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

作者： Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 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

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further. © 2023 The Authors. Published by Elsevier B.V.

关键词： machine learning rice phenotypes prediction statistics

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NoSQL Injection Detection Using Deep Neural Network and Principal Component Analysis of Injection Feature Vectors

NoSQL Injection Detection Using Deep Neural Network and Prin...

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International Conference on computer, Control, Informatics and Its Applications (IC3INA)

作者： Irwin Santoso Soegiharto Abba Suganda Girsang Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program - Master of Computer Science Binus Nusantara University Jakarta Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

NoSQL database has gained popularity in Big Data and other various applications for its simplicity and flexibility. The non-relational nature of NoSQL database such as MongoDB proves to improve development lifecycles and resources efficiency. However, security challenges arise along with increasing usage of NoSQL database, and NoSQL database is no exception to injection attacks. Machine learning proved to be an efficient method, as much has been researched. However, in the future there may be an increasing complexity of features that may prove costly to the model’s performance. Therefore, this research aims to utilize principal component analysis as dimensionality reduction and deep neural network as the classification method, to improve the security of NoSQL database. The text query is converted to feature vectors then further processed to reduce the input dimension of the deep neural network using PCA. The features used are based on previous research and various sources, and some are added after analyzing the dataset.10-fold cross validation is also applied to ensure that the model does not overfit the data, attempting to reduce bias to the result. The 10-fold cross validation model accuracy result is in average 97.44% with a standard deviation of 1.7%, and the testing results are 97.5% in accuracy,95.65% in precision, 91.67% in recall, and 93.61% in F1 score. Thus, it can be concluded that the usage of PCA on injection feature vectors can reduce complexity of the model.

关键词： Technological innovation NoSQL databases Artificial neural networks Vectors Complexity theory Security Informatics Standards Principal component analysis Testing

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Evaluation of Machine Learning on Smart Home Data for Prediction of Electrical Energy Consumption

Evaluation of Machine Learning on Smart Home Data for Predic...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Widianto, Mochammad Haldi Gunawan, Alexander Agung Santoso Heryadi, Yaya Budiharto, Widodo Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Computer Science Department Bina Nusantara University Jl. K.H.Syahdan No. 9 Palmerah Kemanggisan Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

Electrical energy consumption is always increasing, and this causes the supply of electrical energy to be increased to compensate. One solution is to predict electricity energy consumption using Artificial Intelligence (AI) technology in Smart Homes. Several studies' solutions for predicting electrical energy consumption usually focused only on performance but rarely evaluated Machine Learning (ML) by correlation for feature selection and utilized interpretability model. This study uses an ML model for predicting utilization (Linear Regression, Decision Tree, Random Forest, and XGBoost). Then, Feature Selection utilizes correlation to choose the best feature. After that, the interpretability model utilizes Local Interpretable Model-agnostic Explanations (LIME). The results show that XGBoost has the best Root Mean Squared Error (RMSE) value (0.318) with a percentage of the number of train and test data (90/10). After that, by eliminating features that correlate with 0.01, XGBoost improves with an increase of (0.018) to become (0.3). Then from LIME. This work also gets positive feature from XGBoost such as: "Furnance, Well dan Living Room". © 2023 IEEE.

关键词： Forecasting

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TESTING FACILITY LOCATION WITH CONSTRAINED QUEUE TIME PROBLEM: A CASE STUDY IN FLORIDA, USA

TESTING FACILITY LOCATION WITH CONSTRAINED QUEUE TIME PROBLE...

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2024 Winter Simulation Conference, WSC 2024

作者： Monteiro, Almir Antonio Schneider, Rafael de Almeida Guimarães, Vanessa González, Pedro Henrique System Engineering and Computer Science Program Federal University of Rio de Janeiro Brazil COPPEAD Graduate School of Business Federal University of Rio de Janeiro Brazil

ISBN: (纸本)9798331534202

This study addresses the Testing Facility Location with Constrained Queue Time Problem. This optimization problem focuses on determining the best places to deploy testing sites and their available testers for infectious diseases, while constraining the maximum time in the queue with a given probability. An integer programming model is introduced and applied to the three biggest counties, in terms of population, of Florida, United States. Moreover, the Monte Carlo method is used to evaluate the model’s output, aiming to check if the queueing time constraint is being satisfied. Through the experiments, a testing facility deployment plan can be determined for each county and further validated by the simulation. The results show that the solutions returned by the model behaved successfully when submitted to the Monte Carlo method, not exceeding the time in the queue in more than the predefined probability. © 2024 IEEE.

关键词： Monte Carlo methods

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Load Balancing to Optimize MPEG-DASH Video Streaming 39

Load Balancing to Optimize MPEG-DASH Video Streaming

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39th International Conference on Information Networking, ICOIN 2025

作者： Dos Passos, Edenilson Jonatas Fiorese, Adriano Santa Catarina State University (UDESC) Graduate Program in Apllied Computing (PPGCAP) Department of Computer Science (DCC) Joinville Brazil

ISBN: (纸本)9798331506940

Recent advancements in on-demand multimedia streaming have revealed the potential of this business model, driven by the convenience of accessing content anytime and anywhere. However, delivering a seamless user experience remains challenging due to the demanding nature of audiovisual streams, despite significant resource allocation. A promising solution involves using load balancers to distribute workloads evenly among processing units. This study proposes optimizing the processing and transmission resources by means of Software-Defined Networks (SDN). By continuously monitoring performance metrics like CPU usage and throughput, the SDN network controller determines the best server for handling new connections, even allowing seamless server migration during playback. The results show up to 80% faster response times and more consistent video quality, with reduced latency and uninterrupted playback. The approach also confirms the feasibility of SDN in streaming services, providing a foundation for future network improvements. © 2025 IEEE.

