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Trends of Digital Transformation in the Property Management Industry: A Systematic Literature Review

Trends of Digital Transformation in the Property Management ...

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Saputra, Prasetya Cahya Prabowo, Harjanto Ramadhan, Arief Madyatmadja, Evaristus Didik Binus University Binus Graduate Program-Computer Science Computer Science Department Jakarta Indonesia Binus Business School Doctor of Research in Management Binus University Management Department Jakarta Indonesia Telkom University Bandung Indonesia School of Information System Binus University Jakarta Indonesia

ISBN: (纸本)9798350335040

After the Covid 19 pandemic, the property management industry is slowly starting to revive. Even though the occupancy rate has not returned to normal like the Covid19 pandemic, its growth is starting to move in a positive direction. The Covid19 pandemic has provided many lessons for all stakeholders in the property management industry. Digital transformation has become a part that has received important attention in the property management industry worldwide. With digital transformation in the property management industry, all stakeholders are expected to receive various benefits, directly or indirectly. The implementation of digital transformation will involve 6 components, namely: Strategy, Organization, Culture, Customer, People, and Technology. The implementation of digital transformation in the property management industry will require support from many parties, not only internal parties but also external parties. Technological developments, IoT, and demands from residents and property owners are challenges that must be answered by property managers. This systematic literature review will examine articles that have been published in the Scopus database regarding the development of digital transformation in the property management industry from various countries. The purpose of this SLR is to be able to see how the digital transformation trend and the application of its components are in the current property management industry. © 2023 IEEE.

关键词： Apartment Property Management Systematic Literature Review Transformation Digital

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Comparing the Effect of Attention Mechanism and Aspect Embedding on the Performance of Long Short-term Memory Model for Aspect-Based Sentiment Analysis

Comparing the Effect of Attention Mechanism and Aspect Embed...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Murad, Dina Fitria Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

The attention mechanism is one of the key enablers which have positioned transformer models as the state-of-the-art models in Natural Language Processing. By having the attention mechanism, the first version (vanilla) transformer model can retrieve the hidden state but at the same time can maintain a low requirement on the context vector dimension by selectively choosing to which encoder hidden states to give priority attention. The objective of this study is to evaluate the effect of modified Long Short-term Memory models architecture on the model performance as a predictive model. The first model is modified by adding an attention mechanism to its architecture;whilst, the second model is modified by adding additional input to its architecture. The performance of both models is compared by assigning each model the same task: to address the aspect-based sentiment analysis task. The experiment results show that adding an attention mechanism is not significantly increase the performance of the Long Short-term Memory model in comparison to adding input to the same model. In particular, the average training accuracy of the former model is 0.885;whilst, the average training accuracy of the latter model is 0.942. © 2023 IEEE.

关键词： Sentiment analysis

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Critical Factors of Disruptive Innovation: A Review

Critical Factors of Disruptive Innovation: A Review

引用

International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Yoga Prihastomo Harjanto Prabowo Agung Trisetyarso Haryono Soeparno Computer Science Department - BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department - Department of Mathematics Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

People acknowledge innovation as a critical and central factor in increasing economic output and productivity. The phrase disruptive innovation was first familiarized in 1995 and has evolved into a new phenomenon. It exemplifies how a new entrant in a field can eventually compete with the incumbent. Furthermore, many of them may replace them. The study strives to identify the critical factors underlying disruptive innovations. This study employs a systematic literature review strategy and PRISMA meta-analysis. It refers to various reputable data sources from 2013 to 2022. The study included 5,305 articles, analyzed 183 articles that encountered the criteria, and employed 45 appropriate articles. As a result, it encounters six critical factors to disruptive innovation: demand, technology readiness, regulation, labor, external technology, and capital. The proposed formula encapsulates a Success Score for assessing disruptive innovations, integrating various essential factors contributing to success.

