Network formed between users in a social media can be used to encourage information spreading among them. This research applied Social Network Analysis which further can be used to social media marketing to improve the marketing process effectively. Based on previous research, information spreading speed among the social media is affected by the users' activity connection which can be represented in centrality value. The centrality value itself is very affected by the graph structure and weights. This research applied degree and eigenvector centrality to observe the effect of centrality value for twitter data. The result shows that there is significant difference among 10 most influential users. This result will be used for the future research that will be focused in small and medium enterprise (SME) twitter data.

In this paper we study how to determine the nodes that most influential to a node in the network. Social Network Analysis (SNA) can measure the centrality of a node in order to obtain an influential nodes in the dissemination of information. One of the centrality measurement that can be applied is degree centrality. In this research, the method used is Opsahl method, combines two indicators, the number of neighborhood (degree) and the amount of weight relations (strength) of a node and uses tuning parameters. The weight relations are obtained from the number of relations as following/follower, mention and reply. Tuning parameters are parameters which are used to set the influence of both degree and strength to the degree centrality measurement results. Based on test results, the node who has a high strength value is derived from weight relations which are obtained from mentions and replies

According to the World Health Organization (WHO), some chronic diseases such as diabetes mellitus, stroke, cancer, cardiac vascular, kidney failure, and hypertension are essential for early prevention. One of the prevention that can be taken is to predict chronic diseases using machine learning based on personal medical record or general checkup result. The common prediction objective is to minimize the prediction error as low as possible. The most influencing chronic diseases prediction factors are the quality of data and the choice of predictor such as machine learning methods. The five main problems those lower data quality are outliers, missing values, feature selection, normalization, and imbalance. After we ensure the quality of data, the next task is to choose the best machine learning methods. The most influencing factor to consider when we choose the predictor its performance evaluation (accuracy, recall, precision, f1-score). Thus, predicting chronic disease aims to produce increased performance and solve problems in medical data. This paper presents a Systematic Literature Review (SLR) that offers a comprehensive discussion of research on chronic diseases prediction using machine learning and its data preprocessing handling. This paper covers machine learning methods discussion such as supervised learning, ensemble learning, deep learning, and reinforcement learning. The preprocessing handling we discuss includes missing values, outliers, feature selection, normalization, and imbalance. The final discussions of this paper are open issues, and the potential future works in improving the prediction performance for chronic diseases using a data preprocessing handling and machine learning methods.

Aspect extraction is an important step in opinion mining, to generate list of object, aspect and its opinions. Therefore, previous studies still give an opportunity to find the pattern of an aspect and opportunities in terms of aspect extraction performance. This paper propose syntactic pattern based on features observation to extract aspects from unstructured review, accompanied with a comprehensive analysis of varied pattern. This paper also provides some technical issues that arise based on performance evaluation and analysis using syntactic pattern extraction. The experimental results showed that the syntactic pattern approach had several weaknesses that need to be improved

Social Network like Twitter, Facebook, Plurk and Linkedin has million users that constantly evolving and has good information both explicit and implicit information that needs to be explored. Social Network Analysis is the study of social networking with an emphasis on mapping relationships, patterns of interaction between user and content information. One analysis that often be explored is centrality measures that are useful to find the important people in a community by representing in a graph. Kretschmer method is one method of measuring centrality. It is the development of the degree centrality, where the centrality measurement is to find influential people not only involves the number of degree of the user, but also involves the relation weights based on the number of interactions. Therefore, representing the weight graph is very necessary in finding the influential people. Kretschmer method is more suitable for the case of twitter. It is because the most important thing to note is the proximity of the user based on the relationship following/followed and the number of tweet interaction such as mention, retweet and reply. Deeper discussion about the analysis of Kretschmer methods and implementation techniques on twitter is the main discussion of this journal.

The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the big five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features.