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The High Economic Value for Videos in a YouTube Channel by Communication using Digital social media (Case Study: Successful Videos about A Particular Motorcycle Group in a YouTube Channel) 23

The High Economic Value for Videos in a YouTube Channel by C...

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6th International Conference on computers in Management and Business, ICCMB 2023

作者： Pane, Murty Magda Siregar, Christian Sriyanto, Hari Character Building Development Center School of Computer Science Computer Science Department Bina Nusantara University Jakarta Indonesia Character Building Development Center Mass Communication Program Communication Department Faculty of Digital Communication and Hotel and Tourism Bina Nusantara University Jakarta Indonesia Character Building Development Center Information System Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450398046

This research's objective is to describe the sustainable activities of a particular motorcycle club (TP) by delivering reciprocal humor and showing nice friendship on social media from a business perspective, especially from a business psychology perspective. The research used a qualitative method by observations in seven periods for seven tour opportunities. Their high amount of interactive communication has led them into the most popular motorcycle group (recognized by the high number of views, like expressions, and positive comments on the YouTube channel and Instagram) which has also led it to a higher economic value. Their particular cohesiveness and engagement were transmitted and deployed in their internal communications in the videos that influence the netizens and the business world positively through their reciprocal humor and nice friendship. © 2023 ACM.

关键词： Motorcycles

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A Smote-Based Churn Prediction System Using Machine Learning Techniques

A Smote-Based Churn Prediction System Using Machine Learning...

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2023 International Conference on science, Engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Akinrotimi, Akinyemi Omololu Ogundokun, Roseline Oluwaseun Mabayoje, Modinat Abolore Oyekunle, Rafiat Ajibade Adebiyi, Marion Olubunmi Augustine University Department of Mathematics Sciences Computer Science Program Ilara-Epe Epe Nigeria Kaunas University of Technology Department of Multimedia Engineering Lithuania Landmark University Omu Aran Sdg 4. Quality Education Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria University of Ilorin Department of Information and Communication Ilorin Nigeria Landmark University Omu-Aran Department of Computer Science Omu Aran Nigeria

ISBN: (纸本)9798350324785

The financial backbone of every telecommunications company is strictly made up of the number of customers patronizing the organization. Due to the high level of competition amongst existing telecommunication companies, customers sometimes leave, dropping the services of a particular one for the other. Predicting which subscribers may want to leave a telecommunications company and providing solutions to keep them from doing so, are the main objectives of customer churn prediction. Churn prediction helps classify consumers who are likely to move from one business to another. The ability to foresee churners before they leave has been increasingly useful in recent years, especially in light of increased competition among communications carriers. To compute churners for telecommunications providers, the study described in this paper used oversampling techniques to balance churn data and applied the dimensionality reduction technique to discover optimal features with a strong predictive ability for detecting would-be churners and non-churners. The model used Logistic Regression and the Naïve Bayes Classification Algorithm to implement comparative classification strategies. The performance levels of these strategies were compared using a Telecommunications Customer Churn data set, as well as performance metrics such as accuracy, sensitivity, and specificity. The Naïve Bayes Algorithm proves more efficient than its counterpart giving higher positive rate detection and a lower negative rate detection. © 2023 IEEE.

关键词： Forecasting

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Web Development of Direct-to-consumer Genetics Testing 4

Web Development of Direct-to-consumer Genetics Testing

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Azizatikarna, Kians Parung, Deby Erina Amirullah, Dian Hidayat, Alam Ahmad Cenggoro, Tjeng Wawan Budiarto, Arif Simon Pardamean, Bens Genetics Indonesia Jakarta12940 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

As the cost of genetic testing becomes more affordable each year, direct-to-customer (DTC) genetic testing services witness rapid market growth. This has encouraged the development of an easy-to-use website application to optimize potential customers to obtain informed choice regarding the offered DTC genetic testing as well as purchasing information. We present a wireframing process as a part of the agile software development process to build a web portal prototype for an Indonesia-based genetic testing service called DNAku. The approach in building the prototype used in this case is a part of the Agile Software Development Method. In brief, the wireframing stage resulted in this work consists of three main important outcomes. The first one is the use case diagram as a blueprint of this web-based system. A site map is then proposed as an extended version of the use case diagram which visualizes the whole pages with the links. Finally, based on this site map a series of page mock-ups are designed using Hyper Text Marking Language (HTML) and Cascading Style Sheets (CSS). The result of the prototype can be used as the basis for the next development stage, which is the coding stage based on the collected feedback from users. Considering the continuous application development via the wireframing method by taking into account the cycle of feedbacks from involved parties, this approach can strengthen the infrastructure to sustain DTC genetic business model, which has a niche market in Indonesia. © 2022 IEEE.

