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All Journal International Journal Software Engineering and Computer Science (IJSECS)
Yesy Simanjuntak
State University of Medan
Computer Science & IT

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Risk Analysis of Autonomous Vehicle Accidents Using Bayesian Simulation with Statistical and Visual Data Yesy Simanjuntak; Rani Indah Sari; Peter Tymoty Hutabarat; Suvriadi Panggabean
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5781

Abstract

Autonomous vehicles (AVs) are an emerging innovation in intelligent transportation systems, yet traffic accidents remain a critical concern due to environmental uncertainty and sensor limitations. This study aims to analyze collision risk levels in autonomous vehicles using a Bayesian Convolutional Neural Network (Bayesian CNN) integrated with the Monte Carlo Dropout (MC Dropout) technique. The model was trained on 11,000 visual datasets from the Central Bureau of Statistics (BPS) and synthetic data representing diverse road conditions. The Bayesian inference framework enables dynamic and adaptive risk prediction by continuously updating posterior probabilities based on sensor input changes. Simulation experiments were conducted using a Python-based interactive interface (pygame) to visualize vehicle movements and real-time collision probabilities. Results show that 48% of test scenarios were classified as very low risk (0–10%), 28% as low (11–30%), 16% as medium (31–60%), and 8% as high (61–80%). The model achieved a reduction in loss value from 0.43 to 0.08 and maintained 76% of simulations within low and very low risk categories, confirming system stability and reliable convergence. The findings demonstrate that the Bayesian CNN model effectively captures uncertainty and provides adaptive, probabilistic predictions, supporting safer and more intelligent autonomous vehicle operations.
Risk Analysis of Autonomous Vehicle Accidents Using Bayesian Simulation with Statistical and Visual Data Yesy Simanjuntak; Rani Indah Sari; Peter Tymoty Hutabarat; Suvriadi Panggabean
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5781

Abstract

Autonomous vehicles (AVs) are an emerging innovation in intelligent transportation systems, yet traffic accidents remain a critical concern due to environmental uncertainty and sensor limitations. This study aims to analyze collision risk levels in autonomous vehicles using a Bayesian Convolutional Neural Network (Bayesian CNN) integrated with the Monte Carlo Dropout (MC Dropout) technique. The model was trained on 11,000 visual datasets from the Central Bureau of Statistics (BPS) and synthetic data representing diverse road conditions. The Bayesian inference framework enables dynamic and adaptive risk prediction by continuously updating posterior probabilities based on sensor input changes. Simulation experiments were conducted using a Python-based interactive interface (pygame) to visualize vehicle movements and real-time collision probabilities. Results show that 48% of test scenarios were classified as very low risk (0–10%), 28% as low (11–30%), 16% as medium (31–60%), and 8% as high (61–80%). The model achieved a reduction in loss value from 0.43 to 0.08 and maintained 76% of simulations within low and very low risk categories, confirming system stability and reliable convergence. The findings demonstrate that the Bayesian CNN model effectively captures uncertainty and provides adaptive, probabilistic predictions, supporting safer and more intelligent autonomous vehicle operations.
Dijkstra Algorithm-Based Shortest Path Optimization for Multi-Destination Tourism Routes in Samosir Regency: A Google Maps-Driven Case Study Yesy Simanjuntak; Fadillah Amanah; Suvriadi Panggabean
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7604

Abstract

Samosir Regency offers various natural and cultural tourism destinations distributed across several sub-districts. The distribution of these destinations and their road connections can make it difficult for visitors to determine efficient travel routes. This study aimed to determine the shortest route from Tano Ponggol to Hadabuan Nasogo Waterfall using a weighted graph approach and Dijkstra's algorithm based on distance data obtained from Google Maps. Nine tourist attractions were modeled as vertices and thirteen connecting routes as weighted edges representing travel distances for four-wheeled vehicles. Distance data were collected from Google Maps in April 2025 and processed using Dijkstra's algorithm. The results showed that the shortest route was A→B→D→E→H→I, with a total distance of 100.1 km and six of the nine attractions included in the route. Compared with the sequential baseline route that passes through all nine destinations (A→B→C→D→E→F→G→H→I), the shortest route reduced the travel distance by 31.6 km (24%) and the estimated travel time by 1 hour and 3 minutes (27%). However, the shorter route bypassed three attractions (C, F, and G), showing that a shortest-path approach does not ensure coverage of all tourism destinations. Dijkstra's algorithm is therefore suitable for determining the shortest route between a selected source and destination, while tourists who intend to visit multiple destinations may require a multi-stop optimization approach, such as the Traveling Salesman Problem. The findings provide a route recommendation for tourism travel in Samosir Regency and illustrate the need to select an optimization method according to the intended travel objective.
Co-Authors Fadillah Amanah Peter Tymoty Hutabarat Rani Indah Sari Suvriadi Panggabean