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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Oktavia, Tanty Lintang, Febian Wijaya Ongko, Gerald Christian Benedicta, Griselda Valerie Michelle Sembiring, Vieri Aska Juneio Ananta, Zaldy Raditya Hosoda, Takaaki Gaol, Ford Lumban Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan Bina Nusantara University BINUS Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

Smart Home technology offers technological concepts that can be controlled through smart devices to make it easier for the community. Therefore, smart home technology is starting to seize the home electronics market in Indonesia, especially in the city of Jakarta. This study aims to determine the integration of conventional technology into smart home technology for people in Jakarta. The research model in this study uses UTAUT2 which analyzes nine variables, namely Perceived Usefulness, Perceived Cost, Perceived Ease of Use, Social Influence, Perceived Compatibility, Trust, Perceived Pleasure, and Effort Expectations, Intention to Use Smart Home Technology. After the responses were received, data analysis was carried out using the PLS-SEM approach with the SmartPLS application. The results of this study show that there are many people who live in Jakarta who are helped by the presence of smart home technology. However, there are some people who don't really know how to use a smart home because of a lack of knowledge about this technology. © 2023 IEEE.

关键词： Technology transfer

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Metasurfaces for next-generation wireless communication systems

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National science Review 2023年第8期10卷 17-19页

作者： Younes Ra'di Nikita Nefedkin Petar Popovski Andrea Alù Department of Electrical Engineering and Computer Science Syracuse University Photonics Initiative Advanced Science Research Center City University of New York Department of Electronic Systems Aalborg University Physics Program Graduate Center City University of New York

The pace of development in the world of 5G communication systems has proven to be much more demanding than previous generations, with 5G-Advanced seemingly around the corner [1]. Extensive research is already underway to structure the next generation of wireless systems(i.e. 6G), which may potentially enable an unprecedented level of human–machine interaction [2].

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Leveraging BERT with Extractive Summarization for Depression Detection on Social Media 23

Leveraging BERT with Extractive Summarization for Depression...

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23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022

作者： William, David Achmad, Said Suhartono, Derwin Gema, Aryo Pradipta Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Epigene Labs United States

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

In our current time, the well-being of a person is not only determined by the physical health, but also by their mental health. A lot of focus and effort have been spent into raising the awareness of this issue. One such effort comes from the field of computer science by utilizing data from social media to provide additional information in detecting these mental disorders. In this research, authors proposed the Bidirectional Encoder Representations from Transformers (BERT) with extractive summarization to preprocess data obtained from popular social media platform such as Reddit and Twitter. The proposed method evaluated by compare it to several other methods in text classification to Figure out which method is best classifying whether someone is depressed or not. This research also contributes to evaluate whether applying extractive summarization to a document prior to feeding the data into BERT will contribute to an increase in performance when compared to the current method of data preprocessing, by measuring the accuracy, precision, recall and F1-score of each model. The evaluation result found that our proposed method by applying extractive summarization to reduce the sequence length achieved a better result compared to BERT base model. © 2022 IEEE.

关键词： Text processing

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Ransomware Resilience: Investigating Organizational Security Culture and Its Impact on Cybersecurity Practices against Ransomware Threats

Ransomware Resilience: Investigating Organizational Security...

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Informatics Engineering, science & Technology (INCITEST), International Conference on

作者： Semi Yulianto Benfano Soewito Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

The increasing threat of ransomware attacks in an interconnected and digital world presents important hurdles for cybersecurity, prompting further examination into how organizational security culture affects their ability to withstand these malicious threats. This study examines the impact and importance of organizational security culture in ransomware threat mitigation. We have surveyed five organizations, giving points from 1 to 5 for each question on cultural factors, leadership support, employee awareness and involvement (compliance), communication, behavior change or BBS practice incorporation, learning/training delivery methodology, and Size and industry impact on their security culture. The results indicate that a robust security culture, with leadership and effective communication, builds stronger resilience to ransomware. Organizations with jointly accountable cybersecurity and training practices are highly efficient. Suggestions range from establishing a secure environment to increasing management buy-in and integrating compliance. This work helps advance our understanding of ransomware resilience and highlights the key role of organizational security culture. The resulting learnings can help to inform organizations' security posture, ultimately helping them better protect their defenses against ransomware threats.

