## 1 Introduction  
The epidemic of cardiovascular disease (CVD) has shifted from high-income countries to parts of the developing world. More than three-quarters of deaths from cardiovascular disease occur in low-income countries [1]. In the case of cardiovascular disease, correct diagnosis at an early stage is very important. Therefore, a fast and effective diagnostic technique must be given precisely and accurately. One of the pathological measures to diagnose cardiovascular disease is an electrocardiogram (ECG). ECG is the process of recording the electrical activity of the heart over a period of time. The ECG signal consists of P, QRS, T, and U components, known as features [2]. Many studies have been conducted related to ECG signal. The most popular one is to classify cardiovascular disease from ECG using artificial intelligence. One of approach is the utilization of machine learning algorithm. Most machine learning approaches involve three phases such as preprocessing, feature extraction, and feature normalization [3]. However, such approach needs a prior knowledge to determine the feature. In addition the extracted feature sometimes not suitable for different classification task in ECG. In recent years deep learning, part of machine learning, has gained popularity due to its high performance and effectiveness. Deep learning techniques outperform conventional machine learning techniques by extracting the necessary features from the raw data [4]. It has less processing and high accuracy compared to traditional methods. One of deep learning method that can process an ECG signal is Convolutional Neural Networks (CNN). This method have been used to classify heart rate, including detection of Myocardial Infarction (MI) disease [5]. MI refers to myocardial necrosis caused by acute and persistent ischemia and hypoxia in the coronary arteries [6]. MI is more common in the elderly, but more and more young people are suffering from MI due to high pressure, fast-paced and unhealthy lifestyles. The heart produces a change in potential with each beating cycle [7]. A fast, simple, non-invasive electrocardiogram is widely used in the diagnosis of MI. In a study conducted by [PERSON], et al. [8], the proposed 1D-CNN approach was used to detect AF disease with 13 layers and 10-fold cross validation. To remove noise, eight levels of DWT decomposition were used with Symlet5. In this study, researchers used four data sets, such as MIT-BIH Atrial Fibrillation, Physionet Atrial Fibrillation, MIT-BIH Malignant Ventricular Ectopy, and from hospitals in Indonesia. With 9 s of signal segmentation and a combination of DWT and 1D-CNN to detect/classify AF, it allows the model to automatically extract features from the ECG signal and helps improve final classification performance. Another study conducted by [PERSON] [9], proposed a CNN architecture to classify 10 MI classes on 12 leads ECG signals without feature extraction. The dataset used is the ECG Physiobank (PTB) dataset. The result of this research is that the highest classification performance is in lead v4 with an accuracy of 99.78%. However, this study only utilize 12 leads from 15 leads. The remain three leads (vx, vy and vz) are the frank leads which are needed to detect STEMI condition from ECG signal. Moreover, the proposed architecture in [8] are quite complex with 6 layers of convolutional layers, resulting a high computational cost. Research conducted by [PERSON] et al. [10] on the importance of 15 leads ECG signal versus 12 leads ECG signal in improving the diagnostic accuracy of Acute Myocardial Infarction (AMI). The purpose of this study was to examine the usefulness of the 15-lead ECG signal in treating chest pain patients, especially if the 12 lead ECG signal could not diagnose AMI. The results showed that the 12 lead ECG signal was diagnostic of ST-elevation AMI (STEMI) in 158 patients (Group A, 84.5%) who immediately underwent reperfusion therapy.