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International Conference on Informatics and Computing (ICIC)

作者： Ditdit Nugeraha Utama Bagas Rizky Ramadhan Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Research Transfer Technology Office Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Inevery nation, the main goal of a presidential election is to choose a good leader. Citizens of a country need to thoroughly observe and assess the presidential presidential candidates before making their selection. A study was conducted to create a simple decision model using multiple fuzzy criteria, taking into account the constrained perspective of Indonesian citizens. In this context, the primary methods employed for respectively designing and developing the model were object-oriented design and fuzzy logic. According to 7 free-selected presidential candidates, the model simulation revealed that candi- date number 2 received the highest decision value, scoring $\mathbf{6 8. 5 4 2}$ points; with a gap value of $\mathbf{0. 4 9}$.

关键词： Fuzzy logic Object oriented modeling Voting Computational modeling Informatics Context modeling

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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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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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A massively parallel speciation-based differential evolution algorithm applied to the 3D-AB protein structure prediction

A massively parallel speciation-based differential evolution...

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作者： Stubs Parpinelli, Rafael Boiani, Mateus Dias, André E. P. Graduate Program in Applied Computing State University of Santa Catarina Santa Catarina Joinville Brazil Rio Grande do Sul Porto Alegre Brazil Computer Science Department State University of Santa Catarina Santa Catarina Joinville Brazil

One of the most challenging problems in Bioinformatics is the finding of a protein conformation and it is known as the Protein Structure Prediction (PSP) problem. The main feature present in the AB off-lattice model is the use of polarity as the main driving force to guide the optimization process. The present work proposes an adaptive evolutionary algorithm based on GPU that is applied to the 3D-AB off-lattice PSP problem. The proposed approach is named cuDSMjDE, and is composed of a Dynamic Speciation-based Mutation Strategies Differential Evolution that employs the jDE mechanism to control F and CR parameters. A crowding strategy is also adopted, increasing competition between individuals and maintaining the diversity of solutions. All routines of the proposed algorithm are developed to run in GPU. During the experiments, eight Protein Data Bank sequences are employed. Results obtained concerning potential energy are competitive when compared with state-of-the-art algorithms. The use of a massively parallel architecture promoted the necessary scalability with speedups up to 708.78 (Figure presented.). © 2021 John Wiley & Sons, Ltd.

关键词： Evolutionary algorithms

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A Diagnosis on the Teaching of Software Design in a Sample of Undergraduate Courses in computer Science in Brazil

A Diagnosis on the Teaching of Software Design in a Sample o...

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Frontiers in Education (FIE) Conference

作者： Vitor De Souza Castro Sandro Ronaldo Bezerra Oliveira Graduate Program in Computer Science (PPGCC) Federal University of Pará (UFPA) Federal University of the South and Southeast of Para (UNIFESSPA) Belém Pará Brazil Graduate Program in Computer Science (PPGCC) Federal University of Pará (UFPA) Belém Pará Brazil

The training of a professional in the area of computing involves understanding the process of building software. In the computer Science course curricular guidelines, this training is a requirement for graduating from the course. For the construction of the Pedagogical Project of the Course (PPC), the In-stitutions observe reference documents for the development of the project according to the local/regional specificity. Dealing with computing courses, the National Curricular Guidelines (DCN) for Computing courses, the training references of the Brazil-ian Computing Society (SBC) and the Guide to the Software Engineering Body of Knowledge (SWEBOK), are key elements to support the construction of the PPC. In computer Science courses, software engineering is presented as a central element in technological training for students. Software engineering defines stages for the construction of quality software, which begins with the requirements conception, analysis, design and ends with the construction of the solution. The scope of this article will deal with the software design area, which is responsible for defining an implementable structure for a software product, which meets the specified requirements, that is, makes the communication interface between the requirements and the construction of the software. In the specialized literature, the area of Software Design (SD) is presented as important and emerging topics in the training of Computing professionals. Emerging topics for teaching Software Engineering aligned with teaching Software Design were identified, such as: UX Design Practices, Current Interface Prototyping Tools and Design Patterns. One of the challenges for the area of Software Engineering is the high heterogeneity of solutions, leading to hybrid systems, with multi-paradigms, and this heterogeneity reflects on how the professional designs the solution, what is directly associated with training in Software Design. The justification for the development of

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Indonesian Agricultural-crops Classification Using Transfer Learning Model

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Procedia computer Science 2023年 227卷 128-136页

作者： Mahmud Isnan Alam Ahmad Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Classification of Indonesian crops is a critical task in developing farming and getting more understanding of agriculture. However, there is no clear task in classifying types of crops in Indonesia. Transfer learning has been used successfully in a variety of image classification applications. Thus, in this paper, we collected images of Indonesian crops from the internet randomly and proposed a classification by using transfer learning of deep learning with four pre-trained models: EffficientNet- B0, ResNet18, VGG19, and AlexNet. In the experiment, augmentation techniques such as random horizontal flip, random vertical flip, and random affine were utilized to prevent the network from overfitting. The result found that EfficientNet-B0 outperformed other models with an accuracy of 82.55. Then, the model struggled to distinguish between crops in the same family. According to the results, although transfer learning can work well to classify images of Indonesian agricultural crops, some improvements are still required to address existing issues.

