Malaria is contagious and transmitted through Anopheles mosquito. This disease caused about 435,000 people died in 2017. Therefore, it is considered as a dangerous disease. This research aims to implement Siamese Convolutional Neural Network (SCNN) to identify malaria using a collection ofhuman cell images. SCNN is an architecture that uses two convolutional neural networks with the same configurations. Input data for this network are two paired images, where the first is the reference image and the second one is a test image and will produce a similarity score consisting of the numbers 0-1.The data uses in this research is consisted of two classes, namely, parasitized and uninfected. Based on trials that have been carried out, we decide to create SCNNmodel whichis utilizing a pre-trained ImageNet VGG16and a fully connected network with a single hidden layer. Hyperparameter and parameter used are fixed and not all hyperparameter are used. Testing is done by changing the number of reference images and the number of training images. The best accuracy result obtained by using siamese convolutional neural network model is 94.35%.