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UML Design for Decision Support Model in Determining the Sustainability of Online Course Materials 6

UML Design for Decision Support Model in Determining the Sus...

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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Sihotang, Erna Fransisca Angela Kurniawan, Afdhal Utama, Ditdit Nugeraha Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

The scenario of online learning is a very urgent need in the world of future knowledge. Since the Corona Virus Disease-19 pandemic, the world economy has started to plummet and caused many adults to lose their jobs. The advantage is the flexibility and rapid development of the internet. In 2020, the number of unemployed increased significantly. This reason makes people strive to improve their ability to meet job requirements by taking online courses. Online courses are a way that people can choose to improve their skills anywhere and anytime. The sustainability of online course material that is offered to the course user and issued by the company will be discussed in this study. The novelty of this research is to obtain a decision support model based on fuzzy logic for determining online courses. The method used is decision-making based on UML and fuzzy logic for the final decision. The fuzzy inference model process begins by determining the decision parameters then using fuzzification with absolute input then refracted with fuzzy criteria, and ends with defuzzification with absolute output. There are two groups of parameters in this study, company profits which consist of 5 parameters and user benefits, which consist of 9 parameters. Once the model is verified and valid, the final decision is useful for users looking for online course and also useful for the decision unit of online course companies in determining the sustainability of online course materials. © 2022 IEEE.

关键词： Decision support model fuzzy logic online course UML

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Social Network Analysis and PowerBI Dashboard to Determine E-Wallet Marketing Strategies 4

Social Network Analysis and PowerBI Dashboard to Determine E...

引用

4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Rita, Rita Monica, Sheila Aurelia, Miracle Girsang, Abba Suganda Puspita, Virienia Binus Business School Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program Bina Nusantara University Communication Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

The rapid growth of digital payment platforms like OVO, ShopeePay, and GoPay in Indonesia has driven the need for businesses to optimize marketing strategies by analyzing customer interactions through social media. This study leverages Social Network Analysis (SNA) and Power BI to extract insights from Twitter data, offering a comprehensive view of how these platforms engage with consumers and shape brand perceptions. By applying sentiment analysis and classification using IndoBERT results, this study aims to identify key influencers, assess the sentiment around these brands, and visualize marketing communication patterns. The integration of SNA and BI tools provides a detailed, data-driven approach to improving business decision-making in the competitive digital payment market, by utilizing them to evaluate e-wallet tweet activity and user interactions in Indonesia and identifying key periods and regions for targeted marketing. Strategies like loyalty programs and segmented campaigns are recommended to enhance user engagement, address security concerns, and maximize market reach during culturally significant periods such as Eid and the holiday season. © 2024 IEEE.

关键词： Business Intelligence Digital Wallet Marketing Strategy Social Media Social Network Analysis

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The Use of Deep and Machine Learning for Face Expression Recognition: A Literature Review 7

The Use of Deep and Machine Learning for Face Expression Rec...

引用

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

作者： Pangestu, Gusti Hendric Spits Warnars, Harco Leslie Soewito, Benfano Gaol, Ford Lumban BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

In the current era, Artificial Intelligence (AI) and computer Vision take the main role and participating to the people daily activities. Face Expression became as interesting topic to be explore. Face expression recognition or detection could be applied in many aspects, such as for student focus detection based on face expression, intruder detection system, lie detection and many more. Referring the useful of face expression detection, there are also much research that focused on the methodology to detects or recognize the face expression. Several approaches are use such by using Deep Learning, Machine Learning and statistical method. This paper focused on the process to obtain and knowing the best approach or methods to recognize the face expression by using Systematic Literature Review (SLR) process. From hundreds of retrieved papers with similar interest, we do the selection based on the interest, scope, and topic similarity. The process keeps repeating until we found papers that has similarity criteria in output, dataset, subject and the methods. There are 6 papers in total that fulfil our requirements based on subject, output, result and methods. Subsequently, every research paper was deep-reviewed and compared with another to find the best approaches proposed and dataset used. © 2022 IEEE.

