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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Siek, Michael Rukma, Luin Yatnalaksita Parasdyayatma Bina Nusantara University Faculty of Computing and Media Business Information Systems Program Information Systems Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

In the era of emerging technologies, the banks strive to provide a variety of innovations toward digital banking transformations. Hence, digital banking is the result rapid establishment of innovative digital banking applications that attracts more customers allowing for personalized and commercialized banking transactions. Due to the emerging needs of customers and changes in business models, digital banking applications have been sought to be the solutions for some drawbacks in traditional banking operations. This paper aims at identifying and determining the impacts of digital banking application in disrupting the traditional banking industry using hypothesis testing and analysis of regression on response data collected with convenient sampling technique. The data analysis utilized 265 samples gathered in May until June 2022 with the characteristics of respondents having age above 18 years old and using at least one digital banking application. The data analysis results with proper research framework demonstrate that the digital banking application has significantly disrupted the traditional banking industry, primarily due to the perceived ease of use, habit, customer satisfaction, and behavioral intention to use digital banking applications. This research result can provide useful reference and strategy for banking industry to provision innovative solutions towards banking digitalization and automation. © 2022 IEEE.

关键词： Customer satisfaction

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Analysis of Factors Influencing the Buyer Preferences on Goods and Services: Comparing E-Commerce and Retail Stores

Analysis of Factors Influencing the Buyer Preferences on Goo...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Michael Siek Pasha Tadjoedin Business Information Systems Program Information Systems Department Faculty of Computing and Media Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

E-commerce is a type of businesses or individuals doing transactions through the internet, which continues to be improved to perform its tasks with more features and facilities compared to physical and brick and mortar stores. Customers is getting used to shop their wanted products in e-commerce due to convenient, practical, and useful. Specifically, during the Covid-19 pandemic, purchasing goods via e-commerce without physical contact and during lockdowns is most preferable. The research aimed at finding the crucial factors influencing the buyer preferences on goods and services by comparing e-commerce and retail stores using quantitative methods. A modified research framework was developed by considering the existing technology acceptance model and other factors related to purchasing cloths and foods. The collected response data was analyzed and modelled using structural equation model with partial least square algorithm. The modelling results showed that most assessed responses indicate a positive perception of e-commerce. The factors of buyer preference on e-commerce are mainly due to faster product searching with competitive pricing. The research results provide important information and strategies for businesses on how to sell particular products to the customers.

关键词： Surveys COVID-19 Analytical models Technology acceptance model Pandemics Pricing Mathematical models Data models Electronic commerce Business

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SPLORT: Evaluation of a Web-based Approach for Descriptive and Exploratory Analysis of Multivariate Spatio-temporal Data 21

SPLORT: Evaluation of a Web-based Approach for Descriptive a...

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14th International Symposium on Visual Information Communication and Interaction, VINCI 2021

作者： Feijóo-García, Miguel Alfonso Hernández-Peñalosa, José Tiberio Program of Systems and Computing Engineering Universidad de Los Andes Colombia and Program of Systems Engineering Universidad El Bosque Colombia Program of Systems and Computing Engineering Universidad de Los Andes Colombia

ISBN: (纸本)9781450386470

The multivariate Spatiooral data challenges the analysts along the navigation and exploration of the information, using Data Analytics techniques. We present SPLORT, as a user-centered web-based solution for segmented Visual Analytics. The tool involves a "What-Where-When"pyramidal paradigm to drive Spatiooral analysis towards the findings of new insights. It features multi-layered coordinated views to help the analyst navigate and explore multivariate data simultaneously. We evaluated this web-based tool, and reported the findings and results, considering users' performance and perceptions, basing our analysis on their Likert scale (0-5) responses, regarding their experience and the tool's usability, and the sentiment of their feedback, by using Natural Language Processing (NLP) techniques. Our findings from the pilot study held, suggest that SPLORT successfully allows the users to visually query, explore and analyze contextless multi-scale attributes within space and time, which quickly drives them to new insights and to support easier decision-making processes. Moreover, the tool's flexibility, due to its multi-layered GUI, supports better and more friendly visual analytics. © 2021 ACM.

