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检索条件"机构=Computer Science Department BINUS Graduate Program - Doctor of Computer Science"

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The Role of Industry 4.0 Technology in Enhancing Small Medium-Sized Enterprises Stability and Growth 15

The Role of Industry 4.0 Technology in Enhancing Small Mediu...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Aprilinda, Yuthsi Warnars, Harco Leslie Hendric Spits Meyliana, Meyliana Prabowo, Harjanto Hidayanto, Achmad Nizar Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350383829

The purpose of this study is to find out and analyze what has been done by previous studies in knowing the problems faced by SMEs today and how the influence of Industry 4.0 technology in dealing with problems in Small and Medium Enterprises (SMEs), so that they become developed and can maintain their business continuity. Most studies show that SMEs have considerable barriers when trying to use the potential of Industry 4.0 to improve the competitiveness and long-term sustain ability of their business. This systematic literature review uses the Kitchenham technique to identify SME problems and the role of Industry 4.0 technologies in SMEs in various industrialized and developing countries. Through several databases including: IEEE, science Direct, Web of science, Taylor & Francis, Emerald and Springer between 2019 - 2023 were used to find relevant research articles in this study. The research findings offer valuable perspectives on the strategies, obstacles and prospects that SMEs in different countries face in adjusting to Industry 4.0. The contribution of academics in this research is to provide solutions by designing more robust approaches to promote the business sustainability of SMEs and their role in driving economic progress in their countries. © 2023 IEEE.

关键词： Sustainable development

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Development of Sundanese Stemmer Based on Morphophonemics 10

Development of Sundanese Stemmer Based on Morphophonemics

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

作者： Maesya, Aries Arifin, Yulyani Zahra, Amalia Budiharto, Widodo BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Binus Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

The Stemming Process is used to get the base word by removing the word affixes. The purpose of this study is to improve the algorithm in previous research by incorporating the Stemming Process into a morphophonemic approach for language processing based on rules (Rule Based) which is named AM Sundanese Stemmer (AMS). The AM Sundanese stemmer algorithm will utilize morpheme information to perform stemming on Sundanese words and analyze the phonemes in a word to identify the morphemes therein contained. The experimental process was carried out with 11,885 existing data and a sample of 546 words were taken to be stemmed. The test was carried out by comparing the algorithms of Nazief and Adriani, UG 18 and AMS. The test results showed that the AMS Algorithm was better than the Nazief Andriani Algorithm and the UG18 Algorithm with a high F-1 score of 87.52% and the comparison of the execution time of the AMS Stemmer Algorithm was 0.041403 seconds faster than the Nazief Andriani and UG 18 Algorithms. © 2023 IEEE.

关键词： Rule Based Stemming Sundanese Stemmer

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Implementation of the EfficientNet Model for Identification of Wild Edible Plants 4

Implementation of the EfficientNet Model for Identification ...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Anggreainy, Maria Susan Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350376111

Wild plants are plants that are not cultivated but wild plants can be easily found in the neighborhood. However, not all wild plants can be utilized as food crops by the community. Therefore, it is necessary to know what wild plants can be consumed so that people do not have to worry about food availability. The purpose of this study is to determine whether the EfficientNet model can be used to identify wild plants that can be consumed. Experimental results show that the EfficientNet model has an accuracy of 85% in identifying wild plants that can be consumed. © 2024 IEEE.

关键词： convolutional neural network (CNN) edible wild plant EfficientNet image classifier machine learning

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Performance Analysis of Heart Disease Prediction using Kernels in Support Vector Machine Model 4

Performance Analysis of Heart Disease Prediction using Kerne...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Anggreainy, Maria Susan Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350376111

Linear data is a collection of data that is linearly separated by a straight line and nonlinear data is a collection of data that is not separated by a line. Usually, non-linear data is separated by irregular curves. However, for non-linear data. Support Vector Machine (SVM) is difficult to classify data for non-linear data. The solution is to use Kernel Trick. Kernel Trick is a simple method used to map low-dimensional nonlinear data and transform it into a higher-dimensional space. The goal is to make it easier to classify data by finding a hyperplane that can separate the dataset linearly well. This research is to create a program that can predict heart disease using the Support Vector Machine (SVM) model with Kernel Tricks. Heart disease prediction is very important because the heart is one of the most important organs in the human body. The results indicated that the SVM model with the Radial Basis Function (RBF) Kernel achieved the highest accuracy compared to the Linear Kernel and Polynomial Kernel. © 2024 IEEE.

