ANT NESTING OPTIMIZATION UNTUK PENINGKATAN AKURASI CNN DALAM DIAGNOSTIK BRAIN TUMOR - Florentina Yuni Arini; Aloysius Oktavian; Nia Nur Hidayaturrohmah; Daffa Pramata Aryaputra; Alul Hidja Syanjalih; Mohammad Farrel Aldevis; Muhammad Zidan Aisar (SKANIKA: Sistem Komputer dan Teknik Informatika, 2026)
This study discusses the application of a new optimization algorithm, namely Ant Nesting Optimization (ANO), to improve the performance of Convolutional Neural Networks (CNN) in brain tumor classification based on MRI images. ANO is inspired by the behavior of Leptothorax ants in selecting optimal nest locations, which is applied in the model's exploration and exploitation processes. The optimized CNN model shows an increase in classification accuracy of up to 97%, with superior performance in detecting various types of brain tumors. The evaluation results show that the proposed model has faster and more stable loss convergence compared to the standard model. This optimization method not only improves classification precision but also accelerates model stabilization during the training process. With these results, the research proves the effectiveness of ANO as an optimization method in deep learning networks and opens up wider application opportunities in the field of artificial intelligence-based diagnostics.