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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Bonamutial, Marcellino Prasetyo, Simeon Yuda School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Smartphone has become a necessity and the most accessible for every individual and is mainly used for communication. Smartphone price is higher than the previous years because of the new features provided. Smartphone Price Prediction will be using machine learning to predict the smartphone price with the provided feature. The dataset will be obtained through web-scraping with 14 features that consist of categorical and numerical features. Categorical features consist of Brand, Chipset, CPU, GPU, Screen Type, Resolution, and Total Camera, while numerical features consist of RAM, Weight, Storage, Screen Size, Pixel Density, battery capacity, and Price for the target. The model that is proposed for the study will be a decision tree regressor, random forest regressor, k-nearest neighbor regression, and multilayer perceptron regression using two pre-processing data methods that is data normalization and data standardization. The result of the research is data normalization is the most impactful pre-processing data on regression models in terms of performance and error rates. Random forest regressor with no scaling performs better than other models with a score of 0.10425 and 0.97552 for MAPE and R2 respectively. © 2023 IEEE.

关键词： Smartphones

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Behavioral insights into compression: a study of Move-to-Front-or-Middle deterministic online algorithm through sequence classification and characterization

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International Journal of Information Technology (Singapore) 2025年第1期17卷 189-203页

作者： Baisakh Mohapatra, Hitesh Guru, Abhishek Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Sambalpur India Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to Be University Bhubanseswar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Since the list update problem was applied to data compression as an effective encoding technique, numerous deterministic algorithms have been studied and analyzed. A powerful strategy, Move-to-Front (MTF), involves moving an accessed item to the front of the list immediately. Theoretical analysis has demonstrated MTF’s potential, showing it to be 2-competitive through competitive analysis. This success has inspired researchers to develop various intriguing MTF variants and assess their performance both theoretically and experimentally. One such variant, Move-to-Front-or-Middle (MFM), has been experimentally shown that MFM performed better than the classic MTF on Calgary and Cantebury datasets. However, the experimental performance alone does not provide sufficient insight into the internal behavior of the algorithm or explain why its performance surpasses MTF on certain datasets. Therefore, in this paper, we conduct a detailed behavioral study of MFM through sequence classification and characterization to gain deeper understanding of the algorithm and aims to reconcile the disparity between theoretical and experimental outcomes. This paper also by classifying and characterizing MFM’s performance across various request sequences, considering both the presence and absence of locality of reference. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Competitive analysis Deterministic list update algorithm List update problem Sequence classification

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An In-Depth Review of Machine Learning, Blockchain, and Deep Learning Models for Intelligent Security and Resilience Enhancement in Smart Cities

An In-Depth Review of Machine Learning, Blockchain, and Deep...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Lanjewar, Umesh Khekare, Ganesh Wahi, Amitabh Bhagwant University Department of Computer Science & Engineering Ajmer India School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331517953

A smart city is a fast-moving terrain that requires efficient and smart security mechanisms with resilience for solving the intricate challenges of modern urbanism. The current paper presents the critical review of machine learning, blockchain, and deep learning models in strengthening security and making the urban environment at smart cities more resilient. Not all the review articles heretofore successfully integrated these three pivotal domains, which lowered their practical applicability and insight depth. This paper reviews recent state-of-the-art models, including supervised and unsupervised machine learning algorithms, blockchain frameworks, and deep advanced learning architectures. The important machine learning models reviewed include Random Forest, Support Vector Machines, and K-means clustering. These were chosen for their already proved effectiveness in anomaly detection, predictive analytics, and classification tasks. On the other hand, blockchain models of Ethereum and Hyperledger Fabric would be evaluated for decentralized security features, immutability, and capability to improve data integrity and transparency. Deep learning models can handle large-scaled, unstructured data and extract complex patterns. Their integration, therefore, shows great promise in enhancing the security and resilience of smart cities. This paper contributes to a holistic, multidisciplinary viewpoint, filling literature gaps by providing a basic framework for further research and implementation of smart city initiatives. Such insights synthesized across these domains set a course for innovation in the formulation of solutions that fortify urban infrastructures against emerging threats and enhance overall urban resilience. (Abstract) © 2024 IEEE.

关键词： Adversarial machine learning

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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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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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Experiments on IndoBERT Implementation for Detecting Multi-Label Hate Speech with data Resampling through Synonym Replacement Method 8

Experiments on IndoBERT Implementation for Detecting Multi-L...

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8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Darmawan, Michael Adriel William Boentoro, Nathanael Surya, Kevin Christian Sutoyo, Rhio School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315516

The freedom to share information online leads to increased online hate speech. Addressing this issue is crucial to creating better online communities. A study in 2019 used machine learning approaches to detect multi-label hate speech in the Indonesian language, achieving an accuracy of 66.12%. Recently, several researchers have shown improved performance when using the deep learning method. Looking at the opportunity, this work is developing an Indonesian hate speech model using IndoBERT. Various word preprocessing techniques and dataset stratification were explored for optimal results. In addition, this work also tried to solve the imbalance dataset problem in the multi-label dataset through the synonym replacement method. As a result, the IndoBERT model of this research achieved an improved accuracy of 88.23%. It was also implemented into a web-based application to show the potential usage. © 2023 IEEE.

