Iskandar, Muhammad Yashlan and Nugroho, Handoyo Widi (2025) Comparative Evaluation of Decision Tree and Random Forest for Lung Cancer Prediction Based on Computational Efficiency and Predictive Accuracy. Jurnal Teknik Informatika (JUTIF), 6 (5). pp. 3392-3404. ISSN 2723-3871
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Abstract
Early detection of lung cancer is essential for improving treatment outcomes and patient survival rates. This paper presents a comparative evaluation of two classification algorithms: Decision Tree and Random Forest, focusing on both predictive performance and computational efficiency. The models were tested using 10-fold cross-validation to ensure robustness. Both algorithms achieved the same accuracy of 93.3%. However, Random Forest slightly outperformed Decision Tree in recall (88.8% vs. 87.9%), F1-score (92.2% vs. 92.1%), and AUC (0.94 vs. 0.91), while Decision Tree obtained higher precision (97% vs. 95.9%). In terms of computational efficiency, Decision Tree demonstrated faster training and testing times, lower memory usage, and reduced energy consumption compared to Random Forest. The results reveal a clear trade-off between prediction quality and resource usage, highlighting the importance of selecting algorithms not only for their accuracy but also for their practicality in real-world healthcare scenarios. This comprehensive evaluation provides valuable insights for developing intelligent decision support systems that are both effective and resource-efficient, especially in environments with limited computing capacity. These findings contribute to the advancement of resource-aware intelligent systems in the field of medical informatics.
| Item Type: | Article |
|---|---|
| Subjects: | Ilmu Komputer eTheses |
| Divisions: | Pasca Sarjana > Magister Teknik Informatika |
| Depositing User: | Muhammad Yashlan Iskandar |
| Date Deposited: | 07 Nov 2025 06:10 |
| Last Modified: | 07 Nov 2025 06:10 |
| URI: | http://repo.darmajaya.ac.id/id/eprint/23061 |
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