关键词： Sales

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Audio-Driven Facial Landmark Generation in Violin Performance using 3DCNN Network with Self Attention Model 48

Audio-Driven Facial Landmark Generation in Violin Performanc...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Lin, Ting-Wei Liu, Chao-Lin Su, Li Taiwan International Graduate Program Social Networks and Human-Centered Computing Taiwan Academia Sinica Institute of Information Science Taipei Taiwan National Chengchi University Department of Computer Science Taipei Taiwan

ISBN: (纸本)9781728163277

In a music scenario, both auditory and visual elements are essential to achieve an outstanding performance. Recent research has focused on the generation of body movements or fingering from audio in music performance. The audio-driven face generation technique in music performance is still deficient. In this paper, we compile a violin soundtrack and facial expression dataset (VSFE) for modeling facial expressions in violin performance. To our knowledge, this is the first dataset mapping the relationship between violin performance audio and musicians' facial expressions. We then propose a 3DCNN network with self-attention and residual blocks for audio-driven facial expression generation. In the experiments, we compare our methods with three baselines on talking face generation. The codes and dataset are available on the Github (https://***/kevinlin91/icassp-music2face). © 2023 IEEE.

关键词： Music

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Recommendation News for Joumalist Based on Twitter Data Using CNN 6

Recommendation News for Joumalist Based on Twitter Data Usin...

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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Harvy, Ikrar Girsang, Abba Suganda Saragih, Melva Hermayanty Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

Getting the update news fast is important for journalist. This study uses Twitter data which is fast and rich information for get the update news and useful for journalist. This study starts from collecting data from Twitter, then predicting the category of tweet using convolution neural network (CNN) algorithm. With convolution neural network data, Twitter can categorize 10 news categories that mention news to official Twitter media on Twitter social media. Scenario data in predicting news by dividing the data based on 70% training, 15% validation, and 15% testing. Then the data is converted into vector form where the convolution neural network predicts the data in vector form. The results show that, the convolution neural network is able to conduct the good performance based on the values of accuracy, precision, recall and f1 score. It also compares by the other methods. © 2022 IEEE.

关键词： classification news convolution neural network deep learning journalist Social media

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Human-computer Interaction and Knowledge Transfer: Study of E-Learning Achievement 7

Human-Computer Interaction and Knowledge Transfer: Study of ...

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7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Alamsyah, Doni Purnama Othman, Norfaridatul Ahkmaliah Kerta, Johan Muliadi Morika, Doni Ramdhani, Yudi Bina Nusantara University Binus Business School Creativepreneurship Department Jakarta Indonesia Universiti Teknikal Malaysia Melaka Department of Management Technology Melaka Malaysia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Design Design Interior Department Jakarta Indonesia Satu University Information System Program Bandung Indonesia

ISBN: (纸本)9798331505530

Human-computer interaction influences the effectiveness of e-learning knowledge transfer, so it is necessary to pay attention to user engagement and achievement. The aim of this research is to examine the factors that support e-learning knowledge transfer through achievement and user engagement (self-efficacy, self-confidence, self-esteem). The research method was carried out by surveying e-learning users, namely students, data from students was collected through quantitative questionnaires. The data is then processed using the SmartPLS tool through two tests, namely the PLS Algorithm and Boostrapping. The model was tested using inner and outer tests, and the data was analyzed based on the research hypothesis design. Student interactions with e-learning influence several behaviors that can support e-learning knowledge transfer performance. The research results prove that student e-learning achievement has a positive impact on e-learning knowledge transfer. Several supporting factors play a role in increasing e-learning achievement, namely self-efficacy, self-confidence and self-esteem of students. However, the supporting factors for students are only self-efficacy and self-confidence which are directly able to increase e-learning knowledge transfer. It is known that students who are less able to respect themselves in interacting with e-learning are unable to support knowledge transfer. The rules of human-computer interaction play a role in supporting e-learning knowledge transfer, studies on user behavior need to be considered, especially self-efficacy and self-confidence to support online learning academic performance. © 2024 IEEE.

关键词： Knowledge transfer

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A systematic literature review of machine learning application in COVID-19 medical image classification

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Procedia computer science 2023年 216卷 749-756页

作者： Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia. Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia. Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia.

Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance.

