Breast cancer is the most common cancer in women. Breast cancer is also the deadliest cancer in women. In 2012, there were 19,750 breast cancer patients in Indonesia dead. There is no sure way to prevent breast cancer. However, the recovery rates and survival rates can be improved by early detection through routine examination. Using X-ray radiation, breast tissue can be obtained. This image is called mammogram. In this paper, a combination of Gray-level co-occurrence matrix (GLCM) and Support Vector Machine (SVM) is used to classify benign-malignant patient based on mammography image. This paper aims to find the optimal GLCM angle for breast cancer classification cases using mammogram data. Mammogram images used in this paper provided by Curated Breast Imaging Subset of Digital Database Screening Mammography (CBIS-DDSM) dataset. From experimental result, obtained accuracy 63.03% and specificity 89.01%.

Recognition process and classification of food through image processing technology has been developed especially to know the nutrients or information contained in the food. This certainly can be useful for tourists who are in Indonesia, which is sometimes the tourists who are not accustomed to Indonesian food need information about diverse types of food. Considering some foods in Indonesia have similarities and almost resemble each other. So in this study, we utilize the method of Convolution Neural Network which proved quite reliable and fast in the process of classification of a complex and detail object, to get information about Indonesian food for tourists. By using CNN method, the process of classification can run accurately, as well as information about food in the form of names or ingredients of food can be obtained appropriately too. Evidenced by the accuracy of the classification reached 70%, which is this approach will be expected to be applied in the mobile-based system and serve as an easy alternative way to obtain information about Indonesian food.

The victory prediction of DotA 2 game is an interesting thing to know as it is one of the popular online games and is often played by players around the world. The winning opportunity of a team in this game is largely determined by the combination of heroes chosen so that a proper selection of heroes can give the team a victory. A classification is one of many ways used to make prediction. In this study, the classification will be divided into 2 classes, namely win and lose. One popular classification method is the Support Vector Machine (SVM). SVM method is suitable for classification based on 2 classes. However, in the SVM method, it is necessary to determine the optimum parameters to obtain good accuracy results, so that this research will use one of the optimization methods of Particle Swarm Optimization (PSO) for optimization of SVM parameters to increase the accuracy value. In this study, the SVM parameter to be optimized is C Parameter on linear kernel and it is proven that Optimization of C Parameter on SVM using PSO can increase SVM accuracy from 0.53866 to 0.600349718.

Sentiment analysis from social media has turned out to be essential since individuals are normally genuine with their sentiment on giving their perspective. However, in turning social media into a sentiment analysis possess challenges such as comments are usually ambiguous, language barrier problem, slang words, redundant comment, and sentiment classification. This study attempted to distinguish the issues of sentiment classification from Indonesian social media on Jakarta governor election. Several steps are taken to overcome those problems that include preprocessing. The preprocessing strategy used are removing the unrelated tweet, removing URL, deleting duplicate lines, deleting similar lines, removing the unrelated word, removing hashtag, removing Twitter username, removing number in the comment, removing punctuation, checking slang words, and converting the slang word into appropriate word. The preprocessed sentiment is then classified into positive, negative, and neutral. The classification method used in this study are Summation method, Average on Tweet, Average on Tweet with the threshold on objective score, Weighted Average, and Naïve Bayes method. The experimental results show that Naïve Bayes produce the highest precision, highest recall, and highest accuracy for neutral and positive sentiment. But, Naïve Bayes does not produce good results for negative sentiment.

Many companies are unable to maximize the use of information that would strengthen the company competitive market such as, the use of Customer Relationship Management (CRM) to gain insights into customer demands and needs. In addition, even though combination of mining user data and computational methods have been used, those approaches have some limitations. Also, only a few studies use RFM model in understanding the user. Furthermore, data mining techniques applied in the business environment is not yet descriptive. To overcome those problems, a two-step mining method is implemented in this study. Recency Frequency Monetary (RFM) model is used as the basis to create customer segmentation based on customer Recency, Frequency, and Monetary. The first step, the data is extracted to RFM model and then clustered using k-Means algorithm. In the second step, the data in each cluster is analyzed using association analysis to create customer characteristics represented by IF-THEN rules. The cluster results are analyzed using Silhouette and Connectivity measure. The rules are used to mine frequent patterns in each cluster and to explain each cluster to be more descriptive. The results show that the information in each cluster of customer segmentation has more valuable information that may benefit in understanding the customer for the company to apply specific strategies to increase company profits.