Alzheimer’s disease is a type of brain disease that indicate with memory impairment as the early symptoms. These symptoms occur because the nerve in the brain involved in learning, thinking and memory as cognitive function have been damaged. Alzheimer is one of diseases as the leading cause of death and cannot be cured, but the proper medical treatment can delay the severity of the disease. This study proposes the Convolutional Neural Network (CNN) using AlexNet architecture as a method to develop automated classification system of Alzheimer’s disease. The experiment is conducted using Magnetic Resonance Imaging (MRI) datasets to classify Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented from 664 MRI datasets. From the experiment, this study achieved 95% of accuracy. The automated Alzheimer’s disease classification can be helpful as assisting tool for medical personnel to diagnose the stage of Alzheimer’s disease so that the appropriate medical treatment can be provided.

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ABSTRAKPenyakit jantung merupakan salah satu penyebab utama kematian di dunia. Salah satu penyakit jantung yang perlu diperhatikan adalah congestive heart failure (CHF). CHF adalah suatu kondisi di mana jantung tidak mampu memompa darah ke seluruh tubuh. Penyakit ini dapat didiagnosis dengan EKG. Oleh karena itu, pada penelitian ini dibuat sebuah sistem yang dapat mengidentifikasi penyakit CHF secara otomatis menggunakan metode convolutional neural network (CNN) dengan 4 hidden layer dan 16 output channel, fully connected layer, dan aktivasi Softmax. Data yang digunakan dalam penelitian ini diambil dari MITBIH dan BIDMC. Penlitian ini memberikan akurasi 100%, sehingga deteksi penyakit CHF otomatis membantu staf medis mendiagnosis pasien untuk menerima perawatan yang tepat.Kata kunci: Elektrokardiogram (EKG), Convolutional Neural Network (CNN), Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF)ABSTRACTHeart disease is one of the leading causes of death in the world. One of the heart diseases that need to be considered is congestive heart failure (CHF). CHF is a condition in which the heart is unable to pump blood throughout the body. ECG can diagnose this disease. Therefore, this study created a system that can automatically identify CHF disease using the convolutional neural network (CNN) method with four hidden layers and 16 output channels, a fully connected layer, and Softmax activation. The data used in this study were taken from MIT-BIH and BIDMC. In this study provides 100% accuracy. Automated CHF disease detection helps medical staff diagnose patients to receive appropriate treatment.Keywords: Electrocardiogram (ECG), Convolutional Neural Network (CNN), Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF)

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