scholarly journals APLIKASI PEMILIHAN STRATEGI PROMOSI PENERIMAAN MAHASISWABARU POLITEKNIK NEGERI TANAH LAUT MENGGUNAKAN METODE K-MEANS CLUSTERING

2017 ◽  
Vol 2 (1) ◽  
pp. 99-104
Author(s):  
Veri Julianto ◽  
Jaka Permadi ◽  
Noviyanti Noviyanti
Keyword(s):  
Social Media ◽  
Promotion Strategy ◽  
Promotional Strategy ◽  
Numerous Type ◽  
New Students ◽  
Promotion Strategies ◽  
New Student ◽  
Media Data

Politeknik Negeri Tanah Laut in order to improve the new student admission, has adopted numerous type of promotion strategies. The strategies are among others high school visits, banner displays, brochure distribution, and promotions via social media. However, those strategies adopted by Politeknik Negeri Tanah Laut are admittedly costly. Application of Selecting Promotion Strategy Enrollment of New Students in the Politeknik Negeri Tanah Laut is an application used to help determine what kind of promotional strategy is likely to be more accurate and more planned as the guidance as well as the reference in order to increase the enrollment percentage of new students. The determination of such promotional media’s outcomes is supported by applying the method of K-Means Clustering to do the classification of promotional media data obtained from the result of students’ questionnaires. Obviously such thing becomes a solution to help determine the promotional strategy at Politeknik Negeri Tanah Laut.

scholarly journals Evaluation Of Communication Science Master Promotion Program In The Pandemic Situation Of Covid 19

2020 ◽  
Author(s):  
Christina Rochayanti ◽  
Edwi Arief Sosiawan ◽  
Basuki Agus Suparno ◽  
Puji Lestari
Keyword(s):  
Communication Technology ◽  
Study Program ◽  
Promotion Strategy ◽  
Analysis And Evaluation ◽  
Utilization Of Technology ◽  
Promotional Strategy ◽  
New Students ◽  
The Media ◽  
The Right ◽  
New Student

A promotion strategy during a pandemic by utilizing communication technology is the right solution. The UPN Yogyakarta Master of Communication Study Program has developed a promotional strategy by improving the website which has not been maximized, both in terms of appearance, menu completeness, data recording, interactive, and the person in charge of management. The research objective is to evaluate communication technology-based promotion programs during the Covid 19 pandemic. This research method uses the Analysis and Evaluation of Website Platform and Infrastructure. The results showed that the media developed by MIKOM Masters such as Instagram, website, and email with the upnyk.ac.id domain had been used by the admin for promotional activities for new student registration, the introduction of the new color of the MIKOM logo for new branding, namely purple. In addition, the response of prospective new students is quite good and wide-reaching. Disseminate registration information outside Java and increase the number of registrants. UPNVYK MIKOM alumni also often use the tag feature on their @mikomupnyk Instagram account to just interact or create engagement with MIKOM. Utilization of technology during a pandemic also requires the development of media to other platforms such as Facebook, Twitter, and the strengthening of admin human resources who master IT and have a clear schedule in its management

scholarly journals The Application of Data Mining by using K-Means Clustering Method in Determining New Students’ Admission Promotion Strategy

2020 ◽  
Vol 9 (3) ◽  
pp. 824-833
Keyword(s):  
Data Mining ◽  
Word Of Mouth ◽  
Study Program ◽  
Promotion Strategy ◽  
Equal Distribution ◽  
Study Programs ◽  
New Students ◽  
Promotion Strategies ◽  
The University ◽  
New Student

