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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Utama, Ditdit Nugeraha Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350389654

Operating a smart city is intricate and requires comprehensive management. Implementing a decision model is one effective and efficient method to address this complexity. This study seeks to systematically review articles to see the trends in the utilization of fuzzy logic methods and their combinations, particularly in a decision model that supports smart city operations. Through four stages, the study not only presents the bibliometric-based review findings to address specific inquiries but also produces an object-oriented model for review purposes. Based on 131 articles, the research trend indicates a linear growth in studies focusing on smart cities and decision models, with a particular emphasis on incorporating fuzzy logic models. Additionally, according to 9 deep-reviewed articles, the analytic hierarchy process (AHP) emerges as a popular method often combined with the fuzzy logic method. As well, the object-oriented model for review purposes is developed with 19 interrelated objects. It is the main contribution of the study. © 2024 IEEE.

关键词： Smart city

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Object Driven Fuzzy Decision Model for Evaluating the Cryptocurrency's Performance 11

Object Driven Fuzzy Decision Model for Evaluating the Crypto...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Utama, Ditdit Nugeraha Binus Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350389654

The assessment of cryptocurrency performance has been subject to criticism due to the lack of a standardized approach, leaving the evaluation process without clear guidance. In this research, an innovative object-driven decision model based on fuzzy logic (OFDM) conception was developed to systematically analyze cryptocurrency performance. Object-oriented operated as the main method in analyzing and designing the model. In this context, the model elements deeply observed and functioned to become fundamental thinking for constructing the model. Then, fuzzy logic with curve membership function form was also used as the primary method to avoid the bias value of three key parameters;trend line (and then used to see the slope distance), price consistency, and transaction volume consistency. The constructed model is a novel decision model that was successfully constructed for assessing the determined cryptocurrency performance. Utilizing six months of real data from three major cryptocurrencies (Bitcoin, Ethereum, and Dogecoin), the model conclusively demonstrates that Bitcoin exhibits the strongest performance, scoring 36.39 performance points. © 2024 IEEE.

关键词： Fuzzy logic

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Easy Data Augmentation for Handling Imbalanced Data in Fake News Detection 1

Easy Data Augmentation for Handling Imbalanced Data in Fake ...

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1st International Conference on Technology, Engineering, and Computing Applications, ICTECA 2023

作者： Pradana, Rilo Chandra Joddy, Setiawan Girsang, Abba Suganda Binus Graduate Program-Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350360554

Detecting fake news in the digital era is challenging due to the proliferation of misinformation. One of the crucial is-sues in this domain is the inherent class imbalance, where genuine news articles significantly outnumber fake ones. This imbalance severely hampers the performance of machine and deep learning models in accurately identifying fake news. Consequently, there is a compelling need to address this problem effectively. In this study, we delve into fake news detection and tackle the critical issue of imbalanced data. We investigate the application of Easy Data Augmentation (EDA) techniques, including back-translation, random insertion, random deletion, and random swap to mitigate the adverse effects of imbalanced data. This study focuses on employing these techniques in conjunction with a deep learning framework, specifically a Bidirectional Long Short-Term Memory (BiLSTM) architecture. The results of the EDA techniques will be systematically compared to see their effectiveness and their impacts on model performance. This study reveals that various EDA techniques, when coupled with a BiLSTM architecture, yield significant improvements in fake news detection. Among the experiments, it shows that Random Insertion, with an impressive accuracy rate of 81.68%, a precision score of 89.38%, and an F1-Score of 87.77% emerges as the most promising technique. The study also highlights the exceptional potential of Back-translation stands out with an 87.16% recall performance. © 2023 IEEE.

关键词： Long short-term memory

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Code-Mixed Sentiment Analysis Using Machine Learning Approach - A Systematic Literature Review 4

Code-Mixed Sentiment Analysis Using Machine Learning Approac...