关键词： Productivity Industrial electronics Economics Technological innovation Disruptive innovation Systematics Reviews

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Restaurant Customer Feedback Sentiment Analysis using Aspect Embedding Long Short-term Memory Model

Restaurant Customer Feedback Sentiment Analysis using Aspect...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

In the past ten years, there has been a proliferation of unstructured textual data from a variety of fields, allowing for the analysis of the sentiment polarity of the text authors in each of those datasets. To analyze sentiment polarity from unstructured data, machine learning tasks such as aspect-based sentiment analysis are used. The public dataset of restaurant patron feedback was used as the input for this study's empirical results of aspect-based sentiment analysis utilizing the Aspect Embedding Long Short-Term Memory model. The primary experiment's results show that the aspect embedding long short-term memory model can predict aspect sentiment polarization from customer feedback to restaurant service and aspect text representation (aspect text embedding) as input with excellent performance. For example, the Aspect Embedding Long Short-term Memory model, which uses ReLU as its activation function in the Dense layer tends to achieve higher performance (0.97 average accuracies) than the same model equipped with Sigmoid, and Tanh functions which achieve 0.954 and 0.956 average training accuracy respectively. © 2023 IEEE.

关键词： Sentiment analysis

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Aspect-based Sentiment Analysis using Long Short-term Memory Model for Leveraging Restaurant Service Management

Aspect-based Sentiment Analysis using Long Short-term Memory...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

ISBN: (纸本)9798350320954

In general, the hospitality industry has been acknowledged as a major sector that gives a high contribution to economic development in many countries including Indonesia. For that reason, many initiatives have been implemented to help the growth of the hospitality industry in many countries including Indonesia to rebound from the harsh impact of the Covid-19 Pandemic. One such initiative is improving restaurant services as the main sector of the hospitality industry. This paper presents empirical results of sentiment analysis as a means to assess the quality of restaurant services as the first step to improving service quality. In particular, this study explores the aspect-based sentiment analysis method to identify some aspects of restaurant service which need improvement by learning the polarity of customers toward the restaurant services without having to meet the customers directly. By using the aspect-based sentiment analysis method, the customer sentiments comprising opinions, sentiments, evaluations, attitudes, and emotions from restaurant service can be analyzed using customers' online reviews as input. The main experiment findings showed that the Long Short-term Memory model can achieve high performance in predicting aspect polarization in restaurant service reviews. Other findings suggest that Sigmoid as an activation function achieved 0.97 average training accuracy and 0.69 average testing accuracy giving a better performance to the model in comparison to ReLU, Tanh, and ELU activation functions. © 2023 IEEE.

关键词： Sentiment analysis

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A Systematic Literature Review of Recent Trends and Challenges in Digital Twin Implementation 10

A Systematic Literature Review of Recent Trends and Challeng...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Arin, Ikrar Adinata Meyliana Warnars, Harco Leslie Hendric Spits Murad, Dina Fitria Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350339543

This paper reviews the latest trends and challenges in implementing digital twin technology. A digital twin is a tool used in various industries to improve efficiency, optimize processes, and enable advanced analysis. The review involved searching major research databases and search engines for articles published between 2018 and 2023. The findings reveal several important trends, including the development of different types of digital twin dimensions, each with its own advantages and limitations. The benefits of digital twin implementation include improved decision-making, increased productivity, and operational efficiency. However, there are challenges, such as data integration, security and privacy concerns, a lack of standardization, and the need for experts to effectively design and operate digital twins. The implications of these trends and challenges are discussed regarding their impact on the successful adoption and implementation of digital twin technology. The review also highlights the need to address these challenges and explore new approaches for maximizing the benefits of digital twin technology. Overall, this comprehensive review is a valuable resource for researchers, practitioners, and organizations seeking to understand the current landscape, identify areas for improvement, and make informed decisions when implementing digital twin technology. © 2023 IEEE.

关键词： Decision making

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The Impact of Recommendation System Provided Based on Online Learning Readiness in the MOOC Environment 24

The Impact of Recommendation System Provided Based on Online...