This study aims to determine the promotion strategy on the admission of new students at the university. Universities need appropriate promotion strategies to increase the number of new students enrolled in subsequent years and to fulfill the equal distribution of new students in each region and study programs at the University. Classification of new student data reception at the Indo Global Mandiri University (IGM University) in 2018/2019 uses the CRISP-DM data mining application (the Cross-Industry Standard Process for Data Mining) using the K-Means grouping method. Research data using primary and secondary data. The population and sample of the study were 1011 students using 4 (four) attributes in this study, namely the name of the student, the area of origin, the study program, and the promotion strategy (direct visit, word of mouth, media, brochures, and coming directly). This test is carried out with the Waikato Environment for Knowledge Analysis (WEKA) 3.8 tool. The results of this study indicate that the direct visit strategy is the most appropriate in the admission of new students at IGM University, amounting to 492 students with 26%, with this strategy being able to absorb many new student candidates from various regions including Palembang, Regency / City, and regions in outside South Sumatra, there is also equality in various study programs at IGM University. Word of mouth promotion strategies and media are optimized to be included in the promotion team in determining the promotion strategies in the following year to increase the number of new student admissions.

scholarly journals ANALISIS DATA MAHASISWA DENGAN ALGORITMA K-MEAN UNTUK MENDUKUNG STRATEGI PROMOSI STIKes HANG TUAH PEKANBARU

2018 ◽  
Vol 6 (2) ◽  
pp. 103-108
Author(s):  
Eka Sabna
Keyword(s):  
Data Mining ◽  
Data Mining Technique ◽  
Promotion Strategy ◽  
Student Data ◽  
Mining Technique ◽  
Promotional Strategy ◽  
New Students ◽  
Using Data ◽  
The Right ◽  
New Student

New student data owned by STIKes Hang Tuah Pekanbaru has been in large numbers with many variations of attributes stored in the database (database). But the data data has not been utilized optimally, therefore in need of a method that can dig and extract the new student data into a valuable information strategic. This study aims to classify the data of New Students of STIKes Hang Tuah Pekanbaru by using Data Mining Technique. Data Mining Technique used is Clustering Technique with K-Means algorithm and for process used CRISP-DM method. This research is used to determine the right Promotion Strategy. Determining the right promotional strategy will be able to reduce promotional costs and achieve the right promotional goals.

scholarly journals Review article: Detection of informative tweets in crisis events

2020 ◽  
Author(s):  
Anna Kruspe ◽  
Jens Kersten ◽  
Friederike Klan
Keyword(s):  
Social Media ◽  
Review Article ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Semantic Classification ◽  
Crisis Events ◽  
Learning Techniques ◽  
Media Data ◽  
Crisis Situations

Abstract. Messages on social media can be an important source of information during crisis situations, be they short-term disasters or longer-term events like COVID-19. They can frequently provide details about developments much faster than traditional sources (e.g. official news) and can offer personal perspectives on events, such as opinions or specific needs. In the future, these messages can also serve to assess disaster risks. One challenge for utilizing social media in crisis situations is the reliable detection of informative messages in a flood of data. Researchers have started to look into this problem in recent years, beginning with crowd-sourced methods. Lately, approaches have shifted towards an automatic analysis of messages. In this review article, we present methods for the automatic detection of crisis-related messages (tweets) on Twitter. We start by showing the varying definitions of importance and relevance relating to disasters, as they can serve very different purposes. This is followed by an overview of existing, crisis-related social media data sets for evaluation and training purposes. We then compare approaches for solving the detection problem based (1) on filtering by characteristics like keywords and location, (2) on crowdsourcing, and (3) on machine learning techniques with regard to their focus, their data requirements, their technical prerequisites, their efficiency and accuracy, and their time scales. These factors determine the suitability of the approaches for different expectations, but also their limitations. We identify which aspects each of them can contribute to the detection of informative tweets, and which areas can be improved upon in the future.We point out particular challenges, such as the linguistic issues concerning this kind of data. Finally, we suggest future avenues of research, and show connections to related tasks, such as the subsequent semantic classification of tweets.

scholarly journals Polarity Classification Tool for Sentiment Analysis in Malay Language

