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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Yaya Heryadi Bambang Dwi Wijanarko Dina Fitria Murad Cuk Tho Kiyota Hashimoto Computer Science Department BINUS Graduate Program – Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Prince of Songkla University Phuket Thailand

Communication is a form of translation that human learns naturally since early childhood. Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. One example of high-impact language translation is translating health protocols from the World Health Organization to many national languages to prevent the spread of the Covid-19 virus. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of the Long Short-term Memory model as a machine translation involving Bahasa Indonesia and the Sundanese languages. Performance of the Sundanese language to Indonesian language translation is 0.93 average training and validation accuracy and 0.91 average testings BLEU score. Whilst, Indonesian language to Sundanese language translation is 0.92 average training and validation accuracy and 0.88 average testings BLEU score. Both models achieve 0.39 average training loss and 0.38 average validation loss.

关键词： Training Pediatrics Protocols Computational modeling Instruments Organizations Machine translation

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Lightweight End to end Pose-Robust face recognition system with Deep Residual Equivariant Mapping

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Procedia computer science 2021年 179卷 648-655页

作者： Kevin William Gunawan Noptovius Halimawan Suharjito Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta 11480 Indonesia

In the face recognition field of study, pose-robustness and lightness of a model are few of the critical improvement factors of face recognition. However, these fields are still providing challenge for researchers. Even though pose variance is proven to drop the accuracy of deep learning-based models, pose-robustness is not studied often in lightweight face recognition models. Existing pose-robust models have heavier implementation costs compared to lightweight models. We propose a deep learning architecture that implements Deep Residual Equivariant Mapping (DREAM) to improve pose-robustness of a lightweight MobileFaceNets model as a solution to the underlying issue. In the proposed model, the DREAM block is stitched to the MobileFaceNets stem CNN architecture. The evaluation process compares the speed, file size, and accuracy on pose diverse datasets, such as the CFP and IJB-A dataset. The evaluation results of the proposed model show an accuracy improvement of 0.07% with verification speed difference of 0.17 ms. Both of the results show a better performance compared to the baseline naive model.

关键词： face recognition pose-robust lightweight deep learning

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Fundamental Components of Microlearning for Sustainable Quality Education: A Systematic Literature Review

Fundamental Components of Microlearning for Sustainable Qual...

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International Conference on Information Management and Technology (ICIMTech)

作者： Reza Rahutomo Siti Elda Hiererra Yulyani Arifin Muhamad Nanang Suprayogi Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta Indonesia

E-learning was disrupted by a new concept of education named microlearning that carried various digital learning materials in convenient learning paths that were customizable according to users' necessity by using digital application. The competitive advantage of microlearning has been published but caused ubiquitous bias definition. This study aims to identify and analyze fundamental components of existing microlearning research and implementations in e-learning platforms. 42 research articles from SCOPUS database were collected from 2018 to 2022. They were reviewed to discover fundamental components of microlearning that must be considered in its implementations. The result of this study uncovered “Content's variety” the most fundamental components. In contrast, this study finds that advance technology is the least discussed but required in supporting specific usage of microlearning. Limitation of this study lies on the determination of database and keyword usage and the methodology. This study recommends to consider combination of microlearning components to concept a specialized microlearning for sustainable quality education.

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Performance Analysis of Distributed Database System in Cloud Computing Environment

Performance Analysis of Distributed Database System in Cloud...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Nicholas Prasetio Daniel Ferdinand Brandon Christopher Wijaya Maria Susan Anggreainy Afdhal Kumiawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Computer Science Bina Nusantara University Jakarta Indonesia BINUS Graduate Program School of Computer Science Bina Nusantara University Jakarta Indonesia

This research discusses the performance evaluation of distributed database systems in a cloud computing environment Cloud computing environments allow data and applications to be stored and deployed on infrastructure located in different parts of the world. However, the use of distributed database systems in cloud computing environments can cause performance issues, such as complex data access and factors such as network latency, security, and scalability that affect system performance. Therefore, performance evaluation of distributed database systems is necessary to ensure effective data management across the infrastructure. The purpose of this research is to measure and understand the performance of distributed database systems in a cloud computing environment. This is important because proper performance evaluation is needed to ensure distributed database systems can operate effectively and efficiently in such environments. This research will analyze the features of distributed database systems, factors that affect performance, how to measure and compare system performance, and how to improve system performance. Analyzing the performance of distributed database systems in a cloud computing environment can help users choose the most appropriate and efficient cloud computing platform for their business needs and improve operational efficiency.

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Placement of information systems as a strategic tool for achieving company objectives

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Procedia computer science 2024年 234卷 1037-1042页

作者： Wahyu Sardjono Widhilaga Gia Perdana Achmad Cholidin Guntur Salim Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Post Graduate Program of Management Science State University of Jakarta Jl. R.Mangun Muka Raya No.11 Jakarta Timur Jakarta 13220 Indonesia Law Study Program Faculty of Law University of Muhammadiyah Jakarta Indonesia Post Graduate Program Faculty of Economic Science University of Trisakti Jl. Kyai Tapa No. 1 Grogol Jakarta 11440 Indonesia

The information system is a strategic tool for the goals but is considered not optimal because there are indications that long-term target strategies have not been achieved. it is necessary to identify the factors that influence this success so that a model can be built that describes the achievement of the company's strategic objectives. The research was conducted with a quantitative approach through an analysis of data using factor analysis and regression analysis. The results of the study show three factors that influence strategic goals in companies, namely Leadership & Organizational, Goals Visualization, and Enterprise-wide Awareness.

