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检索条件"机构=Master Program in Computer Science"

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Implementation of Database Massively Parallel Processing System to Build Scalability on Process Data Warehouse 3

Implementation of Database Massively Parallel Processing Sys...

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3rd International Conference on computer science and Computational Intelligence, ICCSCI 2018

作者： Daeng Bani, Fajar Ciputra Suharjito Diana Girsang, Abba Suganda Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The problem with telecommunications companies today is that transactional data is more extensive than existing source tables. This makes business reporting less efficient and overwhelms query processing results in data warehouses so that they do not meet business requirements. The fast and complex evolution of the digital world must be scalable to the data warehouse process, so that the authors implement it in the data warehouse using massive parallel processing (MPP) with the Greenplum database, so that business users can get reports faster and more optimally. This case study explains how the MPP system implements and measures the performance of the Greenplum database by performing complex queries in the data warehouse with parallel processing. Therefore, this case study analyzes whether the use of MPP systems can measure the scalability of throughput and the response time in the data warehouse so that system performance in the Greenplum database remains stable for daily, weekly, and monthly operations. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Data warehouses

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Evaluation of ISO 27001 implementation towards information security of cloud service customer in PT. IndoDev Niaga Internet

Evaluation of ISO 27001 implementation towards information s...

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International Conference on Computation in science and Engineering, ICCSE 2017

作者： Fajar, Ahmad Nurul Christian, Hendy Girsang, Abba Suganda Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Every organization must ensure that information assets are protected and information security system has been implemented well. PT. IndoDev Niaga Internet is a provider of business solutions applications and implementation services that include application Human Resource Information System (HRIS) and Enterprise Resource Planning (ERP). In order to ensure the security of the information, then in 2015 PT. IndoDev Niaga Internet implement ISO 27001: 2013. Through the implementation of ISO 27001: 2013, it is expected that information can be properly maintained, which in turn will affect the business continuity. Companies need to know the extent to which the process has been applied and what actions can be done to improve the performance of the application of ISO 27001: 2013. Factor analysis was conducted first to determine the factors that affect to the information security. After the factors that affect to the information security known, then observation and interview conducted to gather data about PT. IndoDev Niaga Internet ISO 27001:2013 implementation according to the factors that affect. And then recommendation and corrective action developed using gap analysis method. The most influential factor to the security of customer information PT. IndoDev Niaga Internet is a factor access control and security operations. For the audit results IS027001: 2013 from the aspect of access control, they found 11 check items that fit into the category of NC (Non-Conformance) of 33 check items in which 9 of them in the category of major and two remaining categories minor, while for operations security aspects of the 12 check items, found 5 that goes into the category of NC (Non-Conformance) and everything was included into the category of minor. © Published under licence by IOP Publishing Ltd.

关键词： Enterprise resource planning

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Hate Speech Text Classification Using Long Short-Term Memory (LSTM) 4

Hate Speech Text Classification Using Long Short-Term Memory...

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4th IEEE International Conference of computer science and Information Technology, ICOSNIKOM 2022

作者： Ariwibowo, Suminar Girsang, Abba Suganda Diana Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399073

This paper aims to build hate speech text classification model by applying a combination of LSTM and FastText. The features of hate speech & non-hate speech, target hate speech, and categories of the hate speech. Dataset of those features taken from previous research by Okky Ibrohim. FastText word embeddings is used for formation of text vectors that will be used as input of the LSTM training model. The evaluation results obtained by getting the level of accuracy using confusion matrix. The accuracy value of text classification in this study is 83.52% on the classification of hate speech, 78.44% on the classification of target labels for hate speech, 82.75% on the classification of the label for category of hate speech. © 2022 IEEE.

关键词： Long short-term memory

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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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Peak Picking Multiple Onset Detection Functions Using Recurrent Neural Networks 22

Peak Picking Multiple Onset Detection Functions Using Recurr...

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10th International Conference on computer and Communications Management, ICCCM 2022

作者： Xuan, Mobai Mizutani, Tetsuya Master's Program in Computer Science University of Tsukuba Japan Department of Information Engineering University of Tsukuba Japan

ISBN: (纸本)9781450396349

Onset detection is a fundamental task in music information retrieval (MIR). Classical approaches using onset detection functions (ODFs) and hand-crafted peak-picking methods are outperformed by those utilizing neural networks, which are considered state-of-the-art. However, these methods bring much more computational costs and thus can be unfavored when implementing onset detections on low-performance devices. Previous work utilizing both an ODF and a recurrent neural network (RNN) provides another option as a trade-off between accuracy and processing speed. However, it only used one single ODF for the input of the network. In the present study, we propose a novel method utilizing multiple ODFs and an RNN fusing these ODFs, together with several techniques in training the network and peak-picking. Our proposed method outperformed previous work by approximately 1% at the cost of a halved processing speed, serving as another option for trading-off between accuracy and speed. One of our future works is looking for other ODFs that can be both fast-calculating and beneficial to accuracy. © 2022 ACM.

关键词： Recurrent neural networks

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The data warehouse for down payment administration in the Constitutional Court of Republic of Indonesia 2

The data warehouse for down payment administration in the Co...

