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

作者： Hasani, Muhammad Fikri Heryadi, Yaya Arifin, Yulyani Lukas Suparta, Wayan Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance. © 2023 IEEE.

关键词： Embeddings

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A Bayesian-based approach for extracting the pion charge radius from electron-electron scattering data

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Chinese Physics C 2021年第8期45卷 12-27页

作者： Alam A Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara UniversityJakarta 11480Indonesia Computer Science Department BINUS Graduate Program-Master of Computer ScienceBina Nusantara UniversityJakarta 11480Indonesia

In this study,we utilize a potentially versatile Bayesian parameter approach to compute the value of the pion charge radius and quantify its uncertainty from several experimental e^(+)e^(-) datasets for the pion vector form *** employ dispersion relations to model the pion vector form factor to extract the *** model selection is used to determine the order of polynomial appearing in the form factor formulation that can be supported by the data,adapting the computation of Bayes evidence and Bayesian effective complexity based on Occam's *** findings indicate that five out of six used datasets favor the nine-parameter model for radius extraction,and accordingly,we average the radii from the *** some inconsistencies with the most updated radius values,our approach may serve as a more intuitive method of addressing parameter estimations in dispersion theory.

关键词： Bayesian complexity dispersion hadron pion

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Scheduling Production in the Flexible Packaging Industry Using Mathematical Models and Genetic Algorithms

Scheduling Production in the Flexible Packaging Industry Usi...

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2023 International Conference on Informatics Engineering, science and Technology, INCITEST 2023

作者： Tarigan, Masmur Gaol, Ford Lumban Mauritsius, Tuga Budiharto, Widodo Esa Unggul University Bina Nusantara University Computer Science Department / Computer Science Graduate Program Jakarta Indonesia Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia Management Bina Nusantara University Department of Information System Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350317862

Minimizing time wastage is a crucial objective for all production operations. Reliable manufacturing scheduling The objective is to mitigate time-based production waste caused by excessive processing, waiting, and transportation. Industry 4.0 promotes technological innovation across all industrial sectors to increase efficiency and effectiveness while preserving high competitiveness. The implementation of artificial intelligence in Industry 4.0 is expected to result in a reduction in time wastage. Non-dominance sequencing genetic algorithms (NSGA), genetic algorithms (GA), and evolutionary algorithms (EA) are the three most common scheduling approaches, according to literature-indexed research summaries from the past five years. The examination of production scheduling in the flexible packaging business, which involves the utilization of different machines and processes and is produced on demand, had not been conducted before in this study. A mathematical scheduling model will be developed in this research to ascertain the shortest production time span in the flexible packaging industry. As a form of technological innovation, this mathematical model will be utilized in experiments employing a genetic algorithm approach. For the genetic algorithm to generate a minimum makespan in the flexible packaging industry's production scheduling process. © 2023 IEEE.

关键词： Genetic algorithms

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Exploring EfficientNet Variants for Image Encoding in Auxiliary Signal Guided Knowledge Encoder-Decoder Framework

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Procedia computer science 2024年 245卷 391-398页

作者： Matthew Martianus Henry Nur Adhianti Heryanto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The growing number of medical images has led to radiologist burnout, which seriously impacts the radiologist's performance. To address the previously mentioned issue, an Auxiliary Signal Guided Knowledge (ASGK) multimodal encoder-decoder framework was designed to automatically generate the medical report based on the proposed medical graph and natural language decoder. It utilizes DenseNet-121 as the image encoder. With DenseNet-121 lack of computational and memory efficiency, this study aims to explore the potential of EfficientNetB0 to EfficientNetB4 as an ASGK image encoder substitute. The framework is trained with IU X-Ray dataset for 30 epochs, with Adam optimizer, a learning rate of 0.01 with 0.8 decay rate, binary cross entropy loss for the medical tags, and cross-entropy loss for the generated medical captions. During the framework training process with each image encoder, the parameter that achieves the highest CIDEr score on the validation set is considered the best image encoder parameter and will be used on the test set. On the test set, EfficientNetB3 as an ASGK image encoder has been shown to increase the CIDEr score to 0.35, a significant increase from the 0.28 CIDEr score obtained by the ASGK using DenseNet-121. This score is only a 1% decrease from the best validation score. It suggests that not only EfficientNetB3 increases the framework's performance, it is also less prone to overfitting. This study has demonstrated that EfficientNetB3 is a potential image encoder substitute for DenseNet-121 in the ASGK framework.

