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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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Scheduling Production in the Flexible Packaging Industry Using Mathematical Models and Genetic Algorithms

Scheduling Production in the Flexible Packaging Industry Usi...

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2023 International Conference on Informatics Engineering, science and Technology, INCITEST 2023

作者： Tarigan, Masmur Gaol, Ford Lumban Mauritsius, Tuga Budiharto, Widodo Esa Unggul University Bina Nusantara University Computer Science Department / Computer Science Graduate Program Jakarta Indonesia Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia Management Bina Nusantara University Department of Information System Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350317862

Minimizing time wastage is a crucial objective for all production operations. Reliable manufacturing scheduling The objective is to mitigate time-based production waste caused by excessive processing, waiting, and transportation. Industry 4.0 promotes technological innovation across all industrial sectors to increase efficiency and effectiveness while preserving high competitiveness. The implementation of artificial intelligence in Industry 4.0 is expected to result in a reduction in time wastage. Non-dominance sequencing genetic algorithms (NSGA), genetic algorithms (GA), and evolutionary algorithms (EA) are the three most common scheduling approaches, according to literature-indexed research summaries from the past five years. The examination of production scheduling in the flexible packaging business, which involves the utilization of different machines and processes and is produced on demand, had not been conducted before in this study. A mathematical scheduling model will be developed in this research to ascertain the shortest production time span in the flexible packaging industry. As a form of technological innovation, this mathematical model will be utilized in experiments employing a genetic algorithm approach. For the genetic algorithm to generate a minimum makespan in the flexible packaging industry's production scheduling process. © 2023 IEEE.

关键词： Genetic algorithms

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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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Performance Analysis of Distributed Database System in Cloud Computing Environment 9

Performance Analysis of Distributed Database System in Cloud...

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

作者： Prasetio, Nicholas Ferdinand, Daniel Wijaya, Brandon Christopher Anggreainy, Maria Susan Kumiawan, Afdhal Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program School of Computer Science Jakarta11530 Indonesia

ISBN: (纸本)9798350370126

This research discusses the performance evaluation of distributed database systems in a cloud computing environment Cloud computing environments allow data and applications to be stored and deployed on infrastructure located in different parts of the world. However, the use of distributed database systems in cloud computing environments can cause performance issues, such as complex data access and factors such as network latency, security, and scalability that affect system performance. Therefore, performance evaluation of distributed database systems is necessary to ensure effective data management across the infrastructure. The purpose of this research is to measure and understand the performance of distributed database systems in a cloud computing environment. This is important because proper performance evaluation is needed to ensure distributed database systems can operate effectively and efficiently in such environments. This research will analyze the features of distributed database systems, factors that affect performance, how to measure and compare system performance, and how to improve system performance. Analyzing the performance of distributed database systems in a cloud computing environment can help users choose the most appropriate and efficient cloud computing platform for their business needs and improve operational efficiency. © 2023 IEEE.

关键词： Distributed database systems

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Deep Learning Approach with EfficientNet-CBAM for Apple Disease Detection

Deep Learning Approach with EfficientNet-CBAM for Apple Dise...

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

作者： Aldiki Febriantono Haryono Soeparno Ford Lumban Gaol Yulyani Arifin Computer Science School of Computer Science Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Disease attacks have the potential to decrease apple productivity, particularly in Poncokusumo Village, Malang Regency. The manual process of disease identification requires a significant amount of time and specialized knowledge, which results in inefficiency and frequently blocks early treatment. The objective of this research is to create a precise deep learning model for the detection of diseases in apples in order to ensure that it functions effectively in situations that are real. To accomplish this objective, directly collected disease image data on fruits from apple fields in Poncokusumo Village. The EfficientNet model implements the CBAM attention mechanism. The EfficientNet B0 version was employed as the basis for the model for this work, with CBAM being implemented to improve feature extraction and identify infection spots within the image. Based on the research findings, the EfficientNet-CBAM model has an F1 score of 92% and an accuracy of 93.67%. The attention mechanism of the model is effective in identifying infected areas, enabling both quicker and more precise detection. This method has the potential to enhance the precision and efficacy of disease identification, as well as offer producers advantages by applying preventive measures earlier.

关键词： Deep learning Productivity Measurement Technological innovation Accuracy Attention mechanisms Pesticides Manuals Mobile applications Diseases

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Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model 8

Sentiment Analysis for TikTok Review Using VADER Sentiment a...

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

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael 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 Program Bina Nusantara University Jakarta11480 Indonesia

TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app;there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80. © 2023 The Authors. Published by Elsevier B.V.

关键词： Google Play Store Sentiment Analysis SVM TikTok VADER Sentiment

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Comparison of YOLOv8 and EfficientDet4 Algorithms in Detecting the Ripeness of Oil Palm Fruit Bunch 10

Comparison of YOLOv8 and EfficientDet4 Algorithms in Detecti...