关键词： Data Analytics

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

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Large-Scale Swarm Control in Cluttered Environments 15th

Large-Scale Swarm Control in Cluttered Environments

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15th International Conference on Social Robotics, ICSR 2023

作者： Elsayed, Saber Mabrok, Mohamed School of Systems and Computing University of New South Wales Canberra Australia Mathematics Program Department of Mathematics Statistics and Physics College of Arts and Sciences Qatar University P.O. Box 2713 Doha Qatar

ISBN: (纸本)9789819987146

In the evolving era of social robots, managing a swarm of autonomous agents to perform particular tasks has become essential for numerous industries. The task becomes more challenging for large-scale swarms and complex environments, which have not been fully explored yet. Therefore, this research introduces a methodology incorporating multiple coordinated robotic shepherds to effectively guide large-scale agent swarms in obstacle-laden terrains. The proposed framework commences with deploying an unsupervised machine-learning algorithm to categorise the swarm into clusters. Then, a shepherding algorithm with coordinated robotic shepherds drives the sub-swarms towards the goal. Also, a path planner based on an evolutionary algorithm is proposed to help robotic shepherds move in a way that minimises the dispersion of each sub-swarm and avoids potential hazards and obstructions. The proposed approach is tested on different scenarios, with the results showing a success rate of 100% in guiding swarms with sizes up to 3000 agents. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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Deep Learning Models for Tsunami Forecasting

Deep Learning Models for Tsunami Forecasting

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IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)

作者： Michael Siek Bismani Rafi Lusadi Soemarno Information Systems Departement Business Information Systems Program School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331543105

ISBN: (纸本)9798331543112

Tsunami induced by seismic earthquake has generated significant disasters in the big cities around coastal regions. One of the most disastrous tsunami events, the 2004 Indian Ocean tsunami has killed 230.000 human casualties and destructed enormous infrastructures and economy in the coastal areas of several countries in the East and South Asia. The negative impacts of tsunami have initiated the vital need for an accurate and fast tsunami early warning system to anticipate infrastructure destruction and human casualties. This paper focuses on the development of a hybrid tsunami forecasting model by utilizing Delft3D-FLOW numerical models and data-driven models using computational intelligence to simulate and forecast tsunami wave amplitudes. Several deep machine learning models were built properly and optimized using Convolutional Neural Network and Long Short- Term Memory algorithms with single and multi-steps prediction methods. The proposed tsunami models provide reliable tsunami forecasts to be utilized for a real-time tsunami early warning system.

关键词： Solid modeling Three-dimensional displays Computational modeling Urban areas Sea measurements Predictive models Alarm systems Tsunami Numerical models Forecasting

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Analysis of News Sentiment for Stock Price Prediction Using VADER Sentiment

Analysis of News Sentiment for Stock Price Prediction Using ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Michael Siek Vic Declan Chandra Information Systems Department Business Information Systems Program School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Stock market trading is often thought to be a lucrative subject and in it, is predominantly occupied by the discussion of stock price prediction. Within it comes a multitude of theories pertaining to stock movement, while they seem irreconcilable an integrated model can be created to improve the accuracy of stock prediction as well as form a synthesis of said multitude. The paper focuses on finding the correlation that lies between the historical stock price of Bank Jago alongside economic indicators and news sentiments through implementing VADER sentiment analysis and regression models. The analysis and modelling data were collected in real-time through Yahoo Finance API and GDELT for the stock news. The analysis results indicate that Pearson and Spearman correlation coefficients between stock prices and news sentiments have low values or relationships due the fact that these methods only describe general statistical relationship on average. However, there are reasonably improvements in correlation coefficients when using rolling techniques and economic indicators. In other hand, the stock traders and investors prefer to obtain real-time predictions or indicators of low or high stock prices and the dynamical stock price directions. The latter is denoted by reasonably good dynamical alignments between stock prices and news sentiments through visual inspection of the chart. This research is highly recommended to continue for the next steps by integrating sentiment analysis and machine learning models for predicting stock prices.