关键词： Automated medical report generation DenseNet-121 EfficientNet Encoder-decoder framework

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An Augmented Reality Game's User Experience and Its Effect on Immersion Leading to Intention 11

An Augmented Reality Game's User Experience and Its Effect o...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Oktavia, Tanty Utomo, Kawang Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University Binus Graduate Program - Information System Management Information Systems Management Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9798350389654

Since the emergence of Pokémon Go in 2016, the world has been introduced to Augmented Reality (AR) games. Many IT companies have begun developing augmented reality (AR) games due to the great commercial potential in this industry. Certain products may have managed to remain viable in the market, while others were forced to close due to one or two factors. Since the emergence of Pokémon Go, no other game has managed to surpass its unprecedented success. The company responsible for developing Pokémon Go has decided to shut down one of their games due to a lack of consumer interest. The technology of augmented reality (AR) is consistently associated with the concept of immersion. Immersion is a key aspect of the game that allows users to deeply engage and feel fully involved in the experience. This study will employ a quantitative methodology, utilising a questionnaire that will be distributed to and completed by those who have engaged in augmented reality (AR) games. The data will be analysed using Smart PLS to examine the impact of user experience on immersion, which in turn influences intention. After gathering over 200 participants by spreading google form questionnaires and doing data analysis, the findings indicate that only a few aspects of user experience, namely Brand Experience and User Need Experience, have an impact on reasons. Niantic, the developer of Pokémon Go, was unable to replicate their success with a similar augmented reality game called Harry Potter: Wizards Unite. The initial release occurred on June 21, 2019, and the shutdown took place on December 21, 2021. In the beginning, they shown a profound enthusiasm for the game but were unable to sustain it, much as Pokémon Go. According to Niantic CEO John Hanke, the primary reason for the game's shutdown was its immersion. Immersion has a notable impact on User Confirmation, which in turn has a notable impact on Satisfaction, and Satisfaction has a notable impact on Intention. Moreover, there have be

关键词： Plant shutdowns

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Perfomance Improvement of mpdCNN as Ensemble Learning Model on Low Resolution Image-Face Recognition System

Perfomance Improvement of mpdCNN as Ensemble Learning Model ...

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International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Daniel Anando Wangean Derwin Suhartono Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Low resolution image-face recognition system is one of the challenging aspects of face recognition models' development. From machine learning, deep learning, and into ensemble learning are implemented to develop face recognition models. Including mpdCNN that published by Mishra in a journal article. This mpdCNN already achieved 88.6% accuracy score on SCFace dataset while published. Based on literature review, ensemble learning model can be improved by modifying parallel layers. The purpose of this research is to improve mpdCNN's performance by implementing some modifications include adding parallel layers, alternating the fusion layers, expanding the layers, and adding residual connections. Some modifications were successfully improved mpdCNN's accuracy score. By adding parallel layers and residual connections into mpdCNN's architecture, the modified mpdCNN that proposed in this research achieved 92.33% accuracy score, measured by Rank-k evaluation metric.

