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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Setiawan, Devin Karnyoto, Andrea Stevens Intan, Indo Pardamean, Bens Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83. © 2024 IEEE.

关键词： Lung cancer

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Authorship Attribution in Bahasa Indonesia Using Twitter dataset on Political Topic 4

Authorship Attribution in Bahasa Indonesia Using Twitter Dat...

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

作者： Muliono, Yohan Gaol, Ford Lumban Chowanda, Andry Budiharto, Widodo Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Fake news so called made-up news intended to cause misinformation is identified as Hoax. In Indonesia, hoaxes could not be ignored, fairly low literacy rate is the cause of hoaxes could spread quickly in Indonesia, to make it even worse, there are so many people would believe in hoaxes without any intention to make sure of the integrity of the news. even the media in Indonesia are competing with one another to provide content with provocative names in the hopes of increasing their rating and reaching as many people as possible. In the course of this research, a method for identifying authors on the basis of their writings will be developed to reduce the spread of hoaxes and encourage people to re-evaluate their decision to create harmful content and hoax news in the future. In this investigation, a transformer-based methodology focusing on IndoBERTbase, IndoBERTbase-uncased, and IndoBERTtweet will be employed in conjunction with the self-collected Indonesian tweet data that will be crawled from Indonesian Social Actor Focused on politicians in Twitter and will be combined with another topic in the future study. This research has shown a promising 98,93% in Training Accuracy using IndoBERTtweet. © 2023 IEEE.

关键词： Curricula

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The Effect of Social Media Influencers on Business Sustainability: Understanding Customer Behavior Change Towards Sustainable Usage Patterns 6

The Effect of Social Media Influencers on Business Sustainab...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Herlina, Maria Grace Budiharto, Alexandra Li Lukito, Elizabeth Margaretha Iskandar, Karto Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

The study aims to fill existing research gaps by creating a new framework that explores the impact of social media influencers on sustainable usage patterns, focusing on business sustainability as a mediating factor. In this research, non-probability judgmental and purposive sampling methods were used. Due to the size and distribution of the population, sampling was also chosen based on the researcher's own judgment and experience. Structural equation modeling (SEM) is used as the data analysis method. Path coefficient analysis yielded significant findings. Social media influencers were found to influence business sustainability significantly. Apart from that, social media influencers have a significant negative affect on sustainable usage patterns. Business sustainability also significantly influences sustainability usage patterns, with a powerful influence. This enriches the dynamics of business sustainability, proposing new knowledge about how it influences this relationship. © 2024 IEEE.

关键词： Sustainable development goals

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Systematic Literature Review on IT Strategic Workspaces: Approach to Develop an Ideal Environment 9

Systematic Literature Review on IT Strategic Workspaces: App...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Putra, Alden Ardiwinata Fusanto, Princessa Fortunata Valonika, Nebrisca Anggreainy, Maria Susan Kurniawan, Afdhal Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Graduate Program Doctor of Computer Science Computer Science Department Bina Nusantara University Jakarta11530 Indonesia

ISBN: (纸本)9798350370126

Technology and information will always develop dynamically;this statement demands programmers to always be creative and keep up with the times. Despite this, their work ethic is always the same and tends to stagnate. programmers' perspectives on their workplace: this perspective is rarely seen as important and the opportunity for a comprehensive study is still widely open. This study aims to conduct a systematic literacy study to ascertain that the workplace has a crucial relationship with a programmer's performance. The focus of the study includes a discussion of how programmers relate to their workplace in a tech company, benchmarking between various office layouts, and criteria that can be derived from the literature review regarding a degree of whether such a workplace is good or bad. The proper base facts are obtained. Nevertheless, the two entities are proven to be interrelated to each other. Such correlations are derived into tangible metrics and parameters (e.g., human cognitive, cultural, physical, behavioral, etc.). Relating issues regarding these topics are also presented. © 2023 IEEE.

