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Systematic Evaluation of Cross Population Polygenic Risk Score on Colorectal Cancer

Procedia computer science 2021年 179卷 344-351页

作者： Bharuno Mahesworo Arif Budiarto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The number of findings in cancer genomics research has grown rapidly in the last decade due to the decline in the cost of human sequencing and genotyping. However, the majority of the reported significant marker associated with cancer traits are based on European and East Asian population. Large population such as South Asian and South-East Asian population are under-represented in genomics research. In this study, we explored the possibility of computing a Polygenic Risk Score (PRS) of colorectal cancer on our test sample based on reported significant Single Nucleotide Polymorphism (SNP). The SNPs used to compute the risk score were collected from GWAS Central and GWAS Catalog. Significant SNPs from IC3 study were used as a benchmark. The result shows that calculating colorectal cancer risk score using reported significant marker from different population group is possible. The p-value of our statistic model shows significant differences between case and control group risk score.

关键词： Polygenic Risk Score Cross Population Colorectal Cancer

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A Convolutional Neural Network-based Ancient Sundanese Character Classifier with Data Augmentation

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Procedia computer science 2021年 179卷 195-201页

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

With an increasing interest in the digitization effort of ancient manuscripts, ancient character recognition becomes one of the most important areas in the automated document image analysis. In this regard, we propose a Convolutional Neural Network (CNN)-based classifier to recognize the ancient Sundanese characters obtained from a digital collection of Southeast Asian palm leaf manuscripts. In this work, we utilize two different preprocessing techniques for the dataset. The first technique involves the use of geometric transformations, noise background addition, and brightness adjustment to augment the imbalanced samples to be fed into the classifier. The second technique makes use of the Otsu’s threshold method to binarize the characters and only uses the usual geometric transformations for the data augmentation. The proposed network with different data augmentation processes is trained on the training set and tested on the testing set. Image binarization from the second technique can outperform the performance of the CNN-based classifier upon the first technique by achieving a testing accuracy of 97.74%.

关键词： ancient sundanese characters convolutional neural network data augmentation document image analysis Otsu’s threshold method

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Convolutional Neural Networks for Scops Owl Sound Classification

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Procedia computer science 2021年 179卷 81-87页

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

Adopting a deep learning model into bird sound classification tasks becomes a common practice in order to construct a robust automated bird sound detection system. In this paper, we employ a four-layer Convolutional Neural Network (CNN) formulated to classify different species of Indonesia scops owls based on their vocal sounds. Two widely used representations of an acoustic signal: log-scaled mel-spectrogram and Mel Frequency Cepstral Coefficient (MFCC) are extracted from each sound file and fed into the network separately to compare the model performance with different inputs. A more complex CNN that can simultaneously process the two extracted acoustic representations is proposed to provide a direct comparison with the baseline model. The dual-input network is the well-performing model in our experiment that achieves 97.55% Mean Average Precision (MAP). Meanwhile, the baseline model achieves a MAP score of 94.36% for the mel-spectrogram input and 96.08% for the MFCC input.

关键词： acoustic features bird sound classification convolutional neural network mean average precision scops owl

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Mobile Collaborator System Application for E-commerce Drugstore 8

Mobile Collaborator System Application for E-commerce Drugst...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Oktavia, Tanty Michael Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University School of Information Systems Information Systems Department Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program-Computer Science Computer Science Department Jakarta11530 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665416979

Mostly Drugstores do not have any system that exclusively connect to access E-commerce system because the marketing concept of drugstores still use conventional store to sell their products. According to this situation, this study will propose e-commerce that accomodate drugstore service. For this study, the analysis methodology consists of seeing the most recent business processes in the e-commerce system system and the current situation in drugstore, interviewing the drugstore owner, and doing a literature research. Then, for the drugstore mobile application, create the Unified Modeling Language (UML). The system design is accomplished through the creation of Object-Oriented Detailed Designs in the form of diagrams, databases, and user interfaces. The drugstore mobile application, which includes features such as recommendation product menus, best-selling product menus, promo product menus, transaction cart menus, order tracking menus, order letter menus, and item receive confirmation menus, is the result of this study. © 2021 IEEE.

关键词： User interfaces

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Predicting Human Activity with LSTM Face Detection on Server Surveillance System

Predicting Human Activity with LSTM Face Detection on Server...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Alexander Nurenie Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Indonesia

Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server Systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.

关键词： Training Surveillance Time series analysis programming Behavioral sciences Servers Face detection

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Sentiment Analysis of Big Cities on The Island of Java in Indonesia from Twitter Data as A Recommender System

Sentiment Analysis of Big Cities on The Island of Java in In...

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

作者： Boby Siswanto Ford Lumban Gaol Benefano Soewito Harco Leslie Hendric Spits Warnars BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665427340

Text mining is a data mining technique to find hidden things from a set of data in the form of text. One of the things that can be obtained with text mining is opinion or sentiment, whether it is positive or negative. Positive sentiment is used as a reference for a subject or object from a collection of texts to be recommended. Java Island is the city with the most population in Indonesia with a variety of culinary delights. This study aims to analyze sentiment recommendations on culinary data from food or cuisine in big cities on the island of Java. Four big cities were selected, namely Jakarta, Bandung, Yogyakarta, and Surabaya. The data source is a tweet of culinary arts of the four cities from Twitter in Indonesia. The Sastrawi Library is used as a text mining data processing tool in Indonesia. The results obtained are the majority of positive sentiments from all cities. The cumulative sentiment value of the four cities is 54%, meaning that the big cities on the island of Java have good culinary delights and deserve to be recommended to be enjoyed.

