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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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The Impact of Shift-Left Testing to Software Quality in Agile Methodology: A Case Study

The Impact of Shift-Left Testing to Software Quality in Agil...

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International Conference on Information Management and Technology (ICIMTech)

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

The point of Agile Methodology is continuous improvement, delivering a small feature quickly without sacrificing the feature quality; every sprint must be better than the previous sprint, and better can be fewer bugs, faster development, and testing. We will present how we reduce production bugs by customizing our sprint iteration. As we know, bugs are unavoidable, there is no software engineer that can make software without a bug; however, we can reduce bugs in production if we can find bugs in lower environments as early as possible. The case study in this paper was taken from one of technology company in Indonesia, the activity was done by the Quality Engineer (QA) Team. We will show that shift-left testing can help us reduce bugs in production. Testing is part of agile methodology, and the main idea of shift-left testing is to move testing early and could be done by any team member, not only QA. We include shift-left testing in our agile methodology for one year in 2022 and compare the result in the previous year.

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An IoT-Driven Fuzzy Decision Model for Determining Room Comfort

An IoT-Driven Fuzzy Decision Model for Determining Room Comf...

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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Ditdit Nugeraha Utama John Reigton Hartono Johan Muliadi Kerta Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Bandung Indonesia

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

The use of the Internet of Things (IoT) has been widely adopted for a lot of purposes. Business process automation is one type of implementation that has been carried out. In addition, the data successfully captured by the IoT ecosystem can be utilized for various computer modeling needs, including the decision model construction in assessing room quality. The decision model itself is a computer model supporting the decision maker in decision making process. This research, via four simple research stages, aims to develop an IoT-based decision model to evaluate room quality based on two parameters: temperature and humidity. The design of the IoT environment and the implementation of temperature and humidity data readings in a room are used as input data for a decision model to assess the comfort level of the room. In this study, the primary method used to design the model is object-oriented, while fuzzy logic is employed to build the model. The developed decision model can simulate data captured from the designed IoT ecosystem, successfully assessing the comfort level of a room. From the simulation results using 18 data points (from 9 days of data capturing) captured by temperature and humidity sensors, the average comfort level of the room was found to be 19.7873.

关键词： Temperature sensors Fuzzy logic Temperature distribution Object oriented modeling Computational modeling Simulation Ecosystems Humidity Data models Internet of Things

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Mapping the Landscape of Social Learning: Integrating Key Success Factors, Challenges, and Digital Platforms 9

Mapping the Landscape of Social Learning: Integrating Key Su...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Marcel Meyliana Warnars, Harco Leslie Hendric Spits Mursitama, Tirta Nugraha BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Business School Doctor of Research in Management Bina Nusantara University Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This research develops a conceptual model to understand the influence of digitalization on social learning, especially in the context of community empowerment. This model maps the relationship between factors that influence success, challenges faced, and variations in the use of digital platforms in social learning. By using a literature study approach, the research results show that effective communication, collaboration and empowerment are the main keys to the success of social learning in society. Key challenges identified include limited access to information, resource constraints, and digital literacy barriers, all of which require specific strategies to overcome. By examining the relationship between success factors, challenges, and use of digital platforms, this research provides new insights that support the development of more effective strategies for using digital technology to support social learning that leads to community empowerment. This research has great potential to increase people's access to learning opportunities and information, allowing them to be more active in the social learning process. © 2024 IEEE.

关键词： Federated learning

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Adopting Augmented Reality (AR) Technology in Retail Sector: Systematic Review of Prisma Methods 7

Adopting Augmented Reality (AR) Technology in Retail Sector:...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Maryani Prabowo, Harjanto Gaol, Ford Lumban Hidayanto, Ahmad Nizar School of Information Systems BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Information Systems Department Computer Science Department Jakarta11480 Indonesia BINUS Business School Doctor of Research in Management Bina Nusantara University Management Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Faculty of Computer Science Universitas Indonesia Depok16424 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

One of the most popular technologies nowadays is augmented reality (AR). popular technologies in various industries. Many industries have adopted this AR technology, one of which is with the aim of marketing the products offered to their customers. With more and more adopting this technology, retailers are also using it. The goal of this research is to determine out what important variables are often encountered in the application of AR technology. A qualitative technique is used in this study, as well as a Systematic Literature Review Prism approach. The result are 56 papers, 35 candidate papers and 26 papers were selected. We examine where and how in retail, augmented reality (AR) is used, the aspects of Augmented reality technology that are frequently examined, and the psychological and behavioral effects that AR can have. © 2022 IEEE.

关键词： Augmented reality

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Leveraging BERT with Extractive Summarization for Depression Detection on Social Media 23

Leveraging BERT with Extractive Summarization for Depression...