关键词： COVID-19 classification disease machine learning medical images

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A Comparative Study of Machine Learning Models for Sentiment Analysis on User Reviews of the Desa Wisata Nusantara Application in the Google Play Store 11

A Comparative Study of Machine Learning Models for Sentiment...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Maulana, Fairuz Iqbal Rahayu, Anita Widartha, Vandha Pradwiyasma Adi, Puput Dani Prasetyo Purnomo, Agung Pramono, Andi School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Pukyong National University Department of Information System Busan Korea Republic of Bandung Indonesia Binus Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia

ISBN: (纸本)9798350389654

In the swiftly changing digital environment, user reviews on platforms like the Google Play Store have emerged as a crucial factor influencing an application's success. These reviews, abundant in user experiences and perceptions, serve as a valuable asset for sentiment analysis. However, the sheer volume of these reviews poses a challenge for manual analysis, thereby necessitating the deployment of automated sentiment analysis methods. We utilize the Bernoulli Naive Bayes (BNB) and Support Vector Machine (SVM) models, selected for its simplicity, efficiency, and capability to handle large datasets with high dimensionality, characteristic of text data such as user reviews. Based on the analysis and conclusions derived from the research inquiries in this study, it is evident that a significant proportion of customer reviews, classifying 42.39% (or 142 reviews) as positive, 45.07% (or 151 reviews) as negative, and 12.54 % (or 42 reviews) as Neutral sentiments regarding the Desa Wisata Nusantara Application on the Google Play Store. We found that the Bernoulli Naive Bayes model had better accuracy than the Support Vector Machine. The accuracy level of the Bernoulli Naive Bayes model is 73 % and the Support Vector Machine is 70%. We present the outcomes of our analysis, discuss their implications, and propose directions for future research. Our research signifies a promising advancement towards harnessing machine learning for sentiment analysis, setting the stage for future investigations in this intriguing field. © 2024 IEEE.

关键词： Contrastive Learning

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Word reuse and combination support efficient communication of emerging concepts

arXiv

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arXiv 2024年

作者： Xu, Aotao Kemp, Charles Frermann, Lea Xu, Yang Department of Computer Science University of Toronto Canada School of Computing and Information Systems University of Melbourne Australia School of Psychological Sciences University of Melbourne Australia Cognitive Science Program University of Toronto Canada

A key function of the lexicon is to express novel concepts as they emerge over time through a process known as lexicalization. The most common lexicalization strategies are the reuse and combination of existing words, but they have typically been studied separately in the areas of word meaning extension and word formation. Here we offer an information-theoretic account of how both strategies are constrained by a fundamental tradeoff between competing communicative pressures: word reuse tends to preserve the average length of word forms at the cost of less precision, while word combination tends to produce more informative words at the expense of greater word length. We test our proposal against a large dataset of reuse items and compounds that appeared in English, French and Finnish over the past century. We find that these historically emerging items achieve higher levels of communicative efficiency than hypothetical ways of constructing the lexicon, and both literal reuse items and compounds tend to be more efficient than their non-literal counterparts. These results suggest that reuse and combination are both consistent with a unified account of lexicalization grounded in the theory of efficient communication. © 2024, CC BY-NC-ND.

关键词： Information theory

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WORDS ARE ALL YOU NEED? LANGUAGE AS AN APPROXIMATION FOR HUMAN SIMILARITY JUDGMENTS 11

WORDS ARE ALL YOU NEED? LANGUAGE AS AN APPROXIMATION FOR HUM...

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11th International Conference on Learning Representations, ICLR 2023

作者： Marjieh, Raja van Rijn, Pol Sucholutsky, Ilia Sumers, Theodore R. Lee, Harin Griffiths, Thomas L. Jacoby, Nori Department of Psychology Princeton University United States Max Planck Institute for Empirical Aesthetics Germany Department of Computer Science Princeton University United States Max Planck Institute for Cognitive and Brain Sciences Germany

Human similarity judgments are a powerful supervision signal for machine learning applications based on techniques such as contrastive learning, information retrieval, and model alignment, but classical methods for collecting human similarity judgments are too expensive to be used at scale. Recent methods propose using pre-trained deep neural networks (DNNs) to approximate human similarity, but pre-trained DNNs may not be available for certain domains (e.g., medical images, low-resource languages) and their performance in approximating human similarity has not been extensively tested. We conducted an evaluation of 611 pre-trained models across three domains - images, audio, video - and found that there is a large gap in performance between human similarity judgments and pre-trained DNNs. To address this gap, we propose a new class of similarity approximation methods based on language. To collect the language data required by these new methods, we also developed and validated a novel adaptive tag collection pipeline. We find that our proposed language-based methods are significantly cheaper, in the number of human judgments, than classical methods, but still improve performance over the DNN-based methods. Finally, we also develop 'stacked' methods that combine language embeddings with DNN embeddings, and find that these consistently provide the best approximations for human similarity across all three of our modalities. Based on the results of this comprehensive study, we provide a concise guide for researchers interested in collecting or approximating human similarity data. To accompany this guide, we also release all of the similarity and language data, a total of 206,339 human judgments, that we collected in our experiments, along with a detailed breakdown of all modeling results. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Deep neural networks

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