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Shrinking Neural Style Transfer Model with Knowledge Distillation 3

Shrinking Neural Style Transfer Model with Knowledge Distill...

引用

3rd International Conference on Artificial Intelligence and Data sciences, AiDAS 2022

作者： Hidayaturrahman Warnars, Harco Leslie Hendric Spits Soewito, Benfano Gaol, Ford Lumban Binus Graduate Program - Doctor of Computer Science Bina Nusatara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

Neural style transfer is a technique to transfer a style of an artistic image to another photorealistic image. The early development of this technique is exploiting the intermediate output of a deep learning model and utilizing it to optimize a targeted image. However, this technique needs some time to finish the process. An approach called knowledge distillation is widely used to optimize deep learning models so the speed of the model can be increased. Commonly, this technique is applied in a supervised task by providing a soft label as a target label for the student model. In this paper, a method is proposed to enable knowledge distillation happened in style transfer tasks. It is found this method is effective enough to make the style transfer process faster and give good results. In this paper also some discussion has been elaborated regarding the condition that is adequate to produce a good student model for style transfer tasks using knowledge distillation. © 2022 IEEE.

关键词： Distillation

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Recommendation System Model for Personalized Learning in Higher Education using Content-Based Filtering Method 7

Recommendation System Model for Personalized Learning in Hig...

引用

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

作者： Julianti, M. Ramaddan Heryadi, Yaya Yulianto, Budi Budiharto, Widodo BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

- An online learning system is a learning approach that typically uses a one fit for all approach in which all student abilities are considered the same so that the provision of learning materials provided is the same for all students. In contrast, a personalization learning system is a teaching and learning approach in which students are positioned as the learning centers and the students are motivated and facilitated to overcome their learning needs based on their learning speed and understanding of the learning process carried out to achieve. In the context of learning, learning materials and learning objectives are the main components that are interrelated in achieving the expected learning outcomes. However, the relationship between learning materials, especially in prerequisite courses, must have similarities so that the learning process can continue in accordance with the learning outcomes to be achieved. This paper presents an experiment results of the proposed the recommendation system modeling for personalization learning model implemented using Content-Based Collaborative Filtering model. In the proposed recommender system model, similarities between the learning materials and the description of the course being are computed as basis for developing recommended learning materials to students. The recommendation system modeling for personalized learning is applied as a solution to achieve learning goals and minimize dropouts due to the inability of students to understand the learning process in online learning systems. The results given in the research are modeling of an adaptive learning personalization recommendation system to provide recommendations for students who do not reach the minimum competency standards. © 2022 IEEE.

关键词： Recommender systems

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COCOMO II Analysis of Developing Multi-Account Partner Software for Crypto Exchange 15

COCOMO II Analysis of Developing Multi-Account Partner Softw...

引用

15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Darmawan, Oscar Adam Gaol, Ford Lumban Soeparno, Haryono Arifin, Yulyani Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350383829

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. © 2023 IEEE.

关键词： Blockchain

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GIS-Based MCDM for Central Business Suitability in a Small City 5

GIS-Based MCDM for Central Business Suitability in a Small C...

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5th International Conference on Informatics and Computing, ICIC 2020

作者： Herlawati, Herlawati Abdurachman, Edi Heryadi, Yaya Soeparno, Haryono Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728180472

Extended urbanization phenomenon in smaller-sized cities in Java should be considered by the government. However, the local governments can only use the plans from the central government. The negative effects of this, among others, are improper land use allocation, lack of facilities, and crimes that are difficult to handle. This study proposes a geographic information system (GIS)-based method to analyze the proper central business district in Karawang, a small district in Java, Indonesia. Multi-criteria analysis of factors affecting the candidate locations was used through a weighted sum method in a model. The spatial data were retrieved and analyzed using a GIS tool to classify region into urban, peri-urban, and rural. Some central business locations have been found after reclassification which is located near the toll gates. Two locations in the north of Karawang were classified have the potential to become the new central business locations. © 2020 IEEE.

关键词： Geographic information systems

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E-commerce Security Issues for SMEs: A Systematic Literature Review 7th

E-commerce Security Issues for SMEs: A Systematic Literature...

