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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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

Aggregating Structural Features for Computational Analysis o...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

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. © 2023 IEEE.

关键词： Proteins

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Single and Multi-Documents Text Summarization Technologies for Natural Language Processing: a Systematic Review on Method and Dataset

Single and Multi-Documents Text Summarization Technologies f...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Arief Agus Sukmandhani Arief Ramadhan Edi Abdurachman Agung Trisetyarso Computer Science Department Binus Online Learning Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

The summarization technologies have been increasingly used in recent decades. Those technologies are a very important part of emerging topics in computer science and engineering, such that Natural Language Processing (NLP). Several methods have been used for analysis to get good summary results. There are two types of document summaries: single document summaries and multi-document summaries. Single document summaries aim to extract information from a single document to get new and relevant summary information, while multi-document summaries extract information from multiple documents. This study focuses on the activities of the semantic literature in previous studies to obtain the basis of the widely used base methods and data sets used in this study. Data was collected from Scopus publication sources from 2019 until 2022 Q2 for analysis. Researchers use guidelines from the semantic literature method by using the basis of Kitchenham and Charters as a reference in its design. In this study, there were forty-eight articles obtained from the filtering results of several criteria used, including exclusion and quality assessment.

关键词： computer science Systematics Filtering Semantics Redundancy Bit error rate Natural language processing

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Classifying Tweet Sentiment Analysis on Airlines using Random Forest Classifier, Naïve Bayes Classifier and K-Nearest Neighbors Classifier Algorithm

Classifying Tweet Sentiment Analysis on Airlines using Rando...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Alexander, Ivan Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

Opinion has always affected businesses and individuals especially from the Public. People react through social media and spread it incompletely. The situation was then accepted as public opinion. There are three categories of opinion i.e., positive, negative, and neutral. To investigate public opinion on a particular issue of interest, sentiment analysis might be employed. By offering a solution to the text classification challenge, this experiment focuses on the perception of airline tweets among the American public. We offer a method to classify tweets as positive, negative, or neutral using three machine learning classifiers: Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbors Classifier. We examined the performance of three machine learning classifiers in terms of accuracy, average precision, average recall, and average f-1 score. The results demonstrate that the Random Forest Classifier, which produces 82% accuracy, 80% average precision, 82% average recall, and 80% average f1-score, performs the best among the machine learning classifiers. © 2023 IEEE.

关键词： Tweets

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Toward Digital Transformation Adoption: A Conceptual Framework from Transformational Leadership Perspective

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Procedia computer science 2024年 234卷 1175-1182页

作者： Marcel Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Department Universitas Kristen Krida Wacana Jakarta Indonesia Smart Cities and Communities Innovation Center Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia

Digital transformation is about transforming processes, business models, domains, and culture. Studies show that the failure rate of digital transformation is quite high up to 90%. Studies show that the transformational leadership model has a significant impact on digital transformation adoption. This paper identifies the positive and negative attributes of transformational leadership including the components that support and are affected for successful adoption of digital transformation. Furthermore, the paper combines several findings related to the attributes and components in the form of a conceptual framework. The conceptual framework can serve as a guide for organizations for their digital transformation journey.

关键词： transformational leadership digital transformation digital adoption conceptual framework

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Identification of Features in ANN's Black Box using Dimensional Reduction and Correlation Analysis: A Case Study of Cardiovascular Disease Dataset

Identification of Features in ANN's Black Box using Dimensio...

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2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Siswanto, Boby Heryadi, Yaya Suparta, Wayan Wibowo, Antoni Soeparno, Haryono Lukas Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia Jakarta Indonesia

ISBN: (纸本)9781665499699

Artificial Neural Network (ANN) is a machine learning algorithm that can perform classification. ANN has limitations;namely, it has a black box working principle, which is unsure which feature is the most influential. This study is to identify the most influential features inside the ANN's black box using a classification model by applying Principal Component Analysis (PCA) dimension reduction combined with Pearson correlation analysis. The result of the proposed model can identify the name of the main features of the data inside the ANN's black box. This study uses two public Kaggle cardiovascular datasets. The first dataset consists of 13 features, and the second dataset consists of 12 features. The result is height and gender are the most influential features in the first dataset with the correlation value of 0.734;sex and smoking are the most influential features in the second dataset with the correlation value of 0.728. Black box model result with 2 PCA's features against a model with height and gender features in the first dataset resulting from the same accuracy on the test dataset of the classification prediction results with the value of 49.90%, while on the second dataset 58.30%. © 2021 IEEE.

关键词： Principal component analysis

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Mobile Application Implementation as Agriculture 4.0 Strategy for Sugarcane Yield Prediction 9

Mobile Application Implementation as Agriculture 4.0 Strateg...