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

作者： Priyadi, Muhammad Reza Azhar Suharjito Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program-Master of Industrial Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

Despite Indonesia's leading position in palm oil processing, the inaccurate assessment of oil palm fruit maturity poses challenges in determining the optimal harvest timing and maintaining product quality. With advancements in information technology, including image processing and object detection techniques like YOLO and EfficientDet, there is potential for automated and precise identification of palm fruit maturity. To explore this, the authors conducted research to identify the most suitable algorithm for detecting the ripeness of palm oil fruit. The study utilized a dataset consisting of 8299 images with six maturity levels, employing augmentation techniques to increase image variability. Results show that YOLO V8s outperformed EfficientDet Lite 4 in terms of object detection precision, achieving higher mean average precision (mAP) scores. YOLO V8s showed a 4% improvement in mAP@50 and a 16% improvement in mAP@50-95 compared to EfficientDet Lite 4. YOLO V8s achieved a mAP of 0.978 for mAP@50 and 0.765 for mAP@50-95, surpassing EfficientDet Lite 4. However, EfficientDet Lite 4 had an advantage in terms of class accuracy and bounding box placement, as indicated by smaller box loss and cIs- loss values. YOLO V8s also demonstrated better frames per second (FPS) performance, ranging from 10 to 15 FPS, compared to EfficientDet Lite 4. © 2023 IEEE.

关键词： Object detection

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Utilizing Sentiment Insights for Software Evalution with Aspect-Based Sentiment Analysis in App Reviews Using IndoBERT-Lite

Utilizing Sentiment Insights for Software Evalution with Asp...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Nikita Ananda Putri Masaling Haryono Soeparno Yulyani Arifin Ford Lumban Gaol Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

With mobile applications increasingly used daily, understanding user feedback is vital for software improvement. Manual analysis of user reviews on platforms is time-consuming and lacks scalability. This study utilizes aspect-based sentiment analysis with the IndoBERT-Lite model to automate the categorization and sentiment detection of feedback. Despite advancements, precise aspect classification and sentiment detection in Indonesian language reviews remain challenging due to limited resources. Using 5,000 Tokopedia reviews, labeled across five aspects, namely functionality, service, performance, usability, and design, the model achieved optimal results with batch size 32, showing 0.85 accuracy and 0.866 F1 score for aspect classification, and 0.998 across all metrics for sentiment classification.

关键词： Measurement Seminars Sentiment analysis Analytical models Accuracy Reviews Scalability Software Mobile applications Usability

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Performance Variations in Digital Talent: Gender-Based Insights from STEM and AI Education Backgrounds 4

Performance Variations in Digital Talent: Gender-Based Insig...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Halim, Erwin Sari, Azani Cempaka Wairooy, Irma Kartika Ario, Muhamad Keenan Achmad, Said Fitrianah, Devi School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

In the digital era, digital talent development has become increasingly vital for organizations and economies to thrive. While much focus has been placed on technical skills, this report emphasizes the often-overlooked role of soft skills in developing digital talent. Through an extensive literature review and data analysis, the study identifies that soft skills are crucial in enhancing digital talent performance. This research explores the factors that influence Digital Talent Performance in Indonesia, specifically comparing the performance levels of male and female digital talents. Data were collected from college students in the Jabodetabek area (cities surrounding Jakarta, Indonesia) using a purposive sampling method, with an online questionnaire distributed to 378 respondents. The study utilized Structural Equation Modeling (SEM) to analyze the collected data using SmartPLS 4.1. software. Key variables examined include Communication Skills, Teamwork, Emotional Intelligence, Academic Achievement, Problem-Solving, Digital Readiness, Digital Skills, Digital Technology, and Digital Talent Performance. Out of the nine hypotheses tested, one was not statistically significant. The results suggest that a combination of both technical and soft skills significantly affects digital talent performance, with important gender-based differences in performance levels emerging from the analysis. This research contributes to understanding Indonesia's complex dynamics of digital talent development. © 2024 IEEE.

关键词： digital skill digital talent soft skills STEM AI talent performance

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Suitability of Latest Version of YOLOv11 in Drone Development Studies

Suitability of Latest Version of YOLOv11 in Drone Developmen...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Mohammad Faisal Riftiarrasyid Ford Lumban Gaol Haryono Soeparno Yulyani Arifin Computer Science Department Binus Graduate Program – Computer Science Bina Nunsantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Although drones have become increasingly advanced and sophisticated, they still face challenges such as limited computing power and high-power consumption. Optimizing their features is a key strategy to address these limitations. One critical feature that can be further improved is the algorithm for specialized tasks like obstacle detection. Recent studies have primarily relied on earlier versions of YOLO for this purpose. However, with the release of the latest YOLO versions, YOLOv10 and YOLOv11, there is an opportunity to assess their suitability for drone applications. In this study, we trained YOLOv10-nano and YOLOv11-nano using a drone-specific dataset and compared their performance with similar studies. The results indicate that YOLOv11-nano outperforms YOLOv10-nano in bounding-box prediction, achieving a higher mAP of 0.957 with a 0.22 difference, as well as faster inference times with a 74.1 ms improvement, resulting in a frame rate gain of 0.0421 FPS. However, when compared to earlier studies, YOLOv11-nano demonstrates superior precision but lags in real-time processing speed. These findings highlight the trade-off between accuracy and inference time, highlighting the suitability of YOLOv11-nano for applications in drone development where accuracy takes precedence over processing speed.

关键词： YOLO Training Seminars Accuracy Cameras Prediction algorithms Real-time systems Intelligent systems Information technology Drones

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