关键词： Deep learning

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QuMIN: quantum multi-modal data fusion for humor detection

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Multimedia Tools and Applications 2025年第18期84卷 18855-18872页

作者： Phukan, Arpan Haq Khan, Anas Anwarul Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta Bihar Patna801106 India Kalinga Institute of Industrial Technology Odisha Bhubaneswar751024 India School of AI and Data Science Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India

Humour detection has attracted considerable attention due to its significance in interpreting dialogues across text, visual, and acoustic modalities. However, effective methods to map correlations among different modalities remain an active area of research. In this study, we go beyond traditional machine learning techniques by introducing a Variational Quantum Circuit (VQC) that capitalizes on the inherent quantum properties of superposition, entanglement, and interference. Our proposed model, Quantum Multi-Modal data Fusion (QuMIN), is designed to better capture and reproduce the interaction across modalities, as well as the internal correlations within each modality. Our introduction of the novel VQC, which augments the DialogueRNN baseline with only an additional 4,809 parameters, signifies a substantial advancement in multi-modal humor detection with improvements of 12.34% in precision, 8.84% in recall and 10.57% in F1 score compared to the state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine learning

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Privacy and Security in the Use of Voice Assistant: An Evaluation of User Awareness and Preferences 8

Privacy and Security in the Use of Voice Assistant: An Evalu...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Dhiya'mardhiyyah, Alya Latif, Jazlyn Jan Keyla Tho, Cuk School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Recently, voice assistants have become increasingly popular with millions of users relying on them to complete daily tasks and access information. While it may be convenient to use, some may question its security and are concerned about their privacy. While the users are attracted to the beneficial services provided by voice assistants, they are risking their personal information. This study aims to evaluate user awareness regarding the privacy and security aspects of voice assistants, and their preferences for future development of voice assistants. The literature review in this study obtained three vulnerabilities of privacy and security in the use of voice assistants and the countermeasures that can be taken. The user-centric countermeasures to mitigate the vulnerability of voice assistants were chosen for analysis by systematically evaluating 151 relevant journal and conference articles from 6 databases and 52 were chosen for analysis. To evaluate user awareness and preferences regarding the use of voice assistants, a questionnaire with a total of 37 participants who have experience in using voice assistants was done. The results showed that the participants were aware of the voice assistant's vulnerability and were concerned that their data might be misused. © 2023 IEEE.

关键词： privacy privacy and security security user awareness user preferences voice assistant

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Deepfake Technology: Negative Impacts, Mitigation Methods, and Preventive Algorithms 8

Deepfake Technology: Negative Impacts, Mitigation Methods, a...

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8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Gonzales, Nicholas Hernan Louis Lobian, Albert Hengky, Federigo Matthew Rendra Andhika, Muhammad Achmad, Said Sutoyo, Rhio Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315516

Deepfakes technology allows the act of generating fake content via deep generative methods. The current technology of deepfake techniques can create life-like images, audio, and fake video. Those outputs can be used for entertainment and education. However, it also has potential risks if it is utilized wrongly. For instance, fake content from deepfakes can spread malevolent propaganda to erode individual/organization/government character. It can also create ethical or legal problems about privacy, consent, and exploitation. This work explores the effects caused by deepfakes and summarizes essential items for policymakers, tech companies, and society to implement proper regulation. A systematic literature review is used as the basis for the methodology of this work by gathering data from pre-existing papers using certain keywords. The purpose is to promote awareness to prevent deepfake technology from being misused. © 2023 IEEE.

关键词： Deep learning

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A Literature Review of Challenges and Solutions in Cloud Security 10

A Literature Review of Challenges and Solutions in Cloud Sec...

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10th International Conference on Information Technology, computer, and Electrical Engineering, ICITACEE 2023

作者： Salim, Luis Harjono, Stephanus Gunawan, Ferdinand Moniaga, Jurike Bina Nusantara University School of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350322729

Cloud computing has emerged as a revolutionary paradigm that provides on-demand access to shared computing resources over the internet. However, it also poses significant security and privacy challenges, particularly in terms of data protection and incident management. This literature review explores the challenges and solutions to achieve a secure cloud computing ecosystem, such as natural disasters, system failures, hacking/malicious activities, and human errors, and discusses potential solutions, including network redundancy, intrusion detection, and disaster recovery. It also discusses common best practices, incident management, and disaster recovery models to ensure availability and data reliability. These practices ensure that the cloud ecosystem will be resilient to attacks and disasters from external and internal factors. As cloud technology continues to grow, we are likely to see improvement in cloud security practices and technologies like integration with machine learning and deep learning models, advanced patterns, or blockchain utilization. These improvements will help patch current known vulnerabilities and challenges in hope of a better and more secure cloud solution and ecosystem. © 2023 IEEE.

关键词： Cybersecurity

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Implementation of Augmented Reality in Android-based Application to Promote Indonesian Tourism 8

Implementation of Augmented Reality in Android-based Applica...

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

作者： Anderies Marvella, Maevy Hakim, Nissa Adila Seciawanto, Priskilla Adriani Chowanda, Andry Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Indonesia is known for its iconic tourism industry. However, there is a significant popularity difference between each destination. This imbalance has been a problem that the Indonesian Ministry of Tourism has been trying to solve through the 'Wonderful Indonesia" campaign. Unfortunately, some did not perceive it as a success. With the breakthrough of Augmented Reality (AR) that is seen as a great factor in enhancing user experience in the tourism sector, this research aims to design and develop an immersive android-based application that implements Augmented Reality and Audio Guidance to promote and create branding for Indonesian tourism spots. This research uses a methodology divided into 4 phases;Analysis Requirement, Research and Collect data, System Design and Development, and Testing. A survey was conducted in the Analysis Requirement phase to find people's knowledge and preference for tourism spots in Indonesia. Another survey was conducted in the Testing phase to test the application's usability and whether the aim has been achieved. The main findings indicate that most local tourist respondents stated that the application developed is an effective way for them to explore and educate themselves about Indonesian tourism sites. Therefore, this application might be a sustainable answer and a new approach compared to what the Indonesian government has done in the past years. © 2023 The Authors. Published by Elsevier B.V.

关键词： Android Augmented Reality Indonesian Tourism Usability Testing

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