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BERT and ULMFiT Ensemble for Personality Prediction from Indonesian Social Media Text

BERT and ULMFiT Ensemble for Personality Prediction from Ind...

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2022 International Symposium on Information Technology and Digital Innovation, ISITDI 2022

作者： Halimawan, Noptovius Suhartono, Derwin Gema, Aryo Pradipta Yunanda, Rezki Binus Graduate Program-Master Of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School Of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Epigene Labs Paris France

ISBN: (数字)9781665461160

ISBN: (纸本)9781665461160

Predicting personality is a growing topic in the field of natural language processing. The study of personality prediction has been proven to benefit the development of recommender systems and automated personality assessments by previous studies. Additionally, the widespread usage of social media in Indonesia such as Twitter has served as a potential source of data for developing such models. Existing personality prediction models has explored the implementation of both traditional machine learning models and deep learning models, with the latter proven to perform better with more data. Despite so, there is not much development of deep learning-based personality prediction models in the domain of Indonesian text. We propose a deep learning-based ensemble model based on Universal Language Model Fine Tuning (ULMFiT) and Bidirectional Encoder Representations from Transformers (BERT), the state-of-the-art pre-trained models for text classification. The model is trained with Indonesian tweets labelled with Five Factor Model (FFM) personality traits along with the users' metadata. The model is tested with five predetermined scenarios to prove the robustness of its performance. The final evaluation of the model proves that it has successfully achieved optimal performance of 76.41 % macro-average accuracy and 75.55% macro-average F-1 score, exceeding the state-of-the-art BERT and ULMFiT © 2022 IEEE.

关键词： Classification (of information)

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Envisioning Risk Management for Smart Contracts: A Conceptual Model

Envisioning Risk Management for Smart Contracts: A Conceptua...

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Informatics Engineering, science & Technology (INCITEST), International Conference on

This study proposes a smart contract risk management model built on the NIST Risk Management Framework (RMF) to help identify, assess, and manage the risks of smart contracts. While smart contracts are beloved as a means to automate and disintermediate business processes, their security vulnerabilities can be critical. The main issue discussed in this paper is the lack of a holistic approach to risk management smart contracts. The resulting framework consists of six steps: Risk identification, assessment, prioritization, mitigation, testing, and continuous monitoring (and was developed through reviewing existing literature on smart contract security and the NIST RMF). It is recommended that a case study be performed to prove the proposed model's effectiveness in managing the risks of smart contracts and minimizing financial losses and reputational harm. The paper presents a risk management framework for smart contracts to increase trust and adoption to enhance security while reducing financial losses and reputation damage. This has wider implications for the security of smart contracts and can be used as a starting point for future work. This study is expected to significantly contribute to smart contract security by introducing an organized way to address these contracts' risks using the NIST RMF.

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Variational quantum neural network with optimized ansatz for predicting malignant ventricular arrhythmias

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Procedia computer science 2024年 245卷 109-116页

作者： Nicholas Dominic Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Preventive strategies should be the utmost priority when dealing with diverse patients suffering from malignant ventricular arrhythmia (MVA) that can lead to sudden cardiac death (SCD). Electrocardiogram (ECG) data is commonly used as a predictor for MVA predictive models. In this study, all ECG signals from MIT-BIH databases were fragmented into five-minute durations with a frequency sampling of 128 Hz. To solve the absence of hybrid optimizations in Machine Learning (ML) models, a novel Variational Quantum Neural Network (VQNN) was invented. Empowered by deep learning capabilities and optimized quantum circuits design, VQNN achieved remarkable performances designated by an accuracy of up to 95.1%, a perfect 100% recall, and a 95.2% score of the area under the Receiver Operating Characteristic curve (AUC ROC) with Conjugate Gradient as an optimizer and EfficientSU2 as a quantum ansatz. Despite the susceptibility to quantum noise, this research settles a new trajectory of utilizing quantum variational algorithms to predict and expand its applicability for MVA cases.

