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Journal : jurnal teknik informatika dan sistem informasi

Perbandingan Performa Algoritma Minimax dan Breadth First Search Pada Permainan Tic-Tac-Toe Setiawan, Jerry; Famerdi, Farhan Agung; Udjulawa, Daniel; Yohannes, Yohannes
Jurnal Teknik Informatika dan Sistem Informasi Vol 4 No 1 (2018): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1077.102 KB)

Abstract

Tic-Tac-Toe is one of the board games that can hone the motor skills of the brain. This game uses 2 pawns, there are X and O. The game started with X’s pawn as the player who first turns, the game got win condition if the player or the enemy put the 3 pawns in a diagonal, vertical or horizontal line. While the game got draw if there is no player or enemy who put 3 pawns in a diagonal, vertical or horizontal line. The game’s problems are the player should think about the next best step to win and defend with put pawn to block enemy’s steps to win. To solve the problems, the game needs some algorithms, there are Minimax algorithm and Breadth First Search algorithm. Minimax algorithm explores node from deepest level and evaluates the scores using minimum or maximum value. Breadth First Search algorithm is an algorithm which explores node widely and compares evaluation scores to the deepest level. In this research, each algorithm is tested to response time and number of nodes needed on a game board with 3×3, 5×5, 7×7, and 9×9 size as much as 16 scenarios. Based on the test results, Breadth First Search algorithm is superior to Minimax on 3×3 board size in terms of response time and the number of nodes required. While the Minimax algorithm is superior to Breadth-First Search on 5×5 and 9×9 board size in terms of response time and the number of nodes required. In the first turn, the algorithm will trace the number of nodes larger than the next step so that the placement of the algorithm for the first turn affects the final result of the node number parameter.
Klasifikasi Lukisan Karya Van Gogh Menggunakan Convolutional Neural Network-Support Vector Machine Yohannes, Yohannes; Udjulawa, Daniel; Febbiola, Febbiola
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3399

Abstract

Painting is a work of art with various strokes, textures, and color gradations so that a painting that is synonymous with beauty is created. The various paintings created have characteristics, such as the paintings by Van Gogh, which have tightly arranged strokes, creating a repetitive and patterned impression. This study classifies paintings by Van Gogh or not by using the VGG-19 and ResNet-50 feature extraction methods. The SVM method is used as a classification method with two optimizations, namely random and grid optimization in the linear kernel. The data set used consisted of 124 Van Gogh paintings and 207 paintings by other painters. The use of VGG-19 feature extraction using grid optimization has the best value of 93,28% using the use of random optimization which has a value of 92,89%. The use of ResNet-50 using grid optimization with the best value of 90,28% using the use of random optimization which has a value of 90,15%. The extraction feature of VGG-19 is better than ResNet-50 in paintings by Van Gogh or not.