scholarly journals IMPLEMENTASI ALGORITMA MODIFIED GUSTAFSON-KESSEL UNTUK CLUSTERING TWEETS PADA AKUN TWITTER LAZADA INDONESIA

2019 ◽  
Vol 8 (3) ◽  
pp. 285-295
Author(s):  
Ratna Kencana Putri ◽  
Budi Warsito ◽  
Mustafid Mustafid
Keyword(s):  
Social Media ◽  
Partition Coefficient ◽  
Clustering Algorithm ◽  
Optimum Number ◽  
Number Of Clusters ◽  
Online Social Media ◽  
Internet Users ◽  
Algorithm Validation ◽  
Twitter Account ◽  
Product Segmentation

Online social media is a new kind of media which is steadily growing and has become publicly popular. Due to its ability to spread informations rapidly and its easiness to access for internet users, social media provides new alternative to conduct advertising and product segmentation. Twitter is one of the most favored social media with 19.5 million users in Indonesia to the date. In this research, the application of text mining to cluster tweets from the @LazadaID Twitter account is done using the Modified Gustafson-Kessel clustering algorithm. The clustering process is executed five times with the number of cluster starts from two to six cluster. The results of this research indicate that the optimum number of clusters formed based on the Partition Coefficient and Classification Entropy validation index are three clusters. Those three clusters are tweets containing electronic stuff offers, discounts, and prize quizes. Tweets with the most retweets and likes are prize quiz tweets. PT Lazada Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @LazadaID Twitter account followers.Keywords: Twitter, advertising, Lazada Indonesia, Gustafson-Kessel Clustering algorithm, validation index

scholarly journals PENERAPAN TEXT MINING UNTUK MELAKUKAN CLUSTERING DATA TWEET AKUN BLIBLI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN K-MEANS CLUSTERING

2022 ◽  
Vol 10 (4) ◽  
pp. 583-593
Author(s):  
Syiva Multi Fani ◽  
Rukun Santoso ◽  
Suparti Suparti
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Virtual Networks ◽  
Number Of Clusters ◽  
Silhouette Coefficient ◽  
Twitter Account ◽  
Computer Based ◽  
Twitter Users ◽  
Clustering Data ◽  
Coefficient Method

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.

scholarly journals Analisis Kinerja Fuzzy C-Means (FCM) dan Fuzzy Subtractive (FS) dalam Clustering Data Alumni STMIK STIKOM Indonesia

2021 ◽  
Vol 6 (1) ◽  
pp. 41
Author(s):  
I Kadek Dwi Gandika Supartha ◽  
Adi Panca Saputra Iskandar
Keyword(s):  
Partition Coefficient ◽  
Clustering Algorithm ◽  
Large Data ◽  
Optimal Number ◽  
Subtractive Clustering ◽  
Data Set ◽  
Number Of Clusters ◽  
Fuzzy C Means ◽  
Fitness Value ◽  
Clustering Data

In this study, clustering data on STMIK STIKOM Indonesia alumni using the Fuzzy C-Means and Fuzzy Subtractive methods. The method used to test the validity of the cluster is the Modified Partition Coefficient (MPC) and Classification Entropy (CE) index. Clustering is carried out with the aim of finding hidden patterns or information from a fairly large data set, considering that so far the alumni data at STMIK STIKOM Indonesia have not undergone a data mining process. The results of measuring cluster validity using the Modified Partition Coefficient (MPC) and Classification Entropy (CE) index, the Fuzzy C-Means Clustering algorithm has a higher level of validity than the Fuzzy Subtractive Clustering algorithm so it can be said that the Fuzzy C-Means algorithm performs the cluster process better than with the Fuzzy Subtractive method in clustering alumni data. The number of clusters that have the best fitness value / the most optimal number of clusters based on the CE and MPC validity index is 5 clusters. The cluster that has the best characteristics is the 1st cluster which has 514 members (36.82% of the total alumni). With the characteristics of having an average GPA of 3.3617, the average study period is 7.8102 semesters and an average TA work period of 4.9596 months.

scholarly journals Micro-celebrity participation and risk communication in Indonesia

2020 ◽  
Vol 26 (2) ◽  
pp. 53-71
Author(s):  
Lidwina Mutia Sadasri
Keyword(s):  
Social Media ◽  
Risk Communication ◽  
Information Dissemination ◽  
Online Social Media ◽  
Internet Users ◽  
Social Media Platforms ◽  
Massive Number ◽  
The Media ◽  
The Government ◽  
National Disaster