关键词： Malignant Ventricular Arrhythmia Sudden Cardiac Death Variational Quantum Algorithm Quantum Computing Neural Network

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ICT-Based Conceptual Model for Local Culture Communities using Soft System Methodology 7

ICT-Based Conceptual Model for Local Culture Communities usi...

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

作者： Nindito, Hendro Prabowo, Harjanto Sfenrianto Hendric, Spits Warnars Harco Leslie BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The problems that exist in the field of art and culture preservation experienced by the arts and culture community side are the limitations on physical facilities for disseminating works, exchanging information between arts and culture communities, as well as the need to increase professionalism so that they can be independent in order to help improve the economy, where the role of regulation in this regard also affects this condition. Based on the facts above, this community requires an interaction model that is integrated with the use of information technology. This study uses a Soft System Methodology approach to create a model that is suitable for the arts and cultural community. The result is an ICI-based conceptual model for a local culture community. (Abstract) © 2022 IEEE.

关键词： Information use

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Implementation of computer Vision of Jakarta Weather Image Categorization Using ResNet 8

Implementation of Computer Vision of Jakarta Weather Image C...

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

作者： Purwandari, Kartika Sigalingging, Join W.C. Hidayat, Alam Ahmad Cenggoro, Tjeng Wawan Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

At the moment, weather data is crucial for supporting neighborhood activities. The economy and trade are both centered in Jakarta, which is also Indonesia's capital. Therefore, it is crucial to have access to weather information so that these activities don't get disrupted, which would then hinder commercial and trade activity. Social media has been a very popular tool for spreading information recently. Particularly on Instagram, where users favor taking images and sharing the information they encounter. @jktinfo is the Instagram account that posts information about the situation in Jakarta and the area, including the current weather. The @jktinfo account is utilized in this project to gather data. Utilizing a variety of techniques, the collected photographs of sunny, cloudy, and wet situations were. © 2023 The Authors. Published by Elsevier B.V.

关键词： computer vision data augmentation deep transfer learning residual network weather image categorization

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Exploring Transfer Learning Approach for Environmental Sound Classification: A Comparative Analysis

Exploring Transfer Learning Approach for Environmental Sound...

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Smart Computing, IoT and Machine Learning (SIML), International Conference on

作者： Theresia Herlina Rochadiani Yulyani Arifin Derwin Suhartono Widodo Budiharto Computer Science Department Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

The expansion of deep learning techniques, as well as the availability of large audio/sound datasets, have fueled tremendous breakthroughs in audio/sound classification during the last several years. The transfer learning approach has emerged as one of the primary approaches for improving the accuracy and durability of classification systems. This study conducts a comprehensive comparative analysis to determine the effectiveness and performance of this method in environment sound classification. This current investigation focuses on environmental sound classification using VGGish and YAMNet pre-trained models with the ESC-50 and BDLib2 datasets. In the ESC-50 dataset, VGGish improves accuracy to 372.22%, while YAMNet improves accuracy to 383.33% when compared to baseline models. Similarly, in the BDLib2 dataset, accuracy increases significantly to 221.43% with VGGish and 246.43% with YAMNet. Transfer learning exhibits remarkable effectiveness in enhancing model performance, with significant accuracy boosts observed in both datasets. YAMNet, designed specifically for sound classification tasks, surpasses VGGish in improving environmental sound classification performance, potentially due to its architecture’s adaptability and diverse training on environmental sounds.

关键词： Training Deep learning Adaptation models Analytical models Accuracy Computational modeling Transfer learning

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