关键词： Text mining Java Sentiment analysis Art Social networking (online) Soft sensors Urban areas

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E-Business Startup: "universia" As University Collaborator System 8

E-Business Startup: "universia" As University Collaborator S...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Oktavia, Tanty Kaslie Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University School of Information Systems Information Systems Department Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program-Computer Science Computer Science Department Jakarta11530 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665416979

According to Covid 19 pandemic every process has to shifting to collaborate with digital technology. One of the field that pushes to shift is Education field. Undergraduate education level, is one of many strategies to improve people competency. In Indonesia, the number of people has steadily increased. However, they have to improve their level of competency to compete with their competitor. According to this condition, many prospective student difficult to find information that their need to decide a place to study. There are many consideration factors that will involved in this decision. Therefore, in this research will build university collaborator system that can support analysis from prospective student perspective to choose education institution. To build this business design this study involve some analysis such as industrial competition will be using Porter Five Force's and supported by Business Model Canvas to define Universia's business model, as well as financial projections for the next seven years. According to the Porter Five Forces model, the information services market around universities still has a lot of room for new entrants. The results of the business model canvas analysis show that, despite being a newcomer to the sector, Universia has a number of value propositions that its competitors do not. Universia's product will be generated in the web based application. The application will be designed using Object Oriented Analysis and Design (OOAD) in conjunction with the Extreme programming approach, which includes steps such as planning, designing, coding, and testing. The result of this study is a system collaborator that can be bridge between prospective student and university, so the prospective student can easily find the information that their need to choose university. © 2021 IEEE.

关键词： Students

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IndoBERT for classifying hate speech in Twitter

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AIP Conference Proceedings 2024年第1期3026卷

作者： Hendri Santosa Fatahillah Rachman Stanley Armando Austen Christianto Abba Suganda Girsang Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Any form of communication that expresses hatred, prejudice, or hostility toward a particular individual or group of people based on attributes such as their race, religion, ethnicity, nationality, gender, sexual orientation, disability, or other protected characteristics is considered hate speech. Hate speech can be verbal, written, or symbolic. Hate speech can take many forms, and it often involves derogatory language, offensive stereotypes, or the incitement of violence or discrimination against the targeted individuals or groups. The content of hate speech is easy found in forum or discussion in social media include twitter. Twitter is a microblogging-based virtual entertainment where clients can peruse and compose text called tweets or tweets. This exploration executes order of disdain discourse in media Twitter utilizing IndoBERT. IndoBERT is the Indonesian form of BERT model utilizing over 220M words. It was a Convolutional Neural Network-based algorithm that had been modified. Th highlight extraction in Transformer isn’t finished by convolution utilizing a part like CNN, however includes an encoder and decoder. The outcome demonstrates IndoBERT’s excellent ability to categorize hate speech.

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Business Start-up: 'CLEVA' application design for employee collaboration training based on android 6

Business Start-up: 'CLEVA' application design for employee c...

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

作者： Oktavia, Tanty Erin Widjaya, Christhoper Arya Hansel Lee, Eugenius Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665449373

E-learning is used as one of the innovations in online learning, but in its implementation e-learning has limitations, especially in terms of flexibility, so that new innovations emerge, namely learning using m-learning because it is more flexible and can be accessed anywhere and anytime. The purpose of writing this study is to analyse problems and business opportunities related to the design of a mobile app-based start-up business in an e-learning system in a company and to design an information system in the form of an Android-based application to answer the analysis of existing problems and opportunities. The methodology used in this study is Rational Unified Process (RUP) with an Object-Oriented Analysis and Design (OOAD) approach which is described by the notation of the Unified Modelling Language (UML). The business analysis used in the writing of this study is the Business Model Canvas (BMC), Marketing Mix, Porter's Five Forces which is complemented by Financial Analysis using the Payback Period (PP), Break Even Point (BEP), and Return on Investment (ROI). In this case, Cleva creates an application system that helps companies carry out training for their employees and helps employees to learn about technical skills and soft skills through mobile-based applications © 2021 IEEE.

关键词： Android (operating system)

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The effects of mobile marketing collaborator on consumers' impulse buying behavior in online marketplace during pandemic 6

The effects of mobile marketing collaborator on consumers' i...

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

作者： Kusuma, Alvita Vania Daniel, Juan Hasri, Raja Alan Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665449373

This study analyzes the influence of the COVID-19 pandemic on impulse buying on the marketplace platform in Jakarta. This research is motivated because the COVID-19 pandemic period lasted quite a long time. Therefore, this research was conducted so that it can be useful for entrepreneurs or marketplace sellers during the COVID-19 pandemic. The research method used is descriptive quantitative method. Data collection techniques using questionnaires and literature study. The techniques used in statistical testing include validity testing, reliability testing, and hypothesis testing. When the research was conducted from September 2020 to January 2021. The results obtained were that there was a relationship between variables, such as mobile marketing, discounts, and lifestyle that could affect impulse buying on the marketplace even though they were moderately related. © 2021 IEEE.

关键词： COVID-19

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