DOI: 10.15849/IJASCA.211128.08 Int. J. Advance Soft Compu. Appl, Vol. 13, No. 3, November 2021 Print ISSN: 2710-1274, Online ISSN: 2074-8523 Copyright © Al-Zaytoonah University of Jordan (ZUJ) Artificial Intelligence Approach in Multiclass Diabetic Retinopathy Detection Using Convolutional Neural Network and Attention Mechanism Amnia Salma 1 , Alhadi Bustamam 1 *, Anggun Rama Yudantha 2 , Andi Arus Victor 2 , and Wibowo Mangunwardoyo 3 1 Department of Mathematics, Faculty of Mathematics and Natural Science, Indonesia University, Depok, West Java, 16424, Indonesia e-mail: amniasalma@ssci.ui.ac.id *Corresponding author, e-mail: alhadi@ssci.ui.ac.id 2 Department of Opthalmology, Faculty of Medicine, Indonesia University, Cipto Mangunkusumo National Hospital, Jakarta, Indonesia 3 Department of Biology, Faculty of Mathematics and Natural Science, Indonesia University, Depok, West Java, 16424, Indonesia Abstract The number of people around the world who have diabetes is about 422 million. Diabetes seriously affects the blood vessels in the retina, a disease called diabetic retinopathy (DR). The ophthalmologist examines signs through fundus images, such microaneurysm, exudates and neovascularisation and determines the suitable treatment for patient based on the condition. Currently, doctors require a long time and professional skills to detect DR. This study aimed to implement artificial intelligence (AI) to resolve the lack of current methods. This study implemented AI for detecting and classifying DR. AI uses deep learning, such the attention mechanism algorithm and AlexNet architecture. The attention mechanism algorithm focuses on detecting the pathological area in the fundus images, and AlexNet is used to classify DR into five levels based on the pathological area. This study also compared AlexNet architecture with and without attention mechanism. We obtained 344 fundus images from the Kaggle dataset, which contains normal, mild, moderate, severe and proliferative DR. The