关键词： Intrusion detection

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Software Testing Model by Measuring the Level of Accuracy Fault Output Using Neural Network Algorithm 7

Software Testing Model by Measuring the Level of Accuracy Fa...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Zulkifli Gaol, Ford Lumban Trisetyarso, Agung Budiharto, Widodo Binus University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Binus University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

The testing stage is essential in software development because it determines the quality level, which is indicated by minimal errors. Meanwhile, the error that is discovered by the tester is called a fault. Therefore, this research aims to create a Model-Based Testing (MBT) using the equivalence partitioning technique in the black-box method. The model is expected to log software errors such as Access, performance, initialization, a data structure or external database, incorrect or missing functions, to identify, categorize, and fix them methodically and appropriately. Based on the discoveries, the errors form a dataset that measures the accuracy of the fault output using a neural network algorithm. The result showed the formation of MBT can be developed using the equivalence partitioning technique by measuring the fault output accuracy level using a neural network algorithm, where the average accuracy rate is 80%. © 2022 IEEE.

关键词： Errors

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E-Government Interoperability: Provincial-Level Architecture Model to Enable Fast Healthcare Interoperability Resources (FHIR) 9

E-Government Interoperability: Provincial-Level Architecture...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Wijayanti, Diana Urbaya, Satriawansyah Sitompul, Taufiq Adrian, Verry BINUS Graduate Program Bina Nusantara University Department of Information Systems Jakarta11480 Indonesia Program USAID Indonesia Jakarta12930 Indonesia University of Oslo Department of Informatics Oslo0316 Norway Binus Graduate Program - Doctor Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Jakarta Provincial Health Office Center of Data and Information Technology Jakarta10160 Indonesia

ISBN: (纸本)9798350390025

The process of using ICT to provide services to the public is known as the Indonesian e-Government system, or Sistem Pemerintahan Berbasis Elektronik (SPBE). The e-Government initiative in Jakarta Provincial Health Office involves enhancing collaboration among public health entities for efficient data exchange and streamlined processes, especially between the Provincial and District Health Offices, public hospitals, government clinics, and primary health care centers (Puskesmas). Achieving interoperability requires standardized protocols and a well-defined architectural model to integrate data seamlessly. This study presents a provincial-level architectural model focused on improving electronic health records interoperability, aiming to promote the adoption of the national Fast Healthcare Interoperability Resources (FHIR) health information exchange platform and enhance the integrity of health data in Jakarta. The study methodology involves conducting literature reviews, observations, and discussions with representatives from healthcare facilities to develop the e-Government architecture model and prototype of the infrastructure layer aiming to facilitate the interoperability of Electronic Health Records (EHRs) across 93 healthcare facilities, all of which are part of the SPBE users. © 2024 IEEE.

关键词： e-government

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Gender-Based Bimodal Analysis of Speech and Text in Non-Contextual Emotion Recognition

Gender-Based Bimodal Analysis of Speech and Text in Non-Cont...

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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Angkasa, Abby Rasya Zahra, Amalia Fung, Wai-Keung Binus Graduate Program Computer Science Department Jakarta11480 Indonesia Cardiff Metropolitan University Cardiff School of Technologies Engineering Department Cardiff United Kingdom

ISBN: (纸本)9798331508579

Emotion recognition can help human-computer interactions by enabling systems to respond empathetically and adapt to users' emotional conditions. This capability improves user experience, supporting the development of a more intuitive and emotionally responsive communication system. This study analyzes a bimodal approach based on gender (male and female) to recognize emotions without contextual information in dialogue analysis. Utilizing the Multimodal EmoryNLP dataset extracted from the TV series Friends with acted speech, we focused on four primary emotions: Angry, Neutral, Joy, and Scared. The model used in this study for text classification is RoBERTa, and wav2vec 2.0 is used for audio feature extraction with the Bi-LSTM model for classification. The experiment results using weighted F1-score reveal that data augmentation enhanced the performance of analyzing the original dataset from 0.46% to 0.52% and the male dataset from 0.43% to 0.51 %. In comparison, the female dataset remained consistent at 0.46%. The weighted F1-score and Unweighted Averaged Recall (UAR) from the male dataset are higher, 51 % and 48%, respectively, than those from the female dataset, 46% and 47%, respectively. Gender-based analysis indicated that male and female datasets exhibited distinct performance patterns, highlighting variations in emotional expression and recognition between genders. These findings underscore the effectiveness of multimodal strategies in emotion recognition and suggest that gender-specific factors play a significant role in enhancing classification performance. While these results highlight performance trends, further validation through repeated trials and statistical analyses could provide stronger generalizations and insights into gender-based differences. © 2024 IEEE.