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7th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2022

作者： Gunawan, Zaldy Abdurachman, Edi Soewito, Benfano Gaol, Ford Lumban Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9789819950140

Significant advancements in information technology have altered existing markets, transforming them from physical to online electronic markets (e-commerce). The practice of conducting business online by smaller and medium-sized companies has received encouragement and assistance from governments all around the world, particularly those of developing nations. With the evolution of e-commerce, businesses must find a better way to implement e-commerce while maintaining its security. The purpose of this article is to carry out a Systematic Literature Review (SLR) in order to determine the types of vulnerabilities that are present in e-commerce platforms used by SMEs. As a result, in the conclusion, security issues and recommended solutions related to e-commerce systems for SMEs will be presented. These outcomes can serve as a foundation for further study on e-commerce security issues, such as solutions and recommendations for providing a secure e-commerce system for SMEs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic commerce

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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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Deep learning and Threshold Probability for Out of Scope Intent Detection in Task Oriented Chatbot 3

Deep learning and Threshold Probability for Out of Scope Int...

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3rd International Conference on Artificial Intelligence and Data sciences, AiDAS 2022

作者： Hasani, Muhammad Fikri Gaol, Ford Lumban Soewito, Benfano Warnars, Harco Leslie Hendric Spits Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

A lot of research on task-oriented chatbots has been carried out to improve research on intent recognition. However, there are not many studies that discuss out-of-scope. This study introduces the use of threshold for classification results. The use of threshold improves recall performance of OOS detection above baseline for BiLSTM but reduces recall inside scope for all models. Directly applying multiclass classification with combined class of inside scope and out of scope also showed higher recall than baseline. © 2022 IEEE.

关键词： Deep learning

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Implementation of Light Gradient Boosting Machine (LGBM) for Customer Services Feedback Classification 5

Implementation of Light Gradient Boosting Machine (LGBM) for...

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5th International Conference on Artificial Intelligence and Data sciences, AiDAS 2024

作者： Anggreainy, Maria Susan Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798331528553

Customer complaints are an important aspect in the business world. Every company must face complaints from its customers, whether related to unsatisfactory products, services or experiences. Customer complaints are a sign of unmet needs or expectations. A customer service person is tasked with serving questions asked by customers or consumers. The purpose of this research is to classify types of complaints to help customer service. The classification carried out is in binary form, namely disputed or not. This research compares several machine learning algorithms for classification of customer questions into appropriate categories for more efficient responses. Based on evaluation metrics, the F1 Score light gradient boosting machine (LGBM) is the best model compared to Multinomial Naive Bayes (MNB), Logistic Regression (LR), SVM (Support Vector Machine), and EXTC (Extra Tree Classifier). © 2024 IEEE.

关键词： Sales

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Identification of Clustering BRISK Keypoint Feature in Grocery Product Using the Elbow Method

Identification of Clustering BRISK Keypoint Feature in Groce...

引用

2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Rahmania, Rissa Anggreainy, Maria Susan Kartowisastro, Iman Herwidiana Budiharto, Widodo Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350353464

In smart retail, the problem in grocery product image classification is fine-grained image representation. Product variants are characterized by differences in color, text, and size. However, while the process of image classification involves quite complex stages, there is still a misclassification of grocery products. This is because there are many areas of the image being calculated, which makes the models not optimally calculate the most important area. This paper aims to identify a number of patch image formations as an important area of the product by involving Binary Robust Invariant Scalable Keypoints (BRISK) as a feature descriptor, K-Means Clustering, and the elbow method. First, we experiment with the BRISK keypoint feature for grocery products with different categories of brands. Second, the keypoint features are clustered using K-Means clustering. Lastly, the number of clusters is analyzed using the elbow method to find the optimal number. The result shows that the number of clusters from 8 products has variations of 5, 5, and 6 clusters. Finally, each cluster represents an important area in forming patches to support fine-grained grocery product image classification. ©2024 IEEE.

关键词： K-means clustering

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A Survey of Refactoring Techniques to Maximize Code Coverage Metric 15

A Survey of Refactoring Techniques to Maximize Code Coverage...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Simatupang, Johannes 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)9798350383829

Refasctoring is a technique used in software development to improve the quality of code without changing its functionality. One metric that is often used to measure code quality is Code Coverage. This study aims to examine refactoring techniques that can maximize Code Coverage Metric. Through the study, identification, evaluation, and summary of empirical evidence from various literature sources are carried out. The results of this study provide guidance on effective refactoring techniques to improve Code Coverage as well as other positive impacts for software development. There are ten refactoring techniques that can be used to improve Code Coverage Metrics in software testing. © 2023 IEEE.

关键词： Software testing

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