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4th International Conference on Informatics and Computational sciences, ICICoS 2020

作者： Tho, Cuk Warnars, Harco Leslie Hendric Spits Soewito, Benfano Gaol, Ford Lumban Bina Nusantara University BINUS Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728195261

Code-mixed language is ubiquitous. Having been commonly practiced among bilingual communities, code-mixed language has emerged as a common language among social media users. Despite its popularity, the analysis of a code-mixed text is challenging as the text does not typically comply with the monolingual grammar. Therefore, the popularity of social media in the past ten years has raised wide attention to develop methods for analyzing code-mixed text such as extracting popularity sentiment from the text. Machine learning-based classifier such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression have been widely used to analyze the sentiment. This paper intends to further explore machine learning classifiers, their performances, variables, and most common classifiers for the code-mixed sentiment analysis. Prisma Methodology was used in this paper, extracting 12 from 230 papers that met predefined required criteria, including publication year within the last 5 years. Our findings suggested that the most common classifiers found in the papers were Support Vector Machine, Naïve Bayes, and Logistic Regression. By using the accuracy and F1 as the performance measures, the Support Vector Machine exhibited a better performance compared to Naïve Bayes and Logistic Regression. Thus, this study supported the use of Support Vector Machine, Naïve Bayes and Logistic Regression as the main classifiers for the code-mixed sentiment analysis. © 2020 IEEE.

关键词： Sentiment analysis

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Indonesian Tweet Emotion Detection Using IndoBERT 9

Indonesian Tweet Emotion Detection Using IndoBERT

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

作者： Masaling, Nikita Ananda Putri Siswanto, Ricky Reynardo Girsang, Abba Suganda Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This study investigates emotion detection on tweets written in the Indonesian language, using the IndoBERT model that has been trained for 50 epochs. The examination conducted in this study provides a comprehensive analysis of the performance dynamics shown by the model. The assessment results provide an accuracy score of 0.74, indicating the model's proficiency in identifying sentiments within the collection of tweets. By using assessment criteria such as accuracy, recall, and the F1-score, the model's comprehensive analytical ability is validated, emphasizing its effectiveness in deciphering various emotional circumstances. The accuracy, recall, and F1-score of the model, with average values of 0.79, 0.78, and 0.78 respectively, combined demonstrate its resilience in accurately capturing subtle emotional subtleties. This study enhances our comprehension of language model applications in the field of emotional intelligence by exploring the complexities of sentiment analysis. It emphasizes the practical value of these applications in real-world scenarios. © 2024 IEEE.

关键词： Emotion Recognition

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Developing Smart Precision Farming Using Big Data and Cloud-Based Intelligent Decision Support System 10

Developing Smart Precision Farming Using Big Data and Cloud-...

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

作者： Nurcahyo, Aldian Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

This study discusses the development of smart precision farming systems using big data and cloud-based intelligent decision support systems. Big data plays an important role in collecting, storing, and analyzing large amounts of data from various sources related to agriculture, including data from weather stations, soil sensors, satellite imagery, crop yield records, pest and disease reports, and other sources. This study highlights the differences between smart farming and precision farming. This study describes key techniques and system architecture, including data collection, processing, analysis, and decision support components. Utilizing a cloud platform enables scalability and optimized performance, which lowers costs and makes it safer and easier to manage. The integration of big data and Alibaba cloud computing in smart precision farming can improve farming productivity by providing timely information and recommendations to farmers for better decision-making. Finally, the system produces smart precision farming, which provides cost-effective real-Time monitoring and predictive analytics to increase agricultural production and sustainability. © 2023 IEEE.

关键词： Decision support systems

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Serverless utilization in microservice e-learning platform 7

Serverless utilization in microservice e-learning platform

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Nday, Bryan Andreas Kusuma, Gede Putra Fredyan, Renaldy Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Since cloud computing becoming the trend, the way servers being implemented slowly moves to the cloud. Companies did not need to buy a physical server machine to deploy an app. Having a private server on cloud infrastructure indeed already reduce some cost for on-premise server maintenance. However, there is still a cost for usage when the server is inactive or having low to no traffic at all. Serverless deployment offer function as a service where application is deployed as a function and cost is billed per function call. This paper proposed a solution where there are two deployment that works in turn between infrastructure as a service and function as a service deployment. This dual deployment offered the system to use the virtual private server or deployed instance on active hours, and switch to serverless functions on inactive hours. Switching to serverless on low traffic hours will cut the usage and cost of the microservice app by the least 25%, while having performance slightly comparable to microservice app deployed to instances. © 2022 Elsevier B.V.. All rights reserved.