关键词： information system strategic tools company achievement factor analysis

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A literature review of crowd-counting system on convolutional neural network 2

A literature review of crowd-counting system on convolutiona...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Chrysler, A. Gunarso, R. Puteri, T. Warnars, H.L.H.S. 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

With the proliferation usage of video surveillance for safety, traffic control, and privacy purposes and with the constant growth of population, it is important to keep monitoring using Closed-Circuit Television (CCTV). With new upcoming developed technologies, new systems and algorithms are introduced and implemented to the crowd counting system today retrieving live video surveillance from the CCTV. However, recent studies show that there are some challenges still faced regarding the crowd counting system which uses the density estimation. The problems that occurred have resulted from the inaccuracy of the system that is caused by several factors. Factors such as the perspective distortion which is caused by the lack of data training and the method such as face detection is an ineffective method to determine the population density. Studies proposed have projected the idea of developing a more robust crowd counting methodology by implementing crowd counting by detection, clustering, and regression. Implementing these methods using the Convolutional Neural Network (CNN) will better the result of the detection since in CNN the image can be inputted and it will undergo several layers which will result in the system being able to differentiate one image from the other. With CNN the process of crowd counting will be able to be more advanced. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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AI-Based Feature Extraction and Cosine Similarity for Automation of Student Learning Assessment

AI-Based Feature Extraction and Cosine Similarity for Automa...

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Information Technology (ACIT)

作者： Bambang Dwi Wijanarko Bachtiar Rosilah Binti Hassan Dina Fitria Murad Ridho Bramulya Ihsan Yaya Heryadi Engkos Achmad Kuncoro Eka Julianti Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Master of English Education Graduate School Universitas Terbuka Jakarta Indonesia Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Manajemen Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Information System Department Universitas Terbuka Jakarta Indonesia

This research is ongoing research into the student learning process which aims to develop artificial intelligence-based technology to calculate essay exam scores automatically, based on the textual proximity of students' answers to the answer key. This research uses an approach based on natural language processing (NLP) and text analysis. Feature extraction was carried out using the BERT model and similarity comparison using the Cosine distance method. Model evaluation was carried out using sensitivity and specificity tests. The model evaluation results show a sensitivity of 98.21%, which means this assessment model is able to detect positive cases well.

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Indonesian-Sundanese Language Machine Translation using Bidirectional Long Short-term Memory Model

Indonesian-Sundanese Language Machine Translation using Bidi...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Yaya Heryadi Bambang Dwi Wijanarko Dina Fitria Murad Cuk Tho Kiyota Hashimoto Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Prince of Songkla University Phuket Thailand

Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of two models: the Long Short-term Memory and the Bidirectional Long Short-term Memory models as machine language translation models involving Bahasa Indonesia and the Sundanese language. The empiric results showed that the Bidirectional Long Short-term Memory model achieves higher performance as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.95 and 0.95 average training accuracy respectively; and 0.90 and 0.89 average testing BLEU scores respectively) than the Long Short-term Memory model as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.93 and 0.92 average training accuracy respectively; and 0.91 and 0.88 average testing BLEU scores). These results validate some previously reported studies that claim the Bidirectional Long Short-term Memory model potentially outperform the Long Short-term Memory model when it is used to process a sequence dataset.

关键词： Training computer science Computational modeling Instruments Machine translation Task analysis Information technology

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Utilizing Semi-supervised Method in Predicting BRCA1 Pathogenicity Variants

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Procedia computer science 2023年 227卷 36-45页

作者： Alam Ahmad Hidayat Joko Pebrianto Trinugroho Rudi Nirwantono Digdo Sudigyo Bens Pardamean Department of Mathematics School of Computer Science Bina Nusantara University Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia 11480

Quantifying the effect of mutations in the BRCA1 gene is useful for understanding their clinical consequences on breast cancer. Machine learning models can be applied to predict the landscape of protein variant effects that might not be always accessible by experiments. In this work, we propose a simple semi-supervised learning method using a Gaussian mixture model to predict ∼90% of the unlabeled missense variants of the BRCA1 gene collected from the ClinVar database. High-quality embeddings are used as a feature of the protein sequences, extracted using the latest pre-trained transformer-based language protein model. A statistical test show that the protein embeddings are effective and robust for predicting pathogenicity. Further, the lower representations of the features are then fed into the semi-supervised model. The prediction performance of the model only for the labeled testing data achieves an AUC score and an accuracy of 79.27% and 71.58%, respectively. Using our defined pathogenic probability score, we find that ∼94% of variants in our unlabeled dataset are well-separated into either benign or pathogenic classes according to that scoring. Our scores obtain a moderate Spearman rank correlation with the results of established unsupervised variant effect models. Finally, our approach can potentially be developed for more accurate and biologically reliable predictions of the variant effects.

关键词： embeddings mutation effect language model protein sequence semi-supervised learning

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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