source and publication date. This research proposes and evaluates a hybrid embedding model that integrates semantic representations from Sentence-BERT (SBERT) with structural representations from Node2Vec. A series of quantitative experiments were conducted on the challenging, multilingual SPICED dataset to determine the optimal model configuration. Using Mean Squared Error (MSE) for evaluation, the results show that a per-paragraph chunking strategy yielded the best performance. This strategy's effectiveness was validated by the identical performance of an optimal fixed-size chunk (450 characters with a 64 overlap), a value that aligns closely with the dataset's average paragraph length. Furthermore, a community-focused (BFS-like) Node2Vec configuration (p=1.0, q=2.0, l=60) was identified as optimal for the structural component. Significantly, the final hybrid model (MSE = 0.1434) proved superior to both the purely semantic (MSE = 0.1449) and purely structural models (MSE = 0.2512). This study concludes that the fusion of content and context provides the most comprehensive and accurate representation for news similarity detection.
Co-Authors Ade Hodijah Asri Maspupah Hayati, Hashri Muhammad Riza Alifi Permadi Supriyo, Reza Ananta Wulan, Sri Ratna