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23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022

作者： William, David Achmad, Said Suhartono, Derwin Gema, Aryo Pradipta Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Epigene Labs United States

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

In our current time, the well-being of a person is not only determined by the physical health, but also by their mental health. A lot of focus and effort have been spent into raising the awareness of this issue. One such effort comes from the field of computer science by utilizing data from social media to provide additional information in detecting these mental disorders. In this research, authors proposed the Bidirectional Encoder Representations from Transformers (BERT) with extractive summarization to preprocess data obtained from popular social media platform such as Reddit and Twitter. The proposed method evaluated by compare it to several other methods in text classification to Figure out which method is best classifying whether someone is depressed or not. This research also contributes to evaluate whether applying extractive summarization to a document prior to feeding the data into BERT will contribute to an increase in performance when compared to the current method of data preprocessing, by measuring the accuracy, precision, recall and F1-score of each model. The evaluation result found that our proposed method by applying extractive summarization to reduce the sequence length achieved a better result compared to BERT base model. © 2022 IEEE.

关键词： Text processing

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Drug Demand Prediction Based on Epidemiology Factors Using Random Forest

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

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

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

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.

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Evaluating the Effectiveness of Transformer Models for Molecular Property Prediction: A Review

Evaluating the Effectiveness of Transformer Models for Molec...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Alyssa Imani Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Transformer models, originally successful in natural language processing, are now being applied to chemical and biological studies, excelling in areas such as molecular property prediction, material science, and drug discovery. BERT, a Transformer-based model, has become foundational in cheminformatics, particularly for QSAR (Quantitative Structure-Activity Relationship) modeling and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluations in drug discovery. However, achieving higher accuracy often requires designing more complex models, which can compromise their interpretability. This posing a challenge for researchers who need to understand the reasoning behind the predictions. The trade-off between accuracy and interpretability presents a critical challenge in applying black box models to real-world problems in cheminformatics. This work compares Transformer-based models with traditional machine learning and deep learning approaches, focusing on both interpretability and performance. The goal is to highlight the strengths and limitations of each method, offering insights into their optimal use in drug discovery and material science.

关键词： Accuracy Toxicology Reviews Biological system modeling Predictive models Transformers Natural language processing Drug discovery Metabolism Information technology

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BERT and ULMFiT Ensemble for Personality Prediction from Indonesian Social Media Text

BERT and ULMFiT Ensemble for Personality Prediction from Ind...

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2022 International Symposium on Information Technology and Digital Innovation, ISITDI 2022

作者： Halimawan, Noptovius Suhartono, Derwin Gema, Aryo Pradipta Yunanda, Rezki Binus Graduate Program-Master Of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School Of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Epigene Labs Paris France

ISBN: (数字)9781665461160

ISBN: (纸本)9781665461160

Predicting personality is a growing topic in the field of natural language processing. The study of personality prediction has been proven to benefit the development of recommender systems and automated personality assessments by previous studies. Additionally, the widespread usage of social media in Indonesia such as Twitter has served as a potential source of data for developing such models. Existing personality prediction models has explored the implementation of both traditional machine learning models and deep learning models, with the latter proven to perform better with more data. Despite so, there is not much development of deep learning-based personality prediction models in the domain of Indonesian text. We propose a deep learning-based ensemble model based on Universal Language Model Fine Tuning (ULMFiT) and Bidirectional Encoder Representations from Transformers (BERT), the state-of-the-art pre-trained models for text classification. The model is trained with Indonesian tweets labelled with Five Factor Model (FFM) personality traits along with the users' metadata. The model is tested with five predetermined scenarios to prove the robustness of its performance. The final evaluation of the model proves that it has successfully achieved optimal performance of 76.41 % macro-average accuracy and 75.55% macro-average F-1 score, exceeding the state-of-the-art BERT and ULMFiT © 2022 IEEE.

关键词： Classification (of information)

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A review: Data pre-processing techniques used for diabetes prediction

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Procedia computer Science 2024年 245卷 667-676页

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Cmputer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

When processing datasets in diabetes classification, common problems included a large number of missing values, outliers, and dataset imbalance. To deal with those issues, this study analyzed 18 studies on diabetes classification with machine learning algorithms over the past 5 years. This revealed the important role of data pre-processing in creating effective classification models, as it was found that by using different data pre-processing techniques, the same model can provide different performance. The study identified K-Nearest Neighbor (KNN) and support vector machine (SVM) as superior methods for filling in missing values, achieving an accuracy of 98.49% and 94.89%, respectively. These approaches outperformed traditional methods such as median or mean replacement. However, the challenge of imbalanced data sets remains in all studies reviewed. The common evaluation metrics used to evaluate the created models in previous studies included accuracy, precision, specificity, sensitivity/recall, and F1 Score. Overall, this review showed that the role of data pre-processing is no less important than algorithm selection to improve the performance of machine learning models in diabetes classification.

关键词： Diabetes Classification Data Pre-Processing Machine Learning Imbalanced Dataset

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