<em>Travelling Salesman Problem</em><span> (TSP) masih menjadi topik menarik untuk dibahas. TSP termasuk bagian dari permasalahan optimasi di dunia nyata. Pada TSP ini terdapat n buah kota yang harus dilalui oleh seorang </span><em>salesman</em><span>, kemudian kembali ke kota dimana pertama kali dia berangkat. Dalam perjalanannya tersebut, seorang </span><em>salesman</em><span> harus memilih rute yang terpendek. Ada banyak algoritma untuk memecahkan masalah TSP. Dan diantara sekian banyak algoritma, pada penelitian ini akan dibahas mengenai bagaimana implementasi algoritma </span><em>greedy</em><span>, </span><em>Artificial Bee Colony </em><span>(ABC), </span><em>Cheapest</em><span> </span><em>Insertion Heuristics </em><span>(CIH), </span><em> </em><span>dan algoritma genetika untuk menyelesaikan kasus TSP. Analisis yang dilakukan adalah perbandingan metode, implementasinya terhadap kasus TSP, serta kelebihan dan kekurangan masing-masing algoritma</span>

With the most recent advances in technology, computer programming has reached the capabilities of human brain to decide things for almost all healthcare systems. The implementation of Convolutional Neural Network (CNN) and Extreme Gradient Boosting (XGBoost) is expected to improve the accurateness of breast cancer detection. The aims of this research were to; i) determine the stages of CNN-XGBoost integration in diagnosis of breast cancer and ii) calculate the accuracy of the CNN-XGBoost integration in breast cancer detection. By combining transfer learning and data augmentation, CNN with XGBoost as a classifier was used. After acquiring accuracy results through transfer learning, this reasearch connects the final layer to the XGBoost classifier. Furthermore, the interface design for the evaluation process was established using the Python programming language and the Django platform. The results: i) the stages of CNN-XGBoost integration on histopathology images for breast cancer detection were discovered. ii) Achieved a higher level of accuracy as a result of the CNN-XGBoost integration for breast cancer detection. In conclusion, breast cancer detection was revealed through the integration of CNN-XGBoost through histopathological images. The combination of CNN and XGBoost can enhance the accuracy of breast cancer detection.

Computer programs can work by imitating the human brain to make decisions that can be used in the health sector. One of them is the Convolutional Neural Network (CNN) which is combined with XGBoost as the classifier. CNN-XGBoost can be implemented for the accuracy of early detection of breast cancer. The problem is how to improve the accuracy of breast cancer detection on mammogram images. The stages of the research method: (1) Collecting the MIAS 2012 dataset, (2) Dividing data into training data and testing data. (3) preprocessing: cropping, resizing, and reshaping. (4) Data Augmentation (5) Transfer Learning (6) Classification using CNN-XGBoost (7) Testing the accuracy. Based on the research that has been carried out, the results are: (1) Obtained data on the accuracy of using CNN-XGBoost on mammogram image analysis in early detection of breast cancer. (2) Further testing is needed to improve accuracy. Further testing is needed with the use of other method or by improving the quality of mammogram images.

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Backpropagation is a supervised learning method on artificial neural networks. The process of training artificial neural networks that are built will be tested several structures of artificial neural networks, with the hope that the best structure is obtained to produce the output with the smallest error. The evaluation purposes use the same data set to form a neural model with a hidden level and the neuron model with two hidden levels then compare these three models both in terms of classification accuracy and training time. A neural network can be concluded that for each architecture, the neural network architecture used to recognize type 3 is 82,42%, type 2 77,5%, type 1 98,1%.