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Analyzing the Impact of Service Design on Maintainability Factor in Microservices Architecture

Analyzing the Impact of Service Design on Maintainability Fa...

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Advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Gintoro Ford Lumban Gaol Haryono Soeparno Yulyani Arifin Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Applications designed utilizing Microservices Architecture (MSA) provide the desirable trait of good maintainability. To ensure optimal maintainability, it is important to provide services that are suitable and adhere to prescribed rules. Multiple aspects must be taken into account while designing services to ensure optimal maintainability. The objective of this study is to examine the elements that impact the capacity to sustain and improve maintainability in service design, ultimately resulting in an application that possesses strong maintainability. The Systematic Literature Review (SLR) will be utilized to identify variables and strategies for their enhancement, by examining pertinent publications on the subject. After examining 45 publications, the study discovered 8 elements and 14 solutions that can enhance the highlighted parameters throughout the services design process. The outcomes of this systematic literature review (SLR) are anticipated to give valuable insights to application developers, empowering them to generate service designs that exhibit commendable maintainability for the developed applications.

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

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

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Advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

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

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.

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Neural Network and Resampling to Handle Imbalance Class on Estimate Software Defect

Neural Network and Resampling to Handle Imbalance Class on E...

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Advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Tri Agus Setiawan Haryono Soeparno Ford Lumban Gaol Yulyani Arifin Computer Science Department BINUS Graduate Program — Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Rapid progress in the field of artificial intelligence has created opportunities for extensive applications in software development. One area that receives attention is the evaluation of code quality using machine learning techniques. In this investigation, we examined the possible application of machine learning to predict the likelihood of defects in computer code. We employ NASA archival data as case studies. Machine learning models employ neural network algorithms. Our exploration involves partitioning the dataset into training data and test data for performance evaluation. The findings indicate that the Neural Organize technique with resampling yields a high level of accuracy in predicting software defects. Our simulated neural network is capable of identifying intricate patterns in the data and providing precise measurements of the size and intensity of defects. These results have significant implications in the software business, enabling developers to promptly identify possible vulnerabilities and take preventive measures before product release.

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A Survey of Refactoring Techniques to Maximize Code Coverage Metric

A Survey of Refactoring Techniques to Maximize Code Coverage...

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Advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Johannes Simatupang Haryono Soeparno Ford Lumban Gaol Yulyani Arifin Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Refasctoring is a technique used in software development to improve the quality of code without changing its functionality. One metric that is often used to measure code quality is Code Coverage. This study aims to examine refactoring techniques that can maximize Code Coverage Metric. Through the study, identification, evaluation, and summary of empirical evidence from various literature sources are carried out. The results of this study provide guidance on effective refactoring techniques to improve Code Coverage as well as other positive impacts for software development. There are ten refactoring techniques that can be used to improve Code Coverage Metrics in software testing.

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Transformer and CNN Comparison for Time Series Classification Model

Transformer and CNN Comparison for Time Series Classificatio...

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Advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Ilvico Sonata Yaya Heryadi Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

In real life, many activities are performed sequentially. These activities must be carried out sequentially, such as the assembly process in the manufacturing production process. This series of activities cannot be reduced or added so that the main goal of the series of activities is achieved. Apart from that, there are also time series events that occur naturally, such as rainy and hot conditions in a certain area. The classification process of time series activities is very important to see the possibility of anomalies occurring. The significant development of machine learning models in recent years has made the process of classifying time series data increasingly researched. Several previous studies stated that deep learning models were more accurate in classifying time series data. In this paper, we will compare Convolutional Neural Network (CNN) and Transformer deep learning models in classifying time series data. Experimental results using the same public datasets for CNN and Transformer model show that the CNN model is more accurate than the Transformer model. The results of measuring accuracy using confusion matrix show that CNN has an accuracy of 92% and Transformer has an accuracy of 80%.

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COCOMO II Analysis of Developing Multi-Account Partner Software for Crypto Exchange

COCOMO II Analysis of Developing Multi-Account Partner Softw...

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Advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Oscar Adam Darmawan Ford Lumban Gaol Haryono Soeparno Yulyani Arifin Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

The technique of calculating the work required for software development is known as software development estimation. The objective cost estimating technique for organizing and carrying out Multi Account Partner (MAP) software projects, COCOMO II, was employed in this study. The MAP software is a software architecture designed to support brokerage cryptocurrency exchanges using the order book and liquidity of established crypto exchanges. This research uses data sets from MAP project development at Indonesia Crypto Exchange Platform. It aims to create a software cost estimation model for MAP software using COCOMO II so that the resulting estimation model can be used as input or reference for estimates of subsequent MAP software development. The result estimated that MAP software finished in about four to five months, with a price range for software development of $7,441 to $8,780. Further research is needed with datasets from other crypto exchanges tested to increase cost estimation accuracy using COCOMO II.

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Measurement of Web-Based Merchant Application Portal (MAP) Using Function Point Analysis and Constructive Cost Model II 4

Measurement of Web-Based Merchant Application Portal (MAP) U...

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4th Asia Pacific Conference on Research in Industrial and Systems Engineering: Building Business Resilience to Face the Challenge in Pandemic Era, APCORISE 2021

作者： Santoso, Irvan Warnars, Harco Leslie Hendric Spits Gaol, Ford Lumban Abdurachman, Edi Soewito, Benfano Cyber Security Program Computer Science Department School of Computer Science Computer Science Program Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Program Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781450390385

The development of technology and the internet is one of the critical factors that must be considered by companies especially those engaged in e-commerce. The web-based application is one of the tools used by e-commerce in making it easier for users to conduct transactions and data processing. The application that developed must be calculated carefully so the effectiveness and efficiency can be appropriately maintained. In this research, the calculation of the size of the software, effort, time, staff, and the total cost needed to work on an application was calculated using the Function Point Analysis (FPA) and Constructive Cost Model II (CoCoMo II) methods. The application that has been analyzed and estimated is Merchant Application Portal (MAP) which is an application designed by one of the companies in Indonesia. The estimation results obtained, scilicet the size of software amounted to 10,02972 KLOC, effort amounted to 48.521 Person Month, time development of 13 months, staff needed as many as four staff, and the estimated cost amounted to IDR 13,828,852.79 or $ 971.54. In addition, this calculation can be used by other companies to find out the resources needed in making a software to be more effective and efficient. © 2021 ACM.

关键词： Portals

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The Implementation of Octave-S in the Risk Management Measurement of Information System in the Department of Communication, Informatics, and Statistics 4th

The Implementation of Octave-S in the Risk Management Measur...

引用

4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Sukardi, Oggsa Gaol, Ford Lumban Matsuo, Tokuro Master of Information System Management Binus Graduate Programs Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Binus Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789811974014

An information system is an important part in an organization to support business processes and to achieve its vision and mission. The information system nowadays has been one of the assets that ought to be protected by an organization. The Department of Communication, Informatics, and Statistics has the information system supporting its various operational activities. In order to protect the information system, OCTAVE-S method as the management method is used. It is expected that the OCTAVE-S method can manage the risks to the information system as the asset by specifying the possible risks and developing countermeasures and mitigation plans for each of the risks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Risk management

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Interpretable Machine Learning for Multi-Class Crop Yield Prediction 3

Interpretable Machine Learning for Multi-Class Crop Yield Pr...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Nurcahyo, Aldian Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Compuer Science Binus Graduate Program Jakarta Indonesia Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia Doctor of Computer Science Bina Nusantara University Binus Graduate Program Compuer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350394559

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices. © 2023 IEEE.

关键词： Forecasting

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

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

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International Conference on Telecommunications (ICT)

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

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.

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