关键词： Support vector regression

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A Decision Model for Smart Village in Measuring Socioeconomic Inequality 11

A Decision Model for Smart Village in Measuring Socioeconomi...

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

作者： Utama, Ditdit Nugeraha Binus Graduate Program - Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350389654

Making socio-economic decisions is critical as they profoundly impact communities. It is imperative to approach these decisions with academic rigour and objectivity to effectively address prevalent community-wide issues. One such challenge often encountered by communities is socio-economic inequality. This study attempts to tackle this challenge by creating a decision model to assess socio-economic inequality in a region. The research was performed via four crucial stages, leading to the creation of a fuzzy decision model based on the Williamson Index (WI) method. This carefully developed model was designed and constructed using an object-oriented approach. The simulation results indicated that the integrated inequality index value for Sawah, an urban village in Ciputat, Indonesia, is 0.65. This em-pirical result provides valuable insights, enabling policymakers and stakeholders (as decision-makers) to design targeted policies and programs to reduce inequality and enhance community prosperity. © 2024 IEEE.

关键词： fuzzy logic inequality index object-oriented smart decision model smart village socio-economic

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Detection of Narcotics Money Laundering in Suspicious Financial Transactions Using Convolutional Extreme Gradient Boosting (ConvXGB) 4

Detection of Narcotics Money Laundering in Suspicious Financ...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Winarno, Leonardus Verrell Sayoga Kinantra Wibowo, Antoni School of Computer Science Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331506490

With the development of narcotics problems that continue to increase, the Indonesian Government responds through the Badan Narkotika Nasional (BNN) with data showing the condition of narcotics tends to grow every year and is followed by the number of assets of money laundering (ML) crimes against narcotics cases. One of the banks in Indonesia as a reporting party for suspicious financial transactions (SFT) has obstacles in detecting narcotics ML due to complex and rarely found patterns. Some previous studies conducted experiments using Convolutional Neural Network (CNN), Extreme Gradient Boosting (XGBoost), and even a combination of both into Convolutional Extreme Gradient Boosting (ConvXGB), and improved model performance in several datasets. This paper designs a model using the ConvXGB algorithm by adopting the CNN architecture, LeNet-5, by applying several convolution layers and pooling layers as a baseline model for feature learning, and the XGBoost as feature classification. Three phases of research are the preprocessing phase by collecting data, transforming data, balancing data with a hybrid sampling technique, splitting data, and scaling data, followed by the implementation phase by creating a ConvXGB model, training and testing the model, then finally the evaluation phase by analyzing results and hyperparameter tuning. The dataset used is SFT from the bank during 2023. This ConvXGB has three convolution layers, a pooling layer, and a flattened layer. The performance test results are the accuracy value and F1-Score value of 99.11% each after hyperparameter tuning. By performing a hybrid model, the model performance results are better. © 2024 IEEE.