Information dissemination in the media, specifically social media, is one of the critical channels of information related to the COVID-19 outbreak sought by the public. The information presented has been related to accurate and reliable situation reports and false information in various forms, not only text-based but also audio and visual. The chaos of data, coupled with a central response that seemed unprepared, shaped the Indonesian community’s perceptions of the COVID-19 outbreak. This fact related to the massive number of internet users in Indonesia is one aspect of the government’s decision, in this case BNPB (Badan Nasional Penanggulangan Bencana; officially National Disaster Management Authority), to engage strong social media influencers. The government collaborated with some influencers to enable public engagement through online social media platforms in the context of COVID-19—two of them being @dr.tirta and @rachelvennya. The platforms also gained more visibility after being appointed COVID-19 influencers. They updated information about COVID-19 on their social media accounts with picture posts and Instagram stories, either individually or in collaboration with others. This study aims to analyse the practice of the Indonesian government’s agency using micro-celebrity to deploy a risk communication frame and the delivery of the message by a celebrated person.

Social Networking Sites

2012 ◽  
pp. 132-147 ◽  
Author(s):  
Minas Michikyan ◽  
Kaveri Subrahmanyam
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Emerging Adults ◽  
Psychosocial Development ◽  
Social Networking Sites ◽  
Media Use ◽  
Well Being ◽  
Social Media Use ◽  
Online Social Media ◽  
Internet Users

In the past few years, social networking sites (SNSs) such as Facebook and MySpace have become increasingly popular among Internet users. They allow individuals to present themselves, share information, establish or maintain connections, and interact and communicate with other users. As SNSs have become tremendously popular among adolescents and emerging adults, research suggests that online social media use may be connected to young people’s development. This encyclopedia entry summarizes up-to-date research on SNSs, and will focus on the relation between adolescents’ and emerging adults’ use of these sites to address traditional developmental concerns and their psychosocial well-being. Researchers have begun to explore the extent to which individuals engage in self-presentation and exploration as well as relationship formation on SNSs, and are examining the relationship between such use and psychosocial outcomes among youth. As digital youth are growing up in an ever connected world, it is important to understand online social media use and the implications of such use on their psychosocial development and psychological well-being.

scholarly journals PENERAPAN FUZZY C-MEANS KLUSTER UNTUK SEGMENTASI PELANGGAN E-COMMERCE DENGAN METODE RECENCY FREQUENCY MONETARY (RFM)

2020 ◽  
Vol 9 (4) ◽  
pp. 421-433
Author(s):  
Stevanus Sandy Prasetyo ◽  
Mustafid Mustafid ◽  
Arief Rachman Hakim
Keyword(s):  
Marketing Strategy ◽  
Customer Segmentation ◽  
Optimum Number ◽  
Number Of Clusters ◽  
Business World ◽  
The Public ◽  
Fuzzy C Means ◽  
Internet Users ◽  
Ease Of Access ◽  
The Right

E-commerce has become a medium for online shopping which is growing and popular among the public. Due to the ease of access for all internet users and the completeness of the products offered, e-commerce has become a new alternative in meeting people's needs. Currently, the competition in the business world is very fierce, any e-commerce company needs to be able to carry out the right marketing strategy to compete in acquiring, retaining, and partnering with customers. In this research, the segmentation of e-commerce customers was carried out using the Fuzzy C-Means cluster and the RFM method. The clustering process is carried out six times with the number of clusters starts from two to seven clusters. The results showed that the optimum number of clusters formed according to the Xie-Beni validity index was four clusters. The cluster becomes customer segments that have the characteristics of each customer based on their recency, frequency, and monetary value. The best segment is segment 4 which has very loyal customers in shopping on tumbas.in e-commerce. From the segments that have been formed, they can be used as a consideration in implementing the right marketing strategy for each customer. Keywords : E-commerce, customer segmentation, Fuzzy C-Means Cluster, RFM, Xie-Beni Index

scholarly journals Spatial Modification in the Parameters of Mountain Image Clustering Algorithm

2019 ◽  
Vol 22 (1) ◽  
pp. 55-58
Author(s):  
Nahla Ibraheem Jabbar
Keyword(s):  
Clustering Algorithm ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Large Data ◽  
Large Data Sets ◽  
Image Clustering ◽  
Optimum Number ◽  
Data Sets ◽  
Number Of Clusters ◽  
Pixel Value

Our proposed method used to overcome the drawbacks of computing values parameters in the mountain algorithm to image clustering. All existing clustering algorithms are required values of parameters to starting the clustering process such as these algorithms have a big problem in computing parameters. One of the famous clustering is a mountain algorithm that gives expected number of clusters, we presented in this paper a new modification of mountain clustering called Spatial Modification in the Parameters of Mountain Image Clustering Algorithm. This modification in the spatial information of image by taking a window mask for each center pixel value to compute distance between pixel and neighborhood for estimation the values of parameters σ, β that gives a potential optimum number of clusters requiring in image segmentation process. Our experiments show ability the proposed algorithm in image brain segmentation with a quality in the large data sets

scholarly journals Understanding international perceptions of the severity of harmful content online