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24th International Arab Conference on Information Technology, ACIT 2023

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

ISBN: (纸本)9798350384307

MOOCs are one of the developments or impacts of e-learning or online learning. Massive Open Online Course (MOOC) is one of the choices for students to increase their knowledge through non-formal channels. Our research aims to determine the level of readiness of students regarding MOOC learning and opportunities for utilizing recommendation systems in students' decision-making regarding selecting MOOCs that suit their interests and needs. We conducted a survey regarding the constraints and needs of learner readiness through 5 aspects, namely 1) technical aspect, (2) social aspect, (3) communication aspect, (4) lecture readiness aspect, and (5) online discussion. The results of this research show that the main challenges that MOOC participants may face in a MOOC environment are Technical Aspects (28.25%), Social Aspects (21.47%), and Communication Aspects (18.93%). Meanwhile, the greatest strength of online learning in the MOOC environment is the social aspect (21%), the most influential and continued with technical and other aspects. Meanwhile, the lecture readiness aspect is quite good, and the online discussion aspect is the biggest aspect that influences the success of MOOC learning. Other results of this research indicate the importance of a recommendation system for MOOC students to make it easier for students to choose the right MOOC according to their interests and needs. © 2023 IEEE.

关键词： MOOC personalization Recommendation system student competency Technology Enhanced Learning

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Sentiment Analysis of Big Cities on the Island of Java in Indonesia from Twitter Data as A Recommender System 3

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

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3rd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021

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

ISBN: (纸本)9781665427333

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. © 2021 IEEE.

关键词： Sentiment analysis

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Drug Demand Prediction Based on Epidemiology Factors Using Random Forest

Drug Demand Prediction Based on Epidemiology Factors Using R...

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Advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Gunadi Emmanuel Yulyani Arifin Ilvico Sonata Muhammad Zarlis Doctor of Computer Science Program Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Management Department Binus Graduate Program Bina Nusantara University Jakarta Indonesia

The characteristics of the disease that spreads quickly, the number of sufferers, and the severity of sufferers of Coronavirus Disease 2019 are components of uncertainty during the pandemic. In an uncertain situation, prediction models for the need for drugs and medical devices are of great concern to policymakers in government, drug manufacturers, distributors, and pharmaceutical installation managers to maintain drug availability. Drug need prediction models that rely on historical data components on drug use are no longer reliable. Learning from the COVID-19 case, epidemiological variables correlate with predicting drug demand. This research includes data on ten major diseases in private hospital units for 2017–2022 to complete historical data on drug use. This study implements the Random Forest algorithm. The research method uses literature studies and processing field data from pharmaceutical installations. The analysis process uses KNIME software. The level of accuracy in predicting drug demand from historical drug use data was 77.272%, increasing to 81.818% with a model for predicting drug demand based on consumption cycles and classification of drug therapy groups. Furthermore, predictions of drug demand can consider variables recorded in medical records related to the seasonal frequency of diseases.

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Challenges and Recommendations for Improving Logistic Skills Through Simulation-Based Learning Technology

Challenges and Recommendations for Improving Logistic Skills...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Tamsir Hasudungan Sirait Meyliana Yulyani Arifin Budi Yulianto Binus Graduate Program Doctor of Computer Scienceept Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Simulation-based learning is one of the most powerful tools to support the educational process. This learning offers an opportunity to learn based on experiences that are very similar to real-world environments. In the context of logistics, which can be considered a complex system, significant efforts are required to enhance students' understanding and practical skills. For that reason, simulations are used so that benefits such as skills and knowledge in the field of logistics can be achieved. However, there are currently several challenges in developing simulation-based learning in higher education. These challenges include the need for simulations that are appropriate for the current learning context, where the students are generally from Generation Z, and how to improve their learning quality in the era of Industry 4.0. To address these challenges, more suitable simulations need to be developed, and higher education institutions must carefully examine how to develop them, including identifying the essential components in the development of simulations for logistics learning. In this research, a systematic literature review using the PRISMA method and also creating a conceptual model of simulation that better meets the current needs of logistics education will be proposed. The results of this research can be used as a basis for developing updated simulations to better prepare students for the challenges of Industry 4.0.

关键词： Analytical models Computational modeling Education Prototypes Data models Fourth Industrial Revolution Proposals Logistics Systematic literature review Investment

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