关键词： Agriculture Classification Crop Deep Learning Transfer learning

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Optical Fiber Coiled Sensors for Acoustic Oblique Wave Detection using Distributed Acoustic Sensing

Optical Fiber Coiled Sensors for Acoustic Oblique Wave Detec...

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IEEE SENSORS

作者： Frederico A. Jahnert Beatriz Brusamarello Danilo F. Gomes Sérgio T. de Camargo Manoel F. da Silva Jean C. Cardozo da Silva Cicero Martelli Jucélio T. Pereira Carlos A. Bavastri Graduate Program in Mechanical Engineering Federal University of Parana (UFPR) Curitiba Brazil Graduate Program in Electrical and Computer Engineering Federal University of Technology-Paraná (UTFPR) Curitiba Brazil Petrobras Research and Development Program Rio de Janeiro Brazil

Distributed Acoustic Sensors (DAS) are able to measure dynamic strain and temperature signals quantitatively along several kilometers of optical fiber using the properties of backscattering light. A drawback of DAS is the sensing resolution of around 1 meter long. This paper analyses the use of DAS as a vibration sensor for a thin plate structure using an acoustic excitation. A coiled method of arranging the sensing optical fibers enhances the measured signal and using a calibration method, the strain in the sensed region is obtained. The method can be used to characterize structures acoustically allowing the structure to detect the direction and reconstruct nearby acoustic events. Acoustic excitations were performed using a speaker at 1 meter distance to a 1 millimeter thick free-free 40 centimeters by 73 centimeter instrumented stainless steel plate inside an anechoic chamber. Results measured using reference microphones and accelerometers were compared to the optical fiber sensor through a Finite Element Model. The results demonstrate that the optical fiber system is able to measure the frequency response with an average 54 dB sound pressure level sound source chirp signal for flexural modes with a clear differentiation between angles of incidence up to 500 Hz.

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Auxiliary Classifier based Residual RNN for Image Captioning 30

Auxiliary Classifier based Residual RNN for Image Captioning

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30th European Signal Processing Conference, EUSIPCO 2022

作者： Çayli, Özkan Kiliç, Volkan Onan, Aytug Wang, Wenwu Electrical and Electronics Engineering Graduate Program Izmir Katip Çelebi University Turkey Department of Computer Engineering Izmir Katip Çelebi University Turkey University of Surrey United Kingdom

ISBN: (纸本)9789082797091

Image captioning aims to generate a description of visual contents with natural language automatically. This is useful in several potential applications, such as image understanding and virtual assistants. With recent advances in deep neural networks, natural and semantic text generation has been improved in image captioning. However, maintaining the gradient flow between neurons in consecutive layers becomes challenging as the network gets deeper. In this paper, we propose to integrate an auxiliary classifier in the residual recurrent neural network, which enables the gradient flow to reach the bottom layers for enhanced caption generation. Experiments on the MSCOCO and VizWiz datasets demonstrate the advantage of our proposed approach over the state-of-the-art approaches in several performance metrics. © 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Recurrent neural networks

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BERT-based Topic Modeling Approach for Malaria Research Publication 4

BERT-based Topic Modeling Approach for Malaria Research Publ...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

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

ISBN: (纸本)9781665473279

Malaria is a communicable disease with half of the global population at risk due to its high morbidity and mortality rates. A massive number of studies are dedicated to malaria research, so it plays a key role in formulating the proper prevention strategy and effective malaria treatment. With the overwhelming number of updated publications in the field, an unsupervised text mining approach such as topic modeling may provide an alternative method for the malaria researcher to keep pace with new insights. In this work, we collect metadata of malaria publications from the PubMed database to perform BERT-based topic modeling to find well-defined topics regarding malaria research. The method is largely based on the popular BERTopic pipeline. We compare the performance of three different language models to generate document embeddings from the data. The dimension reduction and the density-based clustering algorithm are used to cluster the embeddings. The topic representation is computed based on the semantic similarity of the class TF-IDF representation. The substance of the resulting topics is then manually annotated based on the top words of each topic. We demonstrate that by merging initial topics into larger topics using hierarchical clustering and manual content-based examination, the evaluated coherence measure can be further improved, thus enhancing the topic's interpretability. Our modeling result is able to extract ten major topics recurring in the malaria research publication published from 2017-2022. The result provides preliminary insight to understand the dynamics and patterns of malaria research over the years © 2022 IEEE.

关键词： Diseases

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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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