关键词： Measurement Deep learning Accuracy Image resolution Image recognition Face recognition Instruments Robustness Ensemble learning Systematic literature review

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An Energy-Efficient Intrusion Detection Offloading Based on DNN for Edge Computing

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IEEE Internet of Things Journal 2025年第12期12卷 20326-20342页

作者： Simioni, Joao A. Viegas, Eduardo K. Santin, Altair O. de Matos, Everton Pontificia Universidade Catolica do Parana Graduate Program in Computer Science Curitiba80215-901 Brazil Technology Innovation Institute Secure Systems Research Center Abu Dhabi United Arab Emirates

To improve the accuracy of deep neural networks (DNNs) applied to network intrusion detection systems (NIDS) researchers often increase the complexity of their designed model. Given the processing limitations of resource-constrained devices, researchers have proposed offloading the NIDS task to the cloud. However, simultaneously ensuring energy efficiency and detection accuracy remains a challenge. This article proposes a DNN-based NIDS through early exits that operate following an energy-efficient edge-computing architecture implemented twofold. First, we propose a DNN-based NIDS employing multiobjective optimization for efficient inference and computation offloading. It is designed to perform the classification task at the edge device and configured to proactively offload events to the cloud when additional processing capabilities are required. Our insight is to utilize multiobjective optimization to identify the optimal balance between accuracy and energy efficiency in task offloading. Second, the final DNN branch performs classification with a reject option to ensure reliability when analyzing new network traffic patterns, while calibration adjusts the model’s confidence values to enhance generalization. The rejection mechanism allows the model to accept only its most confident classifications enhancing the model’s generalization capabilities. Experiments conducted with our proposal’s prototype through a new intrusion dataset encompassing one-year-long network traffic with over 7TB of data attested to our proposal’s feasibility. It can reduce the edge device’s energy consumption and processing costs to only 1%, while maintaining accuracy. This is achieved while demanding the offloading of only 10% of network events to the cloud, optimizing resource utilization across both the edge and cloud infrastructures. © 2014 IEEE.

关键词： Network intrusion

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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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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

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

ISBN: (纸本)9798331513320

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. © 2025 IEEE.

关键词： industry 4.0 logistics simulation development simulation learning simulation learning component

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Classification Prediction of Heart Disease Using Machine Learning Techniques 4

Classification Prediction of Heart Disease Using Machine Lea...

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Setiawan, Kevis Jonathan Beratha, Putu Satria Anggereainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350318432

Heart disease is also called a common one of global health concerns. A lot of research has been done before to predict someone whether has a heart disease or not by machine learning. In this study, we use five machine learning techniques as comparison which machine learning technique has a most accuracy to recognize heart disease in someone's condition. In this case, we are using UCI Cleveland Dataset as a sample and the result shows that the Support Vector Machine and K-Nearest Neighbor gives the most accuracy which is 85% along with many aspects respectively. © 2023 IEEE.

关键词： Support vector machines

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Protocol for a Graph Database of NTDs for Drug Repurposing 18th

Protocol for a Graph Database of NTDs for Drug Repurposi...

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18th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2024

作者： Giordani, André Ruiz, Duncan D. Graduate Program in Computer Science – PPGCC Pontifical Catholic University of Rio Grande do Sul – PUCRS Porto Alegre Brazil

ISBN: (纸本)9783031878725

Neglected tropical diseases (NTDs) are a significant health burden globally, contributing to 1–2% of all mortality. The quest for new pharmacological interventions for NTDs is hampered by the substantial time and financial resources required for drug development, alongside a marked disinterest from laboratories due to the geographic and economic profiles of affected regions. This study presents a novel protocol for leveraging graph database architectures to elucidate potential connections between NTDs and existing pharmaceuticals that may be repurposed for treatment. Focusing on tuberculosis as a case study, we constructed a graph database integrating comprehensive disease and drug data. By removing established links between tuberculosis and known treatments, we employed a Graph Convolutional Neural Network (Graph-CNN) algorithm to unearth previously unrecognized relationships. This computational approach not only facilitates the discovery of new drug-disease associations but also streamlines the repurposing process for existing medications. Our findings demonstrate the utility of advanced data engineering and machine learning techniques in identifying promising therapeutic candidates for NTDs, potentially accelerating the availability of effective treatments for these underserved diseases. The study’s contribution is underscored by a comparative analysis of state-of-the-art algorithms within our graph database framework, highlighting the efficacy of our protocol in the broader context of drug repurposing research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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