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

作者： Isnan, Mahmud Hidayat, Alam Ahmad Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Classification of Indonesian crops is a critical task in developing farming and getting more understanding of agriculture. However, there is no clear task in classifying types of crops in Indonesia. Transfer learning has been used successfully in a variety of image classification applications. Thus, in this paper, we collected images of Indonesian crops from the internet randomly and proposed a classification by using transfer learning of deep learning with four pre-trained models: EffficientNetB0, ResNet18, VGG19, and AlexNet. In the experiment, augmentation techniques such as random horizontal flip, random vertical flip, and random affine were utilized to prevent the network from overfitting. The result found that EfficientNet-B0 outperformed other models with an accuracy of 82.55. Then, the model struggled to distinguish between crops in the same family. According to the results, although transfer learning can work well to classify images of Indonesian agricultural crops, some improvements are still required to address existing issues. © 2023 The Authors. Published by Elsevier B.V.

关键词： Agriculture Classification Crop Deep Learning Transfer learning

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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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Count-Data Mixed Models of Topical Tweets: A Case of Indonesia Flood Events 8

Count-Data Mixed Models of Topical Tweets: A Case of Indones...

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

作者： Hidayat, Alam Ahmad Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia 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

Examining topic-level variability in modeling Twitter data can potentially yield more comprehensive insights into public perception during critical periods, thereby enhancing natural disaster mitigation and surveillance efforts. In this study, we utilized generalized linear mixed models (GLMMs) to illustrate the variability in tweet counts related to specific topics in Indonesia during the flood events that occurred in February 2021. The glmmTMB library in R was employed for this purpose. The data were assumed to follow two distinct exponential distributions: Poisson and Negative Binomial. To incorporate random effects, random intercepts and random slopes were introduced, allowing them to vary randomly across topics in the initial two models. Additionally, the final model addressed issues related to dispersion and zero-inflation. By evaluating the Akaike Information Criteria scores, we determined that a model based on the Negative Binomial distribution with random zero-inflation intercepts best fit the data. The chosen model formulation and the estimated parameters have the potential to forecast topic-specific trends in Indonesian flood-related Twitter data. © 2023 The Authors. Published by Elsevier B.V.

关键词： count data disaster surveillance floods mixed models Twitter

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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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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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Leveraging Machine Learning to Enhance Software Quality Metrics for BMKG’s Website and Application Services

Leveraging Machine Learning to Enhance Software Quality Metr...

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computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

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

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

This research evaluates the website and application of BMKG using software quality metrics techniques that the perfomance have been upgraded by leveraging machine learning. Three process evaluation are implemented for the BMKG website and application: Crash error reports and time delay response from the website, weather forecast accuracy, and user reviews on application. Crash error and time delay response are monitored using an automatic website monitoring tool: UptimeRobot. The quality of the accuracy of weather data forecasts are evaluated by comparing BMKG data with two global platforms, MetNorway and OpenMeteo, using machine learning techniques: MAE, MSE, and R-squared. User feedbacks are retrieved from the last 1000 reviews from the Google Play Store, then analyzed using machine learning techniques: TDF-IF and F1-value. The results of this research indicates several thing that need to be improved from the BMKG website and application: The speed of website response, the accuracy of weather forecast data, and a user interface that is that is easier to use. By implementing these improvements, the positive feedback of users will increase significantly.

关键词： Measurement Accuracy Reviews Delay effects Weather forecasting Machine learning Software quality User interfaces computer crashes Monitoring

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Telematics Work Field Review Text Classification Using the Naïve Bayes Method

Telematics Work Field Review Text Classification Using the N...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Maesya, Aries Heryadi, Yaya Arifin, Yulyani Lukas Suparta, Wayan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Indonesia

ISBN: (纸本)9781665468763

Indonesia will benefit from a demographic boom in 2030 with a higher labor supply than in earlier decades. Then in industrial revolution 4.0 robotics and artificial intelligence will take the place of low-skilled or menial employment that don't require specialized expertise (AI). To aim research is Telematics Work Field Review Text Classification Using the Naïve Bayes Method. The method using Multinomial Naïve Bayes model which is trained to learn from patterns in training data set without being programmed explicitly. Then, based on the Term Frequency - Inverse Document Frequency, consider the weighting of the word used (TF-IDF). The text classification stage is then carried out using the multinominal nave bayes classification method with evaluation using the confusion matrix, following the acquisition of the TF-IDF value. In the study it took data with web crawling techniques on social media sites twitter. The data collected was 936 data consisting of 7,8% negative sentiments, 26,4% positive sentiments, and 65,8% neutrals. The results of accuracy testing using the Confusion Matrix. And from the results of such tests resulted in an accuracy of 66%, precision 73%, and recall 85%. © 2022 IEEE.

关键词： Telematics

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Disaster recovery plan implementation evaluation model at the corporation 7

Disaster recovery plan implementation evaluation model at th...

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7th Information Systems International Conference, ISICO 2023

作者： Sardjono, Wahyu Perdana, Widhilaga Gia Putra, Gustian Rama Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia Post Graduate Program of Management Science State University of Jakarta Jl. R.Mangun Muka Raya No.11 Jakarta Timur Jakarta13220 Indonesia Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia

The application of information technology in addition to making internal business processes more effective and efficient, is also to improve customer service. This role is important for companies to maintain the sustainability of information systems as a strategic tool for competitive advantage so system availability is absolutely essential. Research related to the Disaster Recovery Plan (DRP) needs to be carried out to see the readiness of a corporation to build a backup system in different places and locations. By using the method of factor analysis and regression analysis to form an evaluation model, it will obtain an overview of what factors must be considered and what the current conditions are. The results of the study show that there are Core Technical DRP, Organizational Preparation, and Costing factors which are the main variables in supporting DRP readiness. The conclusion from this study, through these three new factors a mathematical model can be built as a reference for improving DRP implementation in corporations. © 2023 The Authors. Published by Elsevier B.V.

关键词： Factor analysis

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Software Testing Model by Measuring the Level of Accuracy Fault Output Using Neural Network Algorithm 7

Software Testing Model by Measuring the Level of Accuracy Fa...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Zulkifli Gaol, Ford Lumban Trisetyarso, Agung Budiharto, Widodo Binus University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Binus University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

The testing stage is essential in software development because it determines the quality level, which is indicated by minimal errors. Meanwhile, the error that is discovered by the tester is called a fault. Therefore, this research aims to create a Model-Based Testing (MBT) using the equivalence partitioning technique in the black-box method. The model is expected to log software errors such as Access, performance, initialization, a data structure or external database, incorrect or missing functions, to identify, categorize, and fix them methodically and appropriately. Based on the discoveries, the errors form a dataset that measures the accuracy of the fault output using a neural network algorithm. The result showed the formation of MBT can be developed using the equivalence partitioning technique by measuring the fault output accuracy level using a neural network algorithm, where the average accuracy rate is 80%. © 2022 IEEE.

关键词： Errors

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