Salak is a seasonal fruit that has high export value. The success of salak fruit exported is influence by selection process, but there is still a problem in it. The selection of salak still done manually and potentially misclassified. Research to automate the selection of salak fruit has been done before. The process of selection this salak fruits used convolutional neural network (CNN) based on image of salak fruits. The resulting of accuracy value from previous research is 70.7% for four class classification model and 81.45% for two class classification model. This research was conducted to increase accuracy value the classification of salak exported based on previous research. Accuracy improvement by changing the noise removal process to produce a better image. The changing also occur in the CNN architecture that layer convolution is more deep and with additional parameters such as Stride, Zero Padding, and Adam Optimizer. This change hopefully can increase the accuracy value of the salak classification. The results showed an accuracy value increased 22.72% from 70.70% to 93.42% for the category of four classes CNN models and increased 13,29% from 81.45% to 94.74% for category two classes.

Traffic is a medium to move from one point to another. Therefore, the role of traffic is very important to support vehicle mobility. If congestion occurs, mobility will be hampered so that it gives influence to other sectors such as financial, air pollution and traffic violations. This study aims to create a model to predict vehicle queue at the traffic lights when its status is red. The prediction is conducted by using Neural Network with Extreme Learning Machine method to predict the length of the vehicle queue, and Correlation Analysis was used to measure the correlation between the connected roads. The conducted experiments use data of the length of the vehicle queue at the traffic lights which was obtained from DISHUB (Transportation Bureau) DI Yogyakarta. Several experiments were carried out to determine the optimum prediction model of vehicle queue length. The experiments found that the optimum model had an average MAPE value of 15.5882% and an average Running Time of 5.2226 seconds.

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This paper presents an acne detection method on face images using a Gaussian Mixture Model (GMM). First, the skin area in the face image is segmented based on color information using the GMM. Second, the candidates of the acne region are then extracted using a Laplacian of Gaussian-based blob detection strategy. Then, texture features are extracted from acne candidates using either a Gabor Filter or Gray Level Cooccurrence Matrix (GLCM). Lastly, these features are then utilized as input in the GMM for verifying whether these regions are acne or not. In our experiment, the proposed method was evaluated using face images from ACNE04 dataset. Based on the experiment, it is found that the best classification results were obtained when GLCM features in the Cr-YCbCr channel are applied. In addition, the proposed method has competitive performance compared to K-Nearest Neighbor (KNN).

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