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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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Rapid Monitoring for Flood Inundation Detection-Based Sentinel-1 SAR Image Data and Exposure Assessment in Urban Building Area: Makassar City, Indonesia 3rd

Rapid Monitoring for Flood Inundation Detection-Based Sentin...

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3rd International Conference on Radioscience, Equatorial Atmospheric science and Environment, INCREASE 2023

作者： Sekarjati, Dhanu Hanif, Muhammad Komala, Siffa Anastasya Giofandi, Eggy Arya Putri, Febita Safera Bunga, Brigitta Audrynne Rombe Putklang, Wasana Wilis, Ratna Geoinformatics Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Department of Geophysics and Meteorology Faculty of Mathematics and Natural Science IPB University Bogor Indonesia Graduate Program of Regional Planning Science Faculty of Agriculture IPB University Bogor Indonesia Department of Geography Faculty of Social Science Universitas Negeri Padang Padang Indonesia

ISBN: (纸本)9789819707393

Real-time monitoring remains a challenge in providing accurate information, especially in the case of urban flood inundation disasters. The accuracy of data for disaster risk reduction has a positive impact on reducing the loss of livelihood or the regional economy. Understanding flood levels and their impact on urban areas can be crucial information for flood mitigation and effective urban planning. The research aims to conduct a rapid monitoring investigation for flood inundation detection in Makassar City, Indonesia, using Sentinel-1 SAR image data and assess the exposure of urban building areas to flooding. By identifying flood inundation areas using backscatter values, it was determined that Tamalanrea District exhibits a high density and is prone to flooding as a result of urban expansion. Additionally, Biringkanaya District, which is adjacent to Tamalanrea, was also impacted by the flood. The most severely affected area was found to be Tamalanrea District, with an inundation area covering 73,114 m2. It also recorded the highest extent of built-up area affected by flooding, reaching 29,475 m2. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Floods

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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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An innovative artificial intelligence-based visualization and rendering for e-commerce startup booth design

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Procedia computer science 2024年 245卷 147-154页

作者： Calista Kalleya Andi Pramono Chelsea Putri Angelina Wilbert Alvin Cuarista Riefky Prabowo Fairuz Iqbal Maulana Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Indonesia Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Marketplace development is increasing, as is the number of people with active internet access. Unive is a startup that runs an e-commerce platform connecting Student Activity Units with consumers. Focuses on fostering student entrepreneurship through practical business experience. For its development, this startup needs an interior for business escalation. Designers need software integrated with artificial intelligence (AI) in interior design. This study explores the integration of AI-powered rendering tools in the design of e-commerce platforms, focusing on how these tools enhance the user experience and satisfaction. The research employs a mixed-method approach, combining qualitative and quantitative analyses. The author collected data through user surveys, expert interviews, and performance metrics of AI rendering tools. The author based the evaluation criteria on adherence to interior design principles and the quality of rendering results. Findings indicate that selecting furniture and supporting components that adhere to interior design principles results in optimal rendering outcomes. These outcomes are close to user expectations, significantly improving user engagement and satisfaction. Detailed analysis shows the effectiveness of AI rendering in various design scenarios. The discussion highlights the implications of integrating AI rendering tools in e-commerce platforms. It addresses the potential of these tools to revolutionize user interaction and provides insights into future developments and applications in the field. This study demonstrates the value of AI-powered rendering tools in enhancing e-commerce platform design. By adhering to interior design principles, these tools can achieve rendering results that meet user expectations, ultimately leading to improved user experiences.

关键词： Interior Design Artificial Intelligence Prototype Render Start-up

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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Personalized Recommendation System for Online Learning: An Opportunity

Personalized Recommendation System for Online Learning: An O...

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International Conference on Business and Industrial Research (ICBIR)

作者： Dina Fitria Murad Mohamad Toha Hendra Mayatopani Bambang Dwi Wijanarko Yaya Heryadi Meta Amalya Dewi Riyan Leandros Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Universitas Terbuka Indonesia Department of Information Systems Pradita University Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

In the world of education, learning management systems are currently widely used. The massively open online courses are accessible either via the web or mobile. The Learning Management System (LMS) is one of the learning media that is widely used today in online learning. An LMS with the use of a personalized recommendation system can provide items tailored to the interests and behavior of its users. Items in the recommendation system can be in the form of recommendations for learning models, ways of learning, learning patterns, or learning stages according to the characteristics and profile of the user. The purpose of this study is to identify opportunities for better online learning by utilizing a customized recommendation system based on the history of user interaction with the system, using several factors as determinants or variables. The method used is a review conducted from several stages. The result of this is knowing the factors that influence the user, and the opportunities that exist in the system recommendations on personalized development.