2019 ◽  
Vol 8 (3) ◽  
pp. 259
Author(s):  
Normi Sham Awang Abu Bakar ◽  
Ros Aziehan Rahmat ◽  
Umar Faruq Othman
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
English Language ◽  
Sentiment Lexicon ◽  
The Social ◽  
Polarity Classification ◽  
Media Channels ◽  
Classification Tool

<p>The popularity of the social media channels has increased the interest among researchers in the sentiment analysis(SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool(MaCT). This tool is developed based on the AFINN sentiment lexicon for English language. We have attempted to translate each word in AFINN to its Malay equivalent and later, use the lexicon to collect the sentiment data from Twitter. The Twitter data are then classified into positive, negative, and neutral. For the validation purpose, we collect 400 positive tweets, 400 negative tweets, and 200 neutral tweets, and later, run the tweets through our sentiment lexicon and found 90% score for precision, recall and accuracy. Our main contribution in the research is the new AFINN translation for Malay language and also the classification of the sentiment data.</p>

PENGARUH STRATEGI PROMOSI DI SOCIAL MEDIA TERHADAP KEPUTUSAN PEMBELIAN HANDPHONE MEREK APPLE YANG DI MEDIASI WORD OF MOUTH MARKETING (Studi Kasus Pada Queeniphone Bengkulu)

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Islamuddin Islamuddin ◽  
Ray Depa
Keyword(s):  
Social Media ◽  
Word Of Mouth ◽  
Ordinal Scale ◽  
Purchasing Decisions ◽  
Successive Interval ◽  
Promotion Strategy ◽  
Quantitative Data Analysis ◽  
Promotion Strategies ◽  
Survey Research Methods ◽  
Scale Data

ABSTRACT The purpose of this study was to determine The Effect of Promotion Strategies in Social Media toward Apple Brand Mobile Purchasing Decisions Mediated by Word of Mouth Marketing on Queen iphone Bengkulu. This research used survey research methods with quantitative data analysis by using the MSI method (method of successive interval) to transform ordinal scale data into interval scale data. The population of this study were all of consumers who buy Apple Brand Mobile products on Queeniphone Bengkulu. The results of this study know that there were significant effect toward Apple Brand Mobile Purchasing Decisions Mediated by Word of Mouth Marketing on Queen iphone Bengkulu. It is better for owner of Queen iphone Bengkulu  to maintain and even improve consumer purchasing decisions by using variuos promotion strategy. Keywords:  Promotion strategy, word of mouth marketing, purchasing decisions. 

scholarly journals Analisis Variable yang Memengaruhi Minat Pemilihan Perguruan Tinggi

2021 ◽  
Vol 7 (1) ◽  
pp. 31
Author(s):  
Musthofa Galih Pradana ◽  
Azriel Christian Nurcahyo ◽  
Fandli Supandi
Keyword(s):  
Social Media ◽  
Reference Data ◽  
Prospective Students ◽  
Priority Level ◽  
New Students ◽  
The Many ◽  
The University ◽  
Priority Levels ◽  
Promotion Effort