关键词： Load balancing Monitoring SDN Video-streaming

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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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Examining the impact of attitude, availability, price, and quality of service on broadband Internet customer satisfaction 9

Examining the impact of attitude, availability, price, and q...

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

作者： Arin, Ikrar Adinata Murad, Dina Fitria Warnars, Harco Leslie Hendric Spits Meyliana Bina Nusantara University Information System Department School of Information Systems Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Information Systems Department BINUS Online Learning Jakarta Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Information System Department School of Information Systems Jakarta Indonesia

ISBN: (纸本)9798350370126

After the COVID-19 pandemic, broadband internet utilization is predicted to continue to grow and experience a significant increase. The pandemic has changed how people work, study, communicate, shop, and carry out many aspects of daily life. Research reported that since 2018, research on broadband internet has continued to increase until now. This quantitative study examines the impact of the four critical variables on the link between attitude, availability, pricing, and quality of service (QoS) on broadband internet customer satisfaction using the Structural Equation Model approach and the Partial Lease Square technique. Three hundred and sixty-one citizen respondents in DKI Jakarta Province were collected using the snowball sampling method facilitated by Google Forms. At the same time, the COVID-19 post-pandemic was still ongoing in Indonesia. Through rigorous statistical analysis, the results show that customer satisfaction with broadband internet services is influenced by all factors, from attitude towards broadband Internet, broadband Internet availability, broadband Internet price, and service quality. The implications of this research are significant for service providers. They provide actionable insights for developing effective strategies to improve customer satisfaction and increase broadband internet subscriptions. © 2023 IEEE.

关键词： Quality of service

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Spatial Temporal Changes in Mangrove Forests on Tarakan Island, North Kalimantan, Indonesia 3rd

Spatial Temporal Changes in Mangrove Forests on Tarakan Isla...

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3rd International Conference on Radioscience, Equatorial Atmospheric science and Environment, INCREASE 2023

作者： Giofandi, Eggy Arya Sekarjati, Dhanu Arshad, Mazlan Rahman, Henzulkifli Tjahjono, Boedi Graduate Program of Regional Planning Science Faculty of Agriculture IPB University Bogor Indonesia Geoinformatics Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Graduate Program of Coastal and Ocean Resource Management Faculty of Fisheries and Marine Science IPB University Bogor Indonesia Graduate Program of Tropical Biodiversity Conservation Faculty of Forestry and Environment IPB University Bogor Indonesia Department of Soil Science and Land Resources Faculty of Agriculture IPB University Bogor Indonesia

ISBN: (纸本)9789819707393

Mangroves play an important role in providing essential ecosystem services and resources for fisheries, offering protection against climate change and coastal erosion. Understanding the changes in mangrove forests is essential for the sustainability of the coastal ecosystem. The study aims to analyze the spatiotemporal changes of mangrove forests in Tarakan Island using Sentinel-1 and Sentinel-2 images from 2018 and 2023. The analysis shows changes in land cover, with an increase in aquaculture land and a decrease in other land types. The results of the accuracy assessment show good results, with an accuracy value of 0.82 and a Kappa value of 0.78 in the mangrove areas in 2023. These values indicate that the classification of land cover in the study area was reliable and accurate. The conversion of mangrove forests into aquaculture land poses a challenge for their conservation. Therefore, preserving and conserving mangrove forests in the study area is essential for maintaining the ecological balance and supporting the livelihoods of local communities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Aquaculture

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Control and Data Integrity are Important Factors of Data Governance Technology 10

Control and Data Integrity are Important Factors of Data Gov...

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

作者： Chandra, Yakob Utama Prabowo, Harjanto Gaol, Ford Lumban Purwandari, Betty School of Information Systems Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia BINUS Business School Bina Nusantara University Doctor of Research Management Management Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350339543

The proliferation of data has become an inevitable consequence of the digital age as individuals across all domains increasingly rely on data for various purposes. Both companies and governments have a significant need for data. However, the sheer volume and complexity of this data, along with the potential risks associated with mishandling it, necessitate the establishment of a framework known as data governance. The primary objective of this research paper is to examine the factors that contribute to the effective implementation and utilization of Data governance. This study employs qualitative methods and adopts a systematic literature review approach to address the research inquiry: 'What are the important factors that determine the utilization of data governance technology?' Through the systematic literature review, a total of 41 key factors were identified from 20 research literatures that have been found to significantly impact the effective implementation of data governance. There are thirteen primary technological factors that hold significance in the realm of data governance. These factors encompass technology, application and use, storage of data, sharing, archiving, and preservation, big data algorithmic systems, data flow, deletion, diversity of data, mechanisms, size of data, control, and data integrity. © 2023 IEEE.

关键词： Digital storage

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Investigation of player emotion and instinct on racing video game using 6-11 framework (Case study: split second (2010) single player modes)

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Procedia computer science 2024年 245卷 87-99页

作者： Galih Dea Pratama Andien Dwi Novika Elshad Ryan Ardiyanto Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta 11480 Indonesia

Racing is one of the most prominent genres in the modern video game industry, where the games within the genre enable players to use any vehicle of their choice and win through doing series of action within gameplay. Thus, it made the genre popular over the years, spawning many famous titles such as Split Second back in 2010. Like every other video game title, Split Second also invokes the player emotion and instinct during its gameplay session. To understand the emotion and instinct aspect present in players of the game, we will use 6-11 framework which focuses on the two aspects. As part of the conclusion, Split Second game involves instincts such as Competition, Collecting, and Survival, with emotions including Fear, Pride, Joy, and Excitement, which can be found from its vast single player game modes.

关键词： video games racing video games game analysis 6-11 framework

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