关键词： Software engineering

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Effects of feature selection and normalization on network intrusion detection

Data Science and Management

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data science and Management 2025年第1期8卷 23-39页

作者： Umar, Mubarak Albarka Chen, Zhanfang Shuaib, Khaled Liu, Yan College of Information Technology United Arab Emirates University Al Ain15551 United Arab Emirates School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China Department of Computer Science Shantou University Guangdong Shantou515063 China

The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence (AI) techniques (such as machine learning (ML) and deep learning (DL)) to build more efficient and reliable intrusion detection systems (IDSs). However, the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs. Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues. While most of these researchers reported the success of these preprocessing techniques on a shallow level, very few studies have been performed on their effects on a wider scale. Furthermore, the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used, which most of the existing studies give little emphasis on. Thus, this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets: NSL-KDD, UNSW-NB15, and CSE–CIC–IDS2018, and various AI algorithms. A wrapper-based approach, which tends to give superior performance, and min-max normalization methods were used for feature selection and normalization, respectively. Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization. The models were evaluated using popular evaluation metrics in IDS modeling, intra- and inter-model comparisons were performed between models and with state-of-the-art works. Random forest (RF) models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86% and 96.01%, respectively, whereas artificial neural network (ANN) achieved the best accuracy of 95.43% on the CSE–CIC–IDS2018 dataset. The RF models also achieved an excellent performance compared to recent works. The results show that normalization and feature selection positively affect I

关键词： Decision trees

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Evaluation of REAL-ESRGAN Using Different Types of Image Degradation 11

Evaluation of REAL-ESRGAN Using Different Types of Image Deg...

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

作者： Andriadi, Kus Zarlis, Muhammad Heryadi, Yaya Chowanda, Andry Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia Binus Graduate Program Bina Nusantara University Information System Management Department Jakarta11480 Indonesia School of Computer Science Binus University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350389654

This study evaluates the performance of the REAL-ESRGAN [1] model on images with varying levels of degradation using the DIV2K dataset [2], such as the Wild, the Mild, the Difficult, and the x8 subsets. REAL-ESRGAN was created to solve super-resolution problems and aims to produce high-resolution images from low-resolution images. Experiments were conducted at scales of x2 and x4, and performance was measured using Full-Reference metrics (LPIPS, PSNR, SSIM) and No-Reference metrics (NIQE, MANIQA, CLIPIQA, and PI). The Results were good, especially with the x2 scale;it has higher PSNR and SSIM scores, lower LPIPS and NIQE values, and enhanced visual and perceptual quality. The model faced more significant challenges with the wild and the difficult datasets because they have more complex degradations and compression artifacts;it can be seen with unstable results of Full-Reference and No-Reference metrics. On the contrary, the Mild and x8 datasets yielded better results in both metrics;not only that, even the computational cost for Mild and x8 outperforms the rest of the dataset. This study shows the strengths and limitations of REAL-ESRGAN in handling different levels of image degradation. For future research, the model needs enhancement to tackle the degradation format of the wild and the difficult dataset. It would be good if the REAL-ESRGAN improvement could also maintain the computational cost. © 2024 IEEE.

关键词： Gallium nitride

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Performance Analysis of Distributed database System in Cloud Computing Environment 9

Performance Analysis of Distributed Database System in Cloud...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Prasetio, Nicholas Ferdinand, Daniel Wijaya, Brandon Christopher Anggreainy, Maria Susan Kumiawan, Afdhal Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program School of Computer Science Jakarta11530 Indonesia

ISBN: (纸本)9798350370126

This research discusses the performance evaluation of distributed database systems in a cloud computing environment Cloud computing environments allow data and applications to be stored and deployed on infrastructure located in different parts of the world. However, the use of distributed database systems in cloud computing environments can cause performance issues, such as complex data access and factors such as network latency, security, and scalability that affect system performance. Therefore, performance evaluation of distributed database systems is necessary to ensure effective data management across the infrastructure. The purpose of this research is to measure and understand the performance of distributed database systems in a cloud computing environment. This is important because proper performance evaluation is needed to ensure distributed database systems can operate effectively and efficiently in such environments. This research will analyze the features of distributed database systems, factors that affect performance, how to measure and compare system performance, and how to improve system performance. Analyzing the performance of distributed database systems in a cloud computing environment can help users choose the most appropriate and efficient cloud computing platform for their business needs and improve operational efficiency. © 2023 IEEE.

