检索结果-内蒙古大学图书馆

7th International Conference on ICT for Smart Society, ICISS 2020

作者： Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Syahir, Arsyan Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Sports science can have a significant and positive impact on coaches and athletes, as well as for the professional sports organization. According to this situation, more and more organizations are competing to implement sports science as the monitoring systems. Along with the development of technology, it becomes easier for humans to carry out their activities, including communication with each other. Advances in the field of communication technologies have led to the use of social networks to support all activity. With social media, all kinds of information that people want to search for can be found easily. Social networks have their uniqueness in their characteristics. Like LINE has free calling and video calling features, while Instagram has features like Instagram Story, where users can document their activities in the form of photos or recordings. By all the characteristics of social networks, it will support the activity of sports science. The methodology of this research focused on quantitative research based on the T-test approach. The result shows that social media involvement can significantly support collaboration in sports science, so it can increase people's motivation to interact with other participants in sport organizations. © 2020 IEEE.

关键词： Sports

来源：评论

学校读者我要写书评

暂无评论

Information system design on tobacco addiction measurement protocol for smokers 6

Information system design on tobacco addiction measurement p...

引用

6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Mahesworo, Bharuno Budiarto, Arif Parung, Deby Erina Amirulloh, Dian Simon, Simon Pardamean, Bens Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Genetics Indonesia Product Department Jakarta Indonesia Genetics Indonesia Laboratory Department Jakarta Indonesia Board of Director Genetics Indonesia Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Smoking habits in Indonesia has become a national health burden despite their huge contribution to national economics. With easy access to inexpensive cigarettes in Indonesia, the percentage of teenage smokers in Indonesia is very high. More than 70% of these smokers have tried to quit, but only approximately 5% have succeeded. These facts show the importance of developing precision stop smoking treatments. We proposed an information system design of smoking measurement questionnaire that is able to describe the characteristic differences between smokers and former smokers. This information system design was implemented in genetic service company's web-based application and is expected to help us extract the necessary variables to help us finding biomarkers associated with smoking cessation in our future study. The information system design has been developed by combining and modifying questionnaires from previous studies. The focus is on strategic variables and the compactness of the questionnaire, and it can be used by other researchers to describe the smoking dependency of their samples. The information system design has been tested on pilot respondents/customers and shows results that meet our expectations. © 2021 IEEE.

关键词： Tobacco

来源：评论

学校读者我要写书评

暂无评论

Deep Learning as a Vector Embedding Model for Customer Churn

引用

Procedia computer science 2021年 179卷 624-631页

作者： Tjeng Wawan Cenggoro Raditya Ayu Wirastari Edy Rudianto Mochamad Ilham Mohadi Dinne Ratj Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

To face the tight competition in the telecommunication industry, it is important to minimize the rate of customers stopping their service subscription, which is known as customer churn. For that goal, an explainable predictive customer churn model is an essential tool to be owned by a telecommunication provider. In this paper, we developed the explainable model by utilizing the concept of vector embedding in Deep Learning. We show that the model can reveal churning customers that can potentially be converted back to use the previous telecommunication service. The generated vectors are also highly discriminative between the churning and loyal customers, which enable the developed models to be highly predictive for determining whether a customer would cease his/her service subscription or not. The best model in our experiment achieved a predictive performance of 81.16%, measured by the F1 Score. Further analysis on the clusters similarity and t-SNE plot also confirmed that the generated vectors are discriminative for churn prediction.

关键词： customer churn customer behavior deep learning vector embedding representation learning

来源：评论

学校读者我要写书评

暂无评论

A Bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

引用

International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Rudi Nirwantono Khoirunnisa Assidqi Alam Ahmad Hidayat Joko Pebrianto Trinugroho Bens Pardamean Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Food Biotechnology Research Center Bina Nusantara University Jakarta Indonesia Mathematics Departement School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Design of Polygenic Risk Model with Deep Learning for Colorectal Cancer in Multiethnic Indonesians

引用

Procedia computer science 2021年 179卷 632-639页

作者： Steven Amadeus Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Recently, health management is emerging and attract attention to how to provide better prognostication and health management systems. The challenges in the prognostication are how to develop a model that can self-learn the prognostication features and how to get a high accuracy prediction. Prognostication in health disease involves SNPs which is a genetic marker. In this paper, we propose a polygenic risk model using deep learning: Transformer with self-attention mechanism and DeepLIFT. The use of these deep learning model allows us to predict the risk of colorectal cancer and see the correlation between SNPs.

关键词： Prognostication Polygenic Risk Model Transformer Model DeepLIFT Colorectal Cancer

来源：评论

学校读者我要写书评

暂无评论

Code-Mixed Sentiment Analysis Using Machine Learning Approach – A Systematic Literature Review

Code-Mixed Sentiment Analysis Using Machine Learning Approac...

