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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Applied Data Sciences
Solikhun Solikhun
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Enhancing Lung Cancer Prediction Accuracy UsingQuantum-Enhanced K-Medoids with Manhattan Distance Solikhun Solikhun; Lise Pujiastuti; Mochamad Wahyudi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4190

Abstract

Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection plays a crucialrole in improving treatment outcomes. This study proposes an enhancement of the K-Medoids clusteringmethod by integrating a quantum computing approach using Manhattan distance to improveprediction accuracy for lung cancer diagnosis. The research was conducted using a publicly availablelung cancer dataset consisting of 309 patient records with 14 diagnostic attributes. Comparative experimentswere carried out between the classical K-Medoids and the quantum-enhanced K-Medoids, withperformance evaluated based on clustering accuracy, precision, recall, and F1-score. The results showthat the quantum-based method has the same accuracy as the classical method, namely 88%. Thissuggests that quantum-based clustering can match the accuracy of classical methods after adequatetraining, although consistency and parameter stability remain areas for further refinement. Furtherresearch is recommended to test the model on larger datasets and to explore real-world deployment inclinical decision support systems.
Enhancing Autonomous Vehicle Navigation in Urban Traffic Using CNN-Based Deep Q-Networks Agus Perdana Windarto; Solikhun Solikhun; Anjar Wanto
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.896

Abstract

This research proposes a CNN-based Deep Q-Network (CNN-DQN) model to enhance the navigation capabilities of autonomous vehicles in complex urban environments. The model integrates CNN for spatial abstraction with reinforcement learning to enable end-to-end decision-making based on high-dimensional sensor data. The primary objective is to evaluate the impact of CNN-DQN state abstraction on the quality and stability of the resulting policy. Using a grid-based simulator, the agent is trained on a synthetic dataset representing urban traffic scenarios. The CNN-DQN model consistently outperforms standard DQN in multiple metrics: cumulative reward increased by 14.3%, loss convergence accelerated by 22%, and mean absolute error (MAE) reduced to 0.028. Furthermore, the model achieved a Pearson correlation coefficient of 0.94 in predicted actions and demonstrated superior robustness under Gaussian noise perturbation, with reward loss limited to 6.18% compared to 18.7% in the baseline. Visualizations of CNN feature maps reveal spatial attention patterns that support efficient path planning. The action symmetry index confirms that the CNN-DQN agent exhibits consistent left-right decision behavior, validating its policy regularity. The novelty of this study lies in its combined use of deep spatial encoding and value-based reinforcement learning for structured, rule-based environments with real-time control implications. These findings indicate that CNN-enhanced reinforcement learning architectures can significantly improve autonomous navigation performance and robustness in dynamic urban settings.
Co-Authors Agus Perdana Windarto Lise Pujiastuti Mochamad Wahyudi Wanto, Anjar