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2nd Nommensen International Conference on Technology and Engineering, NICTE 2018

作者： Girsang, Abba Suganda Isa, Sani Muhamad Adytiansya, Nanda Utomo, Oei Kurniawan Simarmata, Janner Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Universitas Negeri Medan Medan Indonesia

The Constitutional Court of The Republic of Indonesia is an institution which has authority to judge and examine constitutions. In this institution, each component has responsibility to manage their own finance and budgeting. Based on this case, they need a solution to summarize the amount and absorption of the budget in every component. This paper proposes a solution to design a data warehouse model to process and analyse the down payment administration data. The nine steps design methodology for developing data warehouse is implemented in this work. This paper aims to bring some benefit for The Constitutional Court, especially in form of information about down payment absorption for every component each year. The result is presented in form of pdf report and informative dashboard using data warehouse tools. © Published under licence by IOP Publishing Ltd.

关键词： Data warehouses

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Design of Smart Home Security System using Object Recognition and PIR Sensor 3

Design of Smart Home Security System using Object Recognitio...

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3rd International Conference on computer science and Computational Intelligence, ICCSCI 2018

作者： Surantha, Nico Wicaksono, Wingky R. Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University JI kebon Jeruk Raya No 27 Jakarta11480 Indonesia

This research aims to design and implement a home security system with the capability for human detection. The traditional home security system, i.e. closed circuit television (CCTV) can only capture and record the video without able to give warning feedback if there is any suspicious object. Therefore, an additional object detection and warning method is required if there is an intruder. The proposed design is implemented using Raspberry Pi 3 and Arduino, which are connected by USB cable. The PIR sensor is installed on the Arduino and the webcam is mounted on Raspberry Pi 3. The Raspberry Pi 3 is used to process inputs from received sensors and process images for human detection. The PIR sensor detects the movement around the sensor to activate the webcam to capture a picture. Then, the object recognition is performed using histogram of gradient (HoG) and support vector machine (SVM) to detect the suspicious object. If the suspicious object is detected, then alarm is activated to warn the house owner about the existence of intruder. The evaluation results show that it takes in average 2 seconds for proposed system to detect the intruder. It also shows that the system can successfully detects the intruder with accuracy of 89%. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Object recognition

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Data mining for the global multiplex weekly average income analysis

Data mining for the global multiplex weekly average income a...

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2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： Dominic, Nicholas Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

The box office (BO) income had significantly declined up to 80% in 2020, as the COVID-19 pandemic emerged. To minimize further financial risks, multiplex (multiple cinema complexes) owners need to analyze their potential income for each movie, each week. Therefore, we developed a proper data mining strategy that allows multiplex owners to analyze and discover insights on how successfully produced movies could be. The methodology comprises (1) data loading and exploration, (2) data cleaning, (3) data selection, integration, and transformation using Pentaho, (4) data mining in which the results were stored in the MySQL database, and (5) pattern evaluation and presentation using Qlik Sense as the Business Intelligence (BI) dashboard. Based on our data mining methodology, we revealed that drama, comedy, action, and thriller are favorite genres. We also found that DreamWorks Animation and Pixar Animation Studios are both the most popular production houses, even Apatow Productions and Escape Artists still have the biggest revenue on average. © 2022 The Authors. Published by Elsevier B.V.

关键词： Animation

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Time-series Analysis of Correlation between Climatic Parameters and Dengue Fever in Indonesia 4

Time-series Analysis of Correlation between Climatic Paramet...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Nirwantono, Rudi Trinugroho, Joko Pebrianto Sudigyo, Digdo Hidayat, Alam Ahmad Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665473279

Dengue fever is mainly caused by female mosquitoes from the genus Aedes. It predominantly occurs in equatorial to sub-tropical regions. The weather factors play a significant role in the mosquito life cycle, so a study on the impact of climate parameters on dengue incidence is required. This study aimed to understand the statistical association between the annual fluctuation of dengue cases with climate factors in Indonesia. Climatic data, annual rainfall, and annual mean ambient temperature of Indonesia were obtained from *** from 2010 to 2020, while the data on the annual incidence of dengue fever in Indonesia were collected from the Indonesian Health Profile by the Ministry of Health of Indonesia from 2010 to 2020. The association between climatic factors with dengue cases was calculated with Spearman statistical test after the data was indicated as not normally distributed. In addition, we performed Poisson regression analysis to examine how the changes in climatic parameters affect the dengue cases. The rain precipitation was insignificantly correlated with the dengue incidence in Indonesia during the study time. In contrast, the ambient temperature statistically showed a strong association with the dengue cases. The association between climatic factors and the number of dengue cases suggests that the model would likely be prospectively applied to the dengue early warning system as a nationwide intervention to reduce the epidemic impact in Indonesia. © 2022 IEEE.

关键词： Data mining

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Systematic Literature Review on Machine Learning Predictive Models for Indoor Climate in Smart Solar Dryer Dome 4

Systematic Literature Review on Machine Learning Predictive ...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Setiawan, Karli Eka Elwirehardja, Gregorius Natanael Pardamean, Bens Bina Nusantara University BINUS Graduate Program Master of Computer Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

Precise predictions of indoor climate conditions are required in the implementation of Smart Solar Dryer Dome (SDD). Trend development of prediction models is discussed in this review from 15 selected research papers (2018-2022) on indoor climate prediction which was obtained from research paper databases The output shows that the most used model for predicting indoor climate is Artificial Neural Network (ANN), especially Recurrent Neural Network (RNN) such as LSTM and GRU. However, there are some potential methods such as Transformer, Combined Support Vector Machines (SVM)-Deep Learning, and sequence-to-sequence which could outperform other commonly used models. Based on findings various opportunities exist to improve the precision of indoor climate prediction, which can bring power consumption efficiency and others benefit to Smart SDD users. Such studies may further be explored to produce more accurate machine learning models. © 2022 IEEE.

关键词： Domes

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