引用

International Conference on Informatics and Computational sciences (ICICoS)

作者： Cuk Tho Harco Leslie Hendric Spits Warnars Benfano Soewito Ford Lumban Gaol BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia 11480

ISBN: (数字)9781728195261

ISBN: (纸本)9781728195278

Code-mixed language is ubiquitous. Having been commonly practiced among bilingual communities, code-mixed language has emerged as a common language among social media users. Despite its popularity, the analysis of a code-mixed text is challenging as the text does not typically comply with the monolingual grammar. Therefore, the popularity of social media in the past ten years has raised wide attention to develop methods for analyzing code-mixed text such as extracting popularity sentiment from the text. Machine learning-based classifier such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression have been widely used to analyze the sentiment. This paper intends to further explore machine learning classifiers, their performances, variables, and most common classifiers for the code-mixed sentiment analysis. Prisma Methodology was used in this paper, extracting 12 from 230 papers that met predefined required criteria, including publication year within the last 5 years. Our findings suggested that the most common classifiers found in the papers were Support Vector Machine, Naïve Bayes, and Logistic Regression. By using the accuracy and F1 as the performance measures, the Support Vector Machine exhibited a better performance compared to Naïve Bayes and Logistic Regression. Thus, this study supported the use of Support Vector Machine, Naïve Bayes and Logistic Regression as the main classifiers for the code-mixed sentiment analysis.

关键词： Support vector machines Sentiment analysis Systematics Social networking (online) Machine learning Regression tree analysis Logistics

来源：评论

学校读者我要写书评

暂无评论

IoT for Water Quality Categorization

IoT for Water Quality Categorization

引用

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

E-payment for Jakarta Smart Public Transportation, Using the Point System for E-Commerce 2

E-payment for Jakarta Smart Public Transportation, Using the...

引用

2nd International Conference on computer, science, Engineering, and Technology, ICComSET 2019

作者： Anwar, Nizirwan Rasjidin, Roesfiansjah Najoan, Daniel Stephanus Rolando, Christopher Tamimmanar Warnars, Harco Leslie Hendric Spits Faculty of Computer Science Esa Unggul University Jakarta11510 Indonesia Faculty of Engineering Esa Unggul University Jakarta11510 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The ease of using transportation is one of the most critical things in the city with a significant population like Jakarta. The growth of the population in Jakarta is increased rapidly. The wage that Many transportations are causing a traffic jam in Jakarta. The government suggests that people use public transportation for their mobility. However, people choose to use their vehicles rather than public transportation. The main reason is that public transportation cannot guarantee the arriving time, whether it is on time or not. Many people move on to online transportation services. However, the massive growth of online transportation is still a contradiction as public transportation. People in Jakarta need faster mobility to go. This research is trying to make a system for public transport without any delay and hard to use it. This problem can be solved by building a new system. This system required e-wallet for payment in public transportation. This E-Wallet will search the possible route from the nearest location to the destination - only the public transport where there is a station that can use it. By using QR-Code generated by e-wallet on the mobile phone, people can scan it directly to a machine located in the station. This method will make a transaction faster than ever. Moreover, people can enjoy another feature like cashback and redeem the prize by a point system. In this era, e-commerce is dominating the market for purchasing something. This is also the attraction of the system. This system is also required excellent facilities from the government so that people can enjoy it. © Published under licence by IOP Publishing Ltd.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

引用

International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Alam Ahmad Hidayat Rudi Nirwantono Mahmud Isnan Joko Pebrianto Trinugroho Bens Pardamean Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department Bioinformatics and Data Science Research Center BINUS Graduate Program Jakarta Indonesia

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Social Network Analysis Algorithms, Techniques and Methods

Social Network Analysis Algorithms, Techniques and Methods

引用

International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Lidia Sandra Agung Trisetyarso Arief Ramadhan Edi Abdurachnan Ford Lumbangaol Sani Muhamad Isa Psychology Department Krida Wacana Christian University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Trisakti Institute of Trasportation and Logistic Trisakti University Jakarta Indonesia

ISBN: (纸本)9781665499705

Understanding how social structures with the use of a network have been an active field of study for academics in the past five years alone. The need to properly comprehends how Social Network Analysis (SNA) is being studied grows more and more in recent years. In this article, we propose a Systematic Literature Review (SLR) to the SNA to see how the algorithms, techniques, and methods are used also discuss their findings. We select thirty-one research studies on SNA. We found different algorithms and techniques that are being used. It is found that the selected research could be categorized into five different main topics which is academic, health, social media, communication, and technology. From all of the research paper discussed, it is also found that many algorithms and techniques are being used to enhanced the SNA, most of them are being machine learning algorithm such as Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression, K Nearest Neighbor, and Whale Optimization Algorithm. While the common features of the datasets used in the research comes as different arrays of user information from social media platforms, Tweets and posts from multiple platforms, also a photographic input such as self-images, portraits, and context related pictures. This article will serve as a single reference for future researchers to the discovery of the latest SNA findings.

关键词： Support vector machines Machine learning algorithms Systematics Mechatronics Social networking (online) Bibliographies Regression tree analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：