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9th International HCI and UX Conference in Indonesia, CHIuXiD 2023

作者： Mawandha, Hangger Gahara Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institut Pertanian Stiper Faculty of Agricultural Technology Yogyakarta55282 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta 11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta 11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta 11480 Indonesia

ISBN: (纸本)9798350304084

Information engineering strategies play a significant and transformative role in shaping the landscape of digital agriculture. It drives innovation and efficiency in modern farming practices, with a specific focus on enhancing the value of agricultural commodities, such as sugarcane. The application of mobile technology plays a crucial part in achieving increased sugarcane productivity. Mobile applications, equipped with advanced developer features, offer rapid and user-friendly access to vital information. Traditional methods of estimating sugarcane production relied on manual data recording on paper, followed by data transfer to computer systems, typically managed by sugar factory junior plant officers. This conventional approach presents several inherent weaknesses, including time and effort intensiveness during data recording and entry. Additionally, the potential for errors in calculation and data input is a concern. The storage capacity for paper-based documents is finite, and farmers are often unable to autonomously assess their production potential. The objective of this research is to confront these obstacles by creating a sugarcane production application for Android. This application functions as an information engineering approach focused on forecasting sugarcane yields for plantation owners and their assistants. The development procedure adhered to the systematic waterfall method, following the principles of the Software Development Life Cycle (SDLC) model. Data collection was carried out through observations, while interviews with junior plant officers provided valuable insights into sugarcane estimation techniques. Analysis involved the synthesis of observational and interview data to inform the design of the application's interface and algorithmic system. The resulting application significantly simplifies the process of estimating production potential for farmers, enabling them to access sugarcane productivity data during harvest. Consequently, sugar

关键词： Forecasting

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Digital Nudge Evaluation on COVID-19 tracing Application 9

Digital Nudge Evaluation on COVID-19 tracing Application

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9th International Conference on Information and Communication Technology, ICoICT 2021

作者： Sukmaningsih, Dyah Wahyu Abdurachman, Edi Purwandari, Betty Trisetyarso, Agung Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (纸本)9781665404471

Nudge is considered as an intervention to change user behavior and influence decision-making. Mobile apps have become a part of our everyday life. In this pandemic era, governments use mobile apps' technology to control the spread of COVID-19 infection. Many governments implemented COVID-19 tracing apps, and citizens are forced to use the apps to record their contact tracing and alert them if they have contact with an infected person. The key to control the increasing spread of the virus also depends on citizens adhere to health protocol. Government has experience difficulty in enforcing the protocol without strict surveillance. Therefore, building awareness of risk is essential-this study attempt to design nudge intervention into COVID-19 tracing apps. From the evaluation of the original apps, four nudge interventions can be applied in the application. © 2021 IEEE.

关键词： COVID-19

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Sentiment Analysis on Service Quality of an Online Healthcare Mobile Platform Using VADER and Roberta Pretrained Model 4th

Sentiment Analysis on Service Quality of an Online Healthcar...

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4th International Conference on Electronics, Biomedical Engineering, and Health Informatics, ICEBEHI 2023

作者： Maulana, Fairuz Iqbal Adi, Puput Dani Prasetyo Lestari, Dian Purnomo, Agung Wangean, Daniel Anando Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia Faculty of Medicine Universitas Airlangga Surabaya60132 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9789819714629

The development of mobile technology has penetrated into the health sector. With the development of this technology, information about health can improve the quality of health independently. The integration of information technology into the healthcare sector, namely the advancement of mobile-based health services, has significantly revolutionised the accessibility of healthcare across various regions in Indonesia. Halodoc is the leading digital health company in Indonesia and has substantially changed the axis of health services in Indonesia by providing health information that is easy to understand, credible, and accessible to everyone. This research will analyze Halodoc’s service quality based on customer reviews from Google Play Store, which will be analyzed using sentiment analysis. Specifically, this research uses VADER (Valence Aware Dictionary and Sentiment Reasoner) and Roberta Pretrained Model to analyze the sentiment of user reviews on Halodoc mobile application. The results of the sentiment analysis can provide insight into users’ overall satisfaction with the platform’s service quality. Results show that 84% users leave reviews with Neutral Sentiment Analysis, 13% with positive comments, and the smallest number of Sentiment Analysis is negative with 3%, in this application. We create correlation matrix to see correlation coefficients between sets of variables and the wordcloud. This research contributes to the growing body of research on sentiment analysis in the healthcare industry and can inform the development of strategies to improve the quality of online healthcare services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Quality control

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Count-Data Mixed Models of Topical Tweets: A Case of Indonesia Flood Events

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Procedia computer science 2023年 227卷 805-812页

作者： Alam Ahmad Hidayat Bens Pardamean Mathematics 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

Examining topic-level variability in modeling Twitter data can potentially yield more comprehensive insights into public perception during critical periods, thereby enhancing natural disaster mitigation and surveillance efforts. In this study, we utilized generalized linear mixed models (GLMMs) to illustrate the variability in tweet counts related to specific topics in Indonesia during the flood events that occurred in February 2021. The glmmTMB library in R was employed for this purpose. The data were assumed to follow two distinct exponential distributions: Poisson and Negative Binomial. To incorporate random effects, random intercepts and random slopes were introduced, allowing them to vary randomly across topics in the initial two models. Additionally, the final model addressed issues related to dispersion and zero-inflation. By evaluating the Akaike Information Criteria scores, we determined that a model based on the Negative Binomial distribution with random zero-inflation intercepts best fit the data. The chosen model formulation and the estimated parameters have the potential to forecast topic-specific trends in Indonesian flood-related Twitter data.

关键词： count data disaster surveillance floods mixed models Twitter

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