关键词： Emotion Recognition

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The Utilization of Fuzzy Logic and Bollinger Bands to Enhance Trading Decision-Making During the Bitcoin Halving Phase 9th

The Utilization of Fuzzy Logic and Bollinger Bands to Enhanc...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Darmawan, Oscar Adam Heryadi, Yaya Lukas Wulandhari, Lili Ayu Sonata, Ilvico Computer Science Department Binus Graduate Program Bina Nusantara University Jakarta11480 Indonesia Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

The volatile behavior of Bitcoin's price, especially during its halving periods, poses considerable obstacles for forecasting and decision-making in cryptocurrency trading. This paper presents a novel method that combines application fuzzy logic with Bollinger Bands to improve trading decision-making in times of high market volatility. This study conducted an experiment utilizing three fuzzy logic controllers and Bollinger Bands (BB) to determine the strength of buy, hold, and sell signals. This dataset includes the initial and final prices that are used to calculate the BB. The raw and computed values serve as the precise input parameters for the Fuzzy Inference System (FIS). The membership functions were categorized into four levels: very low, low, high, and very high, based on the input default settings utilized by traders. Rulesets were created using fuzzy logic to produce signals that indicate the level of strength of a trading advice. This study evaluate the effectiveness of this hybrid method in comparison to the traditional utilization of the Bollinger Band only indicator and Moving Average Convergence Divergence (MACD) indicator, which is widely favored by traders to identify possible market fluctuations. This methodology involves creating a trading simulation that is based on past Bitcoin halving events. The objective is to assess the efficacy of these strategies in managing heightened volatility. The application of fuzzy logic with the Bollinger Bands model yielded a success rate of 92.47% while analyzing 93 daily data points from the previous Bitcoin halving event on May 11, 2020. © 2024 The Authors.

关键词： Costs

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Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series Data 8

Long Short-Term Memory-based Models for Sleep Quality Predic...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Hidayat, Alam Ahmad Budiarto, Arif Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters. © 2023 The Authors. Published by Elsevier B.V.

关键词： deep learning forecasting sleep quality time series wearable devices

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A Machine Learning-Based Approach to Calibrate Low-Cost Particulate Matter Sensors 12

A Machine Learning-Based Approach to Calibrate Low-Cost Part...

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12th Brazilian Symposium on Computing Systems Engineering, SBESC 2022

作者： Pastorio, Andre F. Spanhol, Fabio A. Martins, Leila D. De Camargo, Edson T. Graduate Program in Computer Science Cascavel Brazil Toledo Brazil Londrina Brazil

ISBN: (数字)9781665474252

ISBN: (纸本)9781665474252

Low-cost particulate matter (LC-PM) sensors have been studied around the world as a viable alternative to expensive reference stations for monitoring air quality. However, LC-PM sensors require periodic calibration, since their data are often inaccurate and subject to uncertainty. Sensors calibration can be performed through machine learning methods where the sensor is placed in a real environment subject to the local environmental conditions of the place and its measurement compared to a reference equipment. This work evaluates different machine learning methods in five different models of LC-PM sensors, aiming to select the most appropriate sensor and a calibration method to be used in a low-cost air quality station in the context of smart cities. © 2022 IEEE.

关键词： Calibration

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN) 7

Development of Detection Lung Disease Stand Alone System Bas...

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7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Liewarnata, Edgar Caesar Mariana, Yosica Tan, Sofyan Liawatimena, Suryadiputra Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Automotive and Robotics Program Computer Engineering Department Binus Aso School of Engineering Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification. © 2024 IEEE.

关键词： Lung cancer

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