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The research focused on the problems of buying a second-hand Toyota vehicle. The ability to make fast, precise and accurate decisions will be the key to success in today’s global competition. The number of factors that need to be considered when a purchaser wants to buy a second-hand car also leads the purchaser to make the wrong choice when selecting a second-hand car to buy. This problem is solved by developing a support system that helps consumers make choices about purchasing second-hand cars. This Decision Support System is generated using the Simple Additive Weighting (SAW) method. From this research it can be concluded that the SAW method is very appropriate to use as a decision support tool where three best second-hand car alternatives are obtained consisting of Toyota Fortuner with a preference value of 101, Toyota Rush car with a preference value of 90.4, and the Toyota Kijang Innova with a preference value of 88.6.

Mackerel is an important commercial caught fish for local fishermen, including Rastrelliger kanagurta and Rastrelliger brachysoma. However, to distinguish these two species is rather difficult because of their similar appearance. Convolutional Neural Network (CNN) is a deep learning method that can be used to classify images. One of parameters contributing to the level of accuracy is the layers number applied in CNN architecture. This study aims to classify those two species using CNN with a range of two to five convolutional layers architectures i.e CNN1, CNN2, CNN3 and CNN4, respectively. In this study, 434 images were used as a training group with 217 images for each class. The validation group consisted of 21 images for each class and the test group consisted of 19 images. The results showed that the CNN3 provided the best training and validation accuracy of respectively 100% and 92.6%. The lowest value of training loss and validation loss of 0.000057 and 0.49. The accuracy values of the CNN models using different testing images reached 94.7%.

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