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256762
Author(s):  
Jialun Aaron Jiang ◽  
Morgan Klaus Scheuerman ◽  
Casey Fiesler ◽  
Jed R. Brubaker
Keyword(s):  
Social Media ◽  
Status Quo ◽  
International Survey ◽  
Perceived Severity ◽  
Online Social Media ◽  
Internet Users ◽  
The World ◽  
Social Media Platforms ◽  
Harmful Content ◽  
Similarities And Differences

Online social media platforms constantly struggle with harmful content such as misinformation and violence, but how to effectively moderate and prioritize such content for billions of global users with different backgrounds and values presents a challenge. Through an international survey with 1,696 internet users across 8 different countries across the world, this empirical study examines how international users perceive harmful content online and the similarities and differences in their perceptions. We found that across countries, the perceived severity consistently followed an exponential growth as the harmful content became more severe, but what harmful content were perceived as more or less severe varied significantly. Our results challenge platform content moderation’s status quo of using a one-size-fits-all approach to govern international users, and provide guidance on how platforms may wish to prioritize and customize their moderation of harmful content.

Twitter Hashtag and Online Engagement During Surgical Society Meetings Over a 5-Year Period

2020 ◽  
pp. 000313482094229
Author(s):  
Adel Elkbuli ◽  
Kristen Santarone ◽  
Evander Meneses ◽  
Mark McKenney
Keyword(s):  
Social Media ◽  
Medical Information ◽  
Annual Scientific ◽  
Online Social Media ◽  
Online Engagement ◽  
New Knowledge ◽  
Social Media Platforms ◽  
Twitter Account ◽  
Online Experience ◽  
Active Interaction

Background Utilizing social media platforms can augment medical conferences by sharing new knowledge and information through hashtags. We aim to investigate the use of Twitter, enhanced conference experience, and education at the Eastern Association for the Surgery of Trauma (EAST) Annual Scientific Assembly 2016-2020. Methods EAST hashtags were analyzed for the Annual Scientific Assembly 2016-2020: #EAST2016, #EAST2017, #EAST2018, #EAST2019, and #EAST2020. Using #EAST2016 as a baseline, active interaction through engagement, measured by tweets and retweets, passive interaction through impressions, measured by views, as well as users, and influencers were analyzed. Results 2016-2018 saw a significant increase in engagement (7400 to 9200 to 11 000, respectively, P < .05). 2018-2020 then showed a significant decrease in engagement (11 000 to 9000 to 6700, respectively, P < .05). Impressions, increased significantly from 2016 to 2020 (6.6 million to 13.3 million to 12.6 million to 19.9 million to 20.3 million, respectively, P < .05). Users significantly increased to 2700 in 2018 compared with 1000 in 2016 ( P < .05), and significantly decreased to 814 in 2020 compared with 2700 in 2018 ( P < .05). The top online influencer was the EAST organization Twitter account. Conclusion #EAST2016-2018 showed a significant increase in engagement between users, measured by tweets/retweets. However, #EAST2019-2020 suffered declines in users’ engagement, maybe a result of social media fatigue. Although, #EAST2016-2020 showed a significant increase in impressions. Both passive and to a more extent active forms of online social media engagement can potentially disseminate new knowledge and medical information. Scientific societies should focus on more effective ways to maintain and enhance users’ online experience and engagement toward better utilization of social media platforms.

scholarly journals An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rachid Sammouda ◽  
Ali El-Zaart
Keyword(s):  
Prostate Cancer ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Near Infrared ◽  
Optimum Number ◽  
Cancer Disease ◽  
Number Of Clusters ◽  
Segmented Images ◽  
Segmentation Approach ◽  
Noninvasive Therapy

Prostate cancer disease is one of the common types that cause men’s prostate damage all over the world. Prostate-specific membrane antigen (PSMA) expressed by type-II is an extremely attractive style for imaging-based diagnosis of prostate cancer. Clinically, photodynamic therapy (PDT) is used as noninvasive therapy in treatment of several cancers and some other diseases. This paper aims to segment or cluster and analyze pixels of histological and near-infrared (NIR) prostate cancer images acquired by PSMA-targeting PDT low weight molecular agents. Such agents can provide image guidance to resection of the prostate tumors and permit for the subsequent PDT in order to remove remaining or noneradicable cancer cells. The color prostate image segmentation is accomplished using an optimized image segmentation approach. The optimized approach combines the k-means clustering algorithm with elbow method that can give better clustering of pixels through automatically determining the best number of clusters. Clusters’ statistics and ratio results of pixels in the segmented images show the applicability of the proposed approach for giving the optimum number of clusters for prostate cancer analysis and diagnosis.

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