Kebijakan Promosi dari Universitas untuk mendapatkan mahasiswa baru merupakan hal yang vital bagi kelancaran proses penerimaan Mahasiswa Baru. Pihak lembaga perlu mengkaji dan melakukan analisis yang baik sebelum melakukan promosi. Di era sekarang, banyak media yang dapat dijadikan sebagai media promosi. Dengan berkembangnya cara penyebaran informasi serta banyaknya media pihak yang berkepentingan sudah seharusnya jeli mana yang mendapat prioritas lebih dari semua banyak lini media tempat promosi. Ketika tingkat prioritas sudah ditentukan maka upaya promosi akan menjadi lebih mudah dan lebih tepat sasaran. Akan tetapi penentuan tingkat prioritas tidak dapat dilakukan secara asal dan sembarangan. Perlu dilakukan pengkajian yang lebih dalam bukan hanya sekedar intuisi belaka. Penelitian ini membahas tentang pengaruh promosi dengan minat calon mahasiswa memilih AMIKOM sebagai universitas pilihan dengan acuan tiga variable yaitu sosial media, website, dan referensi alumni. Pengolahan data menggunakan software SPSS dan SPSS AMOS untuk mencari validitas, reliabilitas dan uji hipotesis. Hasil yang didapatkan adalah semua variable signifikan, dengan urutan dari nilai tertinggi ke terendah adalah Sosial Media, Referensi Alumni, dan Website. Kata Kunci—Promosi, Validitas, Reliabilitas, HipotesisPromotion policy from the University to get new students is vital for the smooth process of admission of New Students. The institution needs to review and conduct a good analysis before conducting a promotion. In this era, many media can be used as promotional media. With the development of ways of disseminating information and the many media interested parties it should be observant which gets priority over all the many lines of media wherepromotion. When the priority level has been determined, the promotion effort will be easier and more targeted. However, the determination of priority levels cannot be done arbitrarily and carelessly. Deeper studies need to be done not just mere intuition. This study discusses the effect of promotion with the interest of prospective students choosing AMIKOM as the university of choice with three variables reference, namely social media, website, and alumni reference. Data processing using SPSS and SPSS AMOS software to find validity, reliability, and hypothesis testing. The results obtained are all significant variables, with the order from highest to lowest values are Social Media, Alumni Reference, and Website.Keywords— Promotion, Validity, Reliability, Hypothesis

scholarly journals Uploading Social Media as Indirect Promotion Strategies in Film Destination Tourism: Re-Enactment of Ada Apa dengan Cinta 2 in Sellie Coffee

2020 ◽  
Vol 2 (1) ◽  
pp. 10
Author(s):  
Ju Hee Bhin
Keyword(s):  
Social Media ◽  
Qualitative Approach ◽  
Literature Study ◽  
Promotion Strategy ◽  
Research Results ◽  
Promotion Strategies ◽  
Communication Facility

This study aims at understanding tourist activities and re-enactment of AADC 2 that can potentially be an indirect promotion strategy of Sellie Coffee. Uploading photography of the reenactment of AADC 2 in social media leads to a sensational indirect promotion strategy that needs to be paid any attention. This study is conducted in Sellie Coffee, located in Jalan Prawirotaman, Yogyakarta, Indonesia. This research employs qualitative approach and all the data is obtained through literature study, observation, and interview. The research results show that Instagram, one of a popular social media, provides a complete communication facility, from tourism branding to usergenerated content photography. Moreover, the activity of uploading photos related to AADC 2 in Sellie Coffee visited by film tourists in social media has become one of indirect promotion strategies.

Optimizing Social Media Data Using Genetic Algorithm

2019 ◽  
pp. 126-153
Author(s):  
Sourav Das ◽  
Anup Kumar Kolya ◽  
Dipankar Das
Keyword(s):  
Genetic Algorithm ◽  
Social Media ◽  
Feature Selection ◽  
Feature Reduction ◽  
Social Media Data ◽  
Training Time ◽  
Redundant Feature ◽  
Media Data ◽  
And Training

Twitter-based research for sentiment analysis is popular for quite some time now. This is used to represent documents in a corpus usually. This increases the time of classification and also increases space complexity. It is hence very natural to say that non-redundant feature reduction of the input space for a classifier will improve the generalization property of a classifier. In this approach, the researchers have tried to do feature selection using Genetic Algorithm (GA) which will reduce the set of features into a smaller subset. The researchers have also tried to put forward an approach using Genetic Algorithm to reduce the modelling complexity and training time of classification algorithm for 10k Twitter data based on GST. They aim to improve the accuracy of the classification that the researchers have obtained in a preface work to this work and achieved an accuracy of 87% through this work. Hence the Genetic Algorithm will do the feature selection to reduce the complexity of the classifier and give us a better accuracy of the classification of the tweet.

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