关键词： Distributed database systems

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Efficiency Analysis of Brute Force and Knuth Morris Pratt Algorithms for Indonesian Keyword Search on KBBI 5

Efficiency Analysis of Brute Force and Knuth Morris Pratt Al...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Alvin, Alvin Ramadhany, Daffa Gusti Insan Rabbani, Raya Suryaningrum, Kristien Margi Amalia Saputri, Hanis Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350369489

Keyword search in Indonesian in the Big Indonesian Dictionary (KBBI) is an important problem in the field of information technology, because KBBI is the main reference source in determining the meaning and significance of words in Indonesian. In the keyword search process, there are several algorithms that can be used, one of which is the Brute Force and Knuth Morris Pratt (KMP) algorithms. In this research, we will analyze the efficiency of both algorithms in searching keywords in KBBI. The Brute Force algorithm will compare each character of the keyword with each character in each word in the KBBI. While the KMP algorithm will use a table to match each character in the keyword with each character in each word in the KBBI. The method used for testing the algorithm is to use the KBBI word data set. This test will analyze the running time, memory usage, precision, accuracy, and keyword related of the two algorithms in completing the keyword search in KBBI. The result of this research is a comparison of the complexity and speed of the Knuth Morris Pratt and Brute Force algorithms. The outcomes demonstrate that while searching for keywords in the KBBI, the Brute Force approach is more effective than the KMP algorithm. The Brute Force algorithm has faster execution time, accuracy, and precision compared to the KMP algorithm, the KMP algorithm can be seeded by using less memory than the Brute Force algorithm. So it can be concluded that the Brute Force algorithm is more efficient in searching keywords in the KBBI compared to the KMP algorithm. As a result, the Brute Force algorithm can be utilized as a substitute to quicken the KBBI's keyword search process. © 2023 IEEE.

关键词： Efficiency

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An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

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computer Systems science & Engineering 2023年第2期44卷 1309-1324页

作者： Junaid Rashid Sumera Kanwal Muhammad Wasif Nisar Jungeun Kim Amir Hussain Department of Computer Science and Engineering Kongju National UniversityCheonan31080Korea Department of Computer Science COMSATS University IslamabadWah CampusIslamabad47040Pakistan Centre of AI and Data Science Edinburgh Napier UniversityEdinburghEH114DYUK

In project management,effective cost estimation is one of the most cru-cial activities to efﬁciently manage resources by predicting the required cost to fulﬁll a given ***,ﬁnding the best estimation results in software devel-opment is ***,accurate estimation of software development efforts is always a concern for many *** this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artiﬁcial neural network(ANN).An artiﬁcial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model *** state-of-the-art publicly available datasets are used for *** backpropagation feed-forward procedure used a training set by iteratively processing and training a neural *** proposed model is tested on the test *** estimated effort is compared with the actual effort *** results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.

关键词： Software cost estimation neural network backpropagation forward neural networks software effort estimation artiﬁcial neural network

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Analysis of the Utilization of Human Motion Tracking in the Security Sector: Examining the Accuracy Level and Key Factors in Operation 6

Analysis of the Utilization of Human Motion Tracking in the ...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Indradjaja, Nicholas Chen, Michael Masada, Hossey Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia BINUS Graduiate Program Doctor of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350358346

The use of surveillance cameras (CCTV) is very limited and has many weaknesses. Surveillance cameras can only record events that are happening without being detected, so supervisors still need protection. From these limitations, it is necessary to use Human Motion Tracking on surveillance cameras that can help detect objects caught on camera. This paper analyzes each Human Motion Tracking method in terms of the level of accuracy of the supporting or inhibiting factors, as well as the estimated costs required. This paper will compare each of the methods found to conclude that the Human Motion Tracking method has the highest detection accuracy, equipped with adequate key factors, and accompanied by a balanced cost. In conclusion, this study recommends the Kalman Filter method as the most optimal choice for human Motion Tracking on surveillance cameras, offering a balanced combination of accuracy and efficiency. The implementation of this method is expected to contribute significantly to enhancing security measures and creating a safer environment by effectively monitoring potential security issues. © 2023 IEEE.

关键词： Cameras

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