关键词： Correlation coefficient Hands Sentiment analysis Analytical models Visualization Economic indicators Machine learning Predictive models Inspection Real-time systems

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Analysis of News Sentiment and Stock Price Using Web Scraping and Vader Sentiment Analysis

Analysis of News Sentiment and Stock Price Using Web Scrapin...

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International Conference on Information Management and Technology (ICIMTech)

作者： Michael Siek Erik Sebastian Setiadi Business Information Systems Program Information Systems Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

Rapid development of the information technologies and methods has contributed to the advancement of financial stock analysis and prediction. This paper focuses on investigating Bank Mandiri stock sentiments from financial news data using web scraping and VADER sentiment analysis to obtain accurate news polarity scores. The dynamical alignment and changes between the resulting sentiment analysis and stock prices towards neutral, bullish and bearish stock movements can subsequently be identified and evaluated using Pearson's and Spearman's correlation coefficients for the model refinement. The research results over market conditions indicate that there are consistent alignment and direction between stock sentiments from the financial news and dynamical stock price changes. This work can provide some benefits and insight for the financial investors and stock traders in making a better decision.

关键词： Correlation coefficient Sentiment analysis Analytical models Accuracy Information management Information technology Forecasting

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Developing Accurate Prediction Model Using Machine Learning and Business Intelligence on University Students GPA

Developing Accurate Prediction Model Using Machine Learning ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Michael Siek Kenrick Lim Information Systems Department Business Information Systems Program School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

It has been challenging to determine students' grades and performances throughout college. The purpose of this study is to raise students' performance during each semester and guarantee satisfactory course completion with the use of accurate models for predicting student GPA. The process is using the dataset of student GPA in the first year of university as an input for the machine learning model. Then, using the dataset to develop prediction models such as Linear regression, Decision Tree, Fully Dense Neural Network and MLP. Lastly, results are evaluated using proper evaluation measures. This research provides an accurate prediction model with the usage of machine learning algorithms for predicting Students GPA. As it happens, the linear Regression Model has the best result with 0.1396 on the MSE and 0.3736 on the RMSE. By doing so, Students can then view their GPA to determine and strategize on how they might raise it in the upcoming semester as well as for academic advisor to give more accurate strategy and actions to the students to obtain a good study performance. Because graduates who have high GPAs can start their professions off well and have big opportunities in the foreseeable future.

关键词： Deep learning Accuracy Machine learning algorithms Linear regression Predictive models Prediction algorithms Data structures Time factors Intelligent systems Regression tree analysis

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A NEW HOPE: contextual privacy policies for mobile applications and an approach toward automated generation 24

A NEW HOPE: contextual privacy policies for mobile applicati...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Shidong Pan Zhen Tao Thong Hoang Dawen Zhang Tianshi Li Zhenchang Xing Xiwei Xu Mark Staples Thierry Rakotoarivelo David Lo School of Computing Australian National University and Software and Computational Systems Research Program CSIRO's Data61 Software and Computational Systems Research Program CSIRO's Data61 Khoury College of Computer Sciences Northeastern University School of Computing and Information Systems Singapore Management University

ISBN: (纸本)9781939133441

Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, the concept of contextual privacy policies (CPPs) has been proposed by researchers. The aim of CPPs is to fragment privacy policies into concise snippets, displaying them only within the corresponding contexts within the application's graphical user interfaces (GUIs). In this paper, we first formulate CPP in mobile application scenario, and then present a novel multimodal framework, named SEEPRIVACY, specifically designed to automatically generate CPPs for mobile applications. This method uniquely integrates vision-based GUI understanding with privacy policy analysis, achieving 0.88 precision and 0.90 recall to detect contexts, as well as 0.98 precision and 0.96 recall in extracting corresponding policy segments. A human evaluation shows that 77% of the extracted privacy policy segments were perceived as well-aligned with the detected contexts. These findings suggest that SEEPRIVACY could serve as a significant tool for bolstering user interaction with, and understanding of, privacy policies. Furthermore, our solution has the potential to make privacy notices more accessible and inclusive, thus appealing to a broader demographic. A demonstration of our work can be accessed at https://***/SeePrivacy/

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