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Model of Customer Relationship Management Systems Evaluation Using Factor Analysis 8

Model of Customer Relationship Management Systems Evaluation...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Sardjono, Wahyu Kristin, Desi Maya Putra, Gustian Rama Binus Graduate Program - Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Pakuan University Computer Science Study Program Faculty of Mathematics & Natural Sciences Bogor Indonesia

ISBN: (纸本)9798350326093

Relationship with customers is one of the most crucial factors in business continuity and development. Many current businesses have started delving into Customer Relationship Management (CRM) to establish and maintain their sales and customer relationships. This research was conducted on one retail company that implements CRM but experiencing a gap in their unit sales performance between their targeted sales and the realization. This study aims to analyze the gaps causing factors using Factor Analysis Method, constructing a suitable regression model, and proposing the company's necessary strategy. The research was conducted quantitatively using a questionnaire from 264 respondents. The result of this study showed that there are five causing factors which are CRM Reliability, Underutilization of CRM, CRM Capability, CRM Unpreparedness, and Customer Relation Quality, as well as forming a regression model of the company's CRM performance. © 2023 IEEE.

关键词： Sales

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Recurrent Neural Network-based Models as Bahasa Indonesia-Sundanese Language Neural Machine Translator

Recurrent Neural Network-based Models as Bahasa Indonesia-Su...

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

作者： Bambang Dwi Wijanarko Yaya Heryadi Dina Fitria Murad Cuk Tho Kiyota Hashimoto Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science 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

Sequential data are available in many domains including weather observation data, stock price, gene sequence, and many others. Sequential data processing is very instrumental as a solution to a variety of downstream tasks in Natural Language Processing such as language translation. Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. However, language translation is not an easy computation task when there is a language-resource gap. This paper presents some empirical results from the exploration of the Recurrent Neural Network (RNN), the Gated Recurrent Unit (GRU), the Long Short-term Memory (LSTM), and the Bidirectional Long Short-term Memory (Bi-LSTM) models as machine translation models from Bahasa Indonesia to the Sundanese language. These empirical results show that the Bi-LSTM achieved the highest performance (0.95 average training accuracy and 0.92 average testings BLEU score) compared to the RNN (0.93 average training accuracy and 0.91 average testings BLEU score), the GRU (0.88 average training accuracy and 0.89 average testings BLEU score), and the LSTM (0.92 average training accuracy and 0.91 average testings BLEU score) models. These results validate some previously reported studies that claim the Bi-LSTM model potentially outperforms the other recurrent neural network-based models when it is used to process sequence datasets.

关键词： Training Recurrent neural networks Computational modeling Instruments Data processing Machine translation Task analysis

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Analysis of Factors Influencing Decisions to Purchase Airline Tickets Online During the Covid-19 Pandemic

Analysis of Factors Influencing Decisions to Purchase Airlin...

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

作者： Yulius Lie Anzaludin Samsinga Perbangsa Bens Pardamean Information System 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

The background of this research is the condition of the covid-19 pandemic which has an impact on online ticket sales. Meanwhile, when future of covid-19 pandemic is starting to become clearer, we are going to have a lot more infections but hopefully a lot fewer hospitalizations, people began to travel long distances that had been delayed by implementing health protocols. Airfare competition can influence the decision to buy airline tickets. Various things have been done to attract the attention of buyers. This research is a non-experimental quantitative research. Population in this research was people who have purchased plane tickets online during the covid-19 pandemic, and the sample were 113 respondents age between 15 and 64. Sampling technique used was simple random sampling. Analysis of the data used in this study using multicollinearity, heteroscedasticity, multiple linear, T-test, F-test, and coefficient of determination test. It was concluded that price, promotion, and lifestyle variables can influence online purchasing decisions.

关键词： COVID-19 computer science Protocols Pandemics Sociology Statistics Information technology

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Development of Key Performance Indicators in e-Learning Implementation in Higher Education Institutions 8

Development of Key Performance Indicators in e-Learning Impl...

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8th International Conference on Information Management and Technology, ICIMTech 2023

ISBN: (纸本)9798350326093

Companies or organizations usually use key performance indicators (KPI) as indicators to determine their performance. The company's performance achievements are reflected in the set indicators that will describe the overall performance, as well as higher education institutions can also set key performance indicators, to see their performance achievements. The use of e-learning as a tool to support teaching and learning processes to be more effective and efficient is an example of which performance needs to be measured. This article discusses how the process of developing key performance indicators is carried out through the stages of designing questionnaires and distributing questionnaires to students, data is processed using the method of factor analysis to obtain the main indicators determining performance and the method of regression analysis is also used to build performance models. obtain KPI determinants, and then develop KPI models. The results show that there are indicators of sharing knowledge, managing knowledge, understanding knowledge, and utilization knowledge as performance measurement indicators. © 2023 IEEE.

关键词： Regression analysis

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