Instagram is a social media that has the potential to be used to increase awareness of a product. Approximately 70% of users spend their time searching for a product on Instagram. Many people promote their products with a lack of attention to the target. So that not infrequently the information distributed is inaccurate information and not following user characteristics. This study aims to cluster the characteristics of Instagram users based on hashtag compatibility. The method used in this study is the K-Means Clustering method. Based on the results of the experiment, this research succeeded in clustering Instagram users based on the hashtag match on the text caption. Besides, TF-IDF can be used as a feature suitable for the K-Means Klastering method. The results of the hashtag "#kopi" analysis resulted in hashtag suggestions that can be used for the promotion of a product related to coffee, including the hashtag #coffeeshop and #coffee with total usage of 14968 captions.
The promotion of goods or services is now facilitated by the dissemination of information through Instagram. Dissemination of information is usually done by influencers or promotional accounts. The account used certainly has a lot of followers. Because of the large amount of follower data in that account, it can be grouped into the same characters. This is done to determine the potential for promotion using social media accounts. This study uses data from 2 popular accounts. The first account is an artist with the username ayutingting92. The second account is Infounjaya, the official promotion account from Jenderal Achmad Yani University, Yogyakarta. The results of grouping can divide follower data into two cluster groups with different interactions. The basic difference between the two groups is the number of likes and comments. The infounjaya account analysis results showed that of 4,906 followers, only 3,211 followers were actively involved in the interaction, 1,695 followers were passive followers who did not like or did not comment on the interaction. Meanwhile, the results of the ayutingting92 follower cluster show that out of 1 million sample data followers, only 13,591 followers were actively involved in the interaction of likes and comments, 986,409 were passive followers.
Content generated by Instagram users related to the Healthy Living Community Movement (GERMAS) has provided new media information that is important for the community and, in particular, the health department. At present, Indonesia is facing a serious challenge in the form of a double burden of disease. Changes in people's lifestyles are suspected to be one of the causes of a shift in disease patterns (epidemiological transition) in the last 30 years. Discussions on what topics occur in the community related to health, as well as community complaints, have not been identified. The Data Mining technique makes it possible to analyze and extract any topics that are contained from the data captions from Instagram. This study uses Latent Dirichlet Allocation (LDA) as a method for modeling topics. The results of evaluating the number of topics using topic coherence yielded the eight most appropriate topic segments. Based on the results of content analysis on each topic segment, it was found that the most dominant topic related to GERMAS was a healthy lifestyle diet.
The efficiency of using social media affected modern society's nature and communication; they are more interested in talking through social media than meeting in the real world. The number of talks on social media content depends on the topic being discussed. The more topic interesting will impact the amount of data on social media will be. The data can be analyzed to get the influence of actors (account mentions) on the conversation. The power of an actor can be measured from how often the actor is mentioned in the conversation. This paper aims to conduct entity profiling on social media content to analyze an actor's influence on discussion. Furthermore, using sentiment analysis can determine the sentiment about an actor from a conversation topic. The Latent Dirichlet Allocation (LDA) method is used for analyzes topic modeling, while the Support Vector Machine (SVM) is used for sentiment analysis. This research can show that topics with positive sentiment are more likely to be involved in disaster management accounts, while topics with negative sentiment are more towards involvement in politicians, critics, and online news.
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