关键词： Laundering

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Drug Demand Prediction Based on Epidemiology Factors Using Random Forest 15

Drug Demand Prediction Based on Epidemiology Factors Using R...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Emmanuel, Gunadi Arifin, Yulyani Sonata, Ilvico Zarlis, Muhammad Bina Nusantara University Doctor of Computer Science Program Jakarta Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia Binus Graduate Program Bina Nusantara University Information System Management Department Jakarta Indonesia

ISBN: (纸本)9798350383829

The characteristics of the disease that spreads quickly, the number of sufferers, and the severity of sufferers of Coronavirus Disease 2019 are components of uncertainty during the pandemic. In an uncertain situation, prediction models for the need for drugs and medical devices are of great concern to policymakers in government, drug manufacturers, distributors, and pharmaceutical installation managers to maintain drug availability. Drug need prediction models that rely on historical data components on drug use are no longer reliable. Learning from the COVID-19 case, epidemiological variables correlate with predicting drug demand. This research includes data on ten major diseases in private hospital units for 2017-2022 to complete historical data on drug use. This study implements the Random Forest algorithm. The research method uses literature studies and processing field data from pharmaceutical installations. The analysis process uses KNIME software. The level of accuracy in predicting drug demand from historical drug use data was 77.272%, increasing to 81.818% with a model for predicting drug demand based on consumption cycles and classification of drug therapy groups. Furthermore, predictions of drug demand can consider variables recorded in medical records related to the seasonal frequency of diseases. © 2023 IEEE.

关键词： Forecasting

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Metrics and Red Teaming in Cyber Resilience and Effectiveness: A Systematic Literature Review 29

Metrics and Red Teaming in Cyber Resilience and Effectivenes...

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29th International Conference on Telecommunications, ICT 2023

作者： Yulianto, Semi Soewito, Benfano Gaol, Ford Lumban Kurniawan, Aditiya Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350361100

Rising cyber risks have compelled organizations to adopt better cyber-protection measures. This study focused on discovering crucial security metrics and assessing the function of red teaming in enhancing cybersecurity defenses against novel cyber hazards. The PRISMA standard considered nine core research works issued between 2014 and 2023. The inclusion of red teaming best practices can significantly enhance cybersecurity architecture. Accurate simulations of cyber threats during red teaming exercises help identify vulnerabilities, and actively embracing red teaming can amplify an organization's capacity to repel future cyber assaults. Researchers and practitioners can utilize the study's insights to pioneer novel security solutions. Combining red teaming methodologies with relevant metrics is essential for enhancing cybersecurity posture. The study's discoveries grant companies a priceless benefit in navigating the rapidly changing cyber threat environment and reinforcing their cyber protection mechanisms. © 2023 IEEE.

关键词： Cybersecurity

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Random Number Generator for Securing Data Exchange in Smart Factory: Recent Trends and Best Practices 3

Random Number Generator for Securing Data Exchange in Smart ...

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3rd International Conference on computer Technologies, ICCTech 2024

作者： Sama, Andi Meyliana Heryadi, Yaya Sahroni, Taufik Roni Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information System Information System Department Jakarta11480 Indonesia BINUS Graduate Program-Doctor of Computer Science Jakarta Computer Science Department Jakarta11530 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Industrial Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350395419

IT/OT convergence in the form of a Cyber-Physical System (CPS) presents an opportunity to apply recent Information Technology (IT) advancements to Operational Technology (OT). The IT/OT convergence introduces cyber security threats for OT. The OT communication protocols, for example, have limited or no security capabilities for securing data exchange beyond basic authentication. The RNG (Random Number Generator) function is crucial in PLC for producing the encryption keys or the initial seed for security algorithms. The software-based PRNG (Pseudo RNG) is the most common approach for RNG in OT, instead of hardware-based RNG. This study provides a systematic literature review of the most recent evidence in digital transformation to secure data exchange in smart factories, within OT, or across OT and IT settings. It does this by looking at the PRNG on PLC and identifying the most recent trends and best practices. The findings include up-to-date data on author countries' distribution, published journals, research trends, industries, and industry types. The findings also address whether data flow is encrypted or shared in plaintext to ensure secure data transfer between IT and legacy OT environments. This paper concludes that while there is known RNG implementation in the actual embedded system in ICS, there is no evidence in the literature for using RNG in ICS networks for PLC implementation to secure data transfer with encryption. © 2024 IEEE.

关键词： Data transfer

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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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