关键词： Infrastructure as a service (IaaS)

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Automation testing using silk test workbench for website 7

Automation testing using silk test workbench for website

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Parapat, Feliks Abedyoga Chandra Kusuma, Gede Putra Majiid, Muhammad Rizki Nur Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Automation testing is essential to carry out functional testing quickly and precisely. Software testing is beneficial for testers doing many testing processes according to the existing scenarios. So, there is an urgency to explore a more advanced solution to replace the traditional approach. Silk Test Workbench is a tool for automation testing that can accelerate the functional testing of complex applications. For this research, we proposed automation testing on website applications with various testing scenarios that have been made. The visual test recording method is used for automation testing with Silk Test Workbench. Silk Test Workbench can speed up testing time, make a good test asset testing scenario, make it easier for users to do testing, and speed up the process to get the result in the form of a screen capture of the testing process. A comparison of testing time has been performed. The automatic testing time with the scenarios is 2 hours 46 minutes. The automatic testing time will be 2 hours 36 minutes using the all-in-one scenario. This study also used questionnaires from colleagues in the Quality Assurance field to get maximum User Acceptance Test results. © 2022 Elsevier B.V.. All rights reserved.

关键词： Websites

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The Floating Fuzzy Logic based Evaluation Model for Appraising the Student Performance 6

The Floating Fuzzy Logic based Evaluation Model for Appraisi...

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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Utama, Ditdit Nugeraha Bina Nusantara University Computer Science Department BINUS Graduate Program - Master of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

Membership function (MF) in process of fuzzy logic is very meaningful. It depicts the core of model. It can be adopted from the expert judgment and also coming from the configuration of data behavior. The study is an experimental research by analyzing time-series data behavior in developing the MF by using the programming language Python. The MF academically produced called as floating MF (FMF). Such a FMF operated to realize the fuzzy logic conception in an evaluation model. The constructed model is a model in simply measuring the student's performance. By using the ten series-data of student's grade point average (GPA) and absence per annual (APS) values, the model can simulate the moving students' performance per semester. When compared to the conventional FL based model, the proposed model has a similarity average 85%. © 2022 IEEE.

关键词： evaluation model floating fuzzy logic moving performance value student performance

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Survey and Improvement Strategies for Gene Prioritization with Large Language Models

arXiv

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arXiv 2025年

作者： Neeley, Matthew B. Qi, Guantong Wang, Guanchu Tang, Ruixiang Mao, Dongxue Liu, Chaozhong Pasupuleti, Sasidhar Yuan, Bo Xia, Fan Liu, Pengfei Liu, Zhandong Hu, Xia Jan and Dan Duncan Neurological Research Institute Texas Children’s Hospital Houston United States Graduate School of Biomedical Sciences Program in Quantitative and Computational Biosciences Baylor College of Medicine Houston United States Graduate School of Biomedical Sciences Program in Genetics and Genomics Baylor College of Medicine Houston United States Department of Computer Science Rice University Houston United States Department of Computer Science Rutgers University NJ United States Department of Pediatrics Baylor College of Medicine Houston United States Department of Molecular and Human Genetics Baylor College of Medicine Houston United States Human Genome Sequencing Center Baylor College of Medicine Houston United States Baylor Genetics Houston United States

Background Rare diseases can be difficult to diagnose due to limited patient data and broad genetic diversity. Despite the advances in variant prioritization tools, many rare disease cases remain undiagnosed. While large language models (LLMs) have performed well in medical exams, their accuracy in diagnosing rare genetic diseases has not yet been evaluated. Methods To identify causal genes for genetic diseases, we benchmarked various LLMs for gene prioritization. We employed multi-agent and Human Phenotype Ontology (HPO) classification approaches to identify patient case groups based on phenotypes categorizing them into different levels of solvability. To address LLM limitations in ranking large gene sets, we used a divide-and-conquer strategy to break down the ranking task into smaller subsets. Mini-batching inputs and limiting the number of generated tokens improved efficiency. Results In its vanilla form, GPT-4 consistently outperformed the other LLMs with an accuracy around 30%. Multi-agent and HPO classification approaches aided us in distinguishing between confidently-solved and challenging cases. In addition, we observed bias towards well-studied genes and input order sensitivity which are drawbacks of LLMs in disease-causal gene prioritization. Our divide-and-conquer strategy enhanced accuracy by overcoming positional and gene frequency biases in literature. This framework, based on benchmarking insights and novel techniques, significantly optimizes the overall process when identifying disease-causal genes for genetic diseases when compared to our baseline evaluation, and thereby better enables the development of targeted diagnostic and therapeutic interventions. Conclusions Using HPO classification and our novel multi-agent techniques, and our LLM divide-and-conquer strategy (1) highlighted the importance of accounting for differences in patient case solvability and (2) yielded improved performance in identifying causal genes for rare diseases when compare

关键词： Gene Ontology

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