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检索条件"机构=Computer Science Department Binus Graduate Program - Master of Computer Science"

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Improving Supplier Evaluation Model using Ensemble Method-Machine Learning for Food Industry 8

Improving Supplier Evaluation Model using Ensemble Method-Ma...

引用

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

作者： Asrol, Muhammad Wahyudi, Sofyan Suharjito Harito, Christian Utama, Ditdit N. Syafrudin, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Artificial Intelligence Sejong University Seoul05006 Korea Republic of

Supplier evaluation has a crucial role in maintaining efficiency in the food industry supply chain. Machine learning approaches can be employed to formulate models aimed at analyzing and evaluating supplier performance. Previous research has successfully designed decision tree and neural network models for assessing suppliers in the food industry with accuracies of 84.2% and 92.8% separately. Recognizing the opportunity to improve the model's performance, this study aims to advancing the machine learning models accuracy for analyzing and evaluating suppliers in the food industry. Two main models are proposed to enhance model accuracy: ensemble methods and support vector machine. This research has successfully designed a supplier evaluation model and demonstrated that the ensemble method - gradient boosting model outperforms other ensemble methods and support vector machine which is achieved a accuracy of 93.6% on a cross-validation dataset. The development of a dashboard is required to implement the supplier evaluation model using machine learning, facilitating decision-makers in evaluating and controlling supplier performance. © 2023 The Authors. Published by Elsevier B.V.

关键词： Ensemble Method Food Machine Learning Supply chain Supplier evaluation

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Simple Fuzzy Decision Support Model for Evaluating the Cryptocurrency's Performance

Simple Fuzzy Decision Support Model for Evaluating the Crypt...

引用

2022 International Conference of science and Information Technology in Smart Administration, ICSINTESA 2022

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

ISBN: (纸本)9781665472883

Evaluation of cryptocurrency's performance is performed questionably. There is no role for computer-model, make such an evaluation process does not have guidance. In this study, a simple decision support model (DSM) based on fuzzy logic was academically constructed to observe the cryptocurrency's performance. By operating the primary method of fuzzy logic and taking into account three types of parameters (i.e. time-series close price data, daily max-min price, and transaction number), a novel DSM for evaluating the cryptocurrency's performance was fruitfully executed. Based on three types of real cryptocurrency six-month data (i.e. Bitcoin Ethereum, and Dogecoin), the model could irreversibly expose that Bitcoin has the best performance with 36.39 performance points. © 2022 IEEE.

关键词： Fuzzy logic

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AI-Based Video Analysis for Driver Fatigue Detection: A Literature Review on Underlying Datasets, Labelling, and Alertness Level Classification 1

引用

International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Ariansyah, Dedy Rahutomo, Reza Elwirehardja, Gregorius Natanael Asadi, Faisal Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Reduced alertness because of fatigue or drowsiness accounts for a major cause of road accidents globally. To minimize the likelihood of alertness reduction-related crashes, a video-based detection emerges as a non-intrusive method and can offer high accuracy for the countermeasure development. Many Artificial Intelligence (AI) techniques have been developed to aid in video analysis for fatigue detection. However, the issues in AI-based video analysis for fatigue countermeasure development are rarely discussed. This paper reviews current video-based countermeasure development in the literature and highlights some reliability issues of AI-based fatigue countermeasure development in terms of underlying dataset, labelling method, and alertness level classification. The result serves as insights and consideration to academics and industrialists in developing a high reliable AI-based video analysis for driver fatigue countermeasure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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BERT base model for toxic comment analysis on Indonesian social media 7

BERT base model for toxic comment analysis on Indonesian soc...

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

作者： Nabiilah, Ghinaa Zain Prasetyo, Simeon Yuda Izdihar, Zahra Nabila Girsang, Abba Suganda Computer Science Department Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Social media is an online media that functions as a platform for users to participate, share, create, and exchange information through various forums and social networks. The rapid increase in social media activity causes an increase in the number of comments on social media. This is prone to triggering debate due to the easy formation of open discussions between social media users. However, the debate often triggers the emergence of negative things, causing great fights on social media. Social media users often use comments containing toxic words to argue and corner a party or group. This study conducted an experiment to detect comments containing toxic sentences on social media in Indonesia using a Pre-Trained Model that was trained for Indonesian. This study performed a multilabel classification and evaluated the classification results generated by the Multilingual BERT (MBERT), IndoBERT, and Indo Roberta Small models. The optimal result of this study is to use the IndoBERT model with an F1 Score of 0.8897. © 2022 Elsevier B.V.. All rights reserved.

关键词： Social networking (online)

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UML Design for Decision Support Model in Determining the Sustainability of Online Course Materials 6

UML Design for Decision Support Model in Determining the Sus...

引用

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

作者： Sihotang, Erna Fransisca Angela Kurniawan, Afdhal Utama, Ditdit Nugeraha Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

The scenario of online learning is a very urgent need in the world of future knowledge. Since the Corona Virus Disease-19 pandemic, the world economy has started to plummet and caused many adults to lose their jobs. The advantage is the flexibility and rapid development of the internet. In 2020, the number of unemployed increased significantly. This reason makes people strive to improve their ability to meet job requirements by taking online courses. Online courses are a way that people can choose to improve their skills anywhere and anytime. The sustainability of online course material that is offered to the course user and issued by the company will be discussed in this study. The novelty of this research is to obtain a decision support model based on fuzzy logic for determining online courses. The method used is decision-making based on UML and fuzzy logic for the final decision. The fuzzy inference model process begins by determining the decision parameters then using fuzzification with absolute input then refracted with fuzzy criteria, and ends with defuzzification with absolute output. There are two groups of parameters in this study, company profits which consist of 5 parameters and user benefits, which consist of 9 parameters. Once the model is verified and valid, the final decision is useful for users looking for online course and also useful for the decision unit of online course companies in determining the sustainability of online course materials. © 2022 IEEE.

关键词： Decision support model fuzzy logic online course UML

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Analyzing Company's Share Purchase Value Based on Financial Reports Using Artificial Neural Network (ANN) 9

Analyzing Company's Share Purchase Value Based on Financial ...

引用

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

作者： Shawn, Jeremy Gunawan, Jonathan Hao, Chen Zi Anggreainy, Maria Susan Kurniawan, Afdhal Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350370126

Because financial time series forecasting is sensitive to political, economic, and social factors, it is not a simple task. As a result, those who make investments in currency exchange and financial markets typically search for reliable models that can guarantee they will maximize their profile and minimize their losses. Fortunately, many studies have used a method from Artificial Neural Networks (ANNs) called Backpropagation, could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast stock share prediction from closing value of PT. Bank Central Asia Tbk, and PT. Bank Maybank Indonesia Tbk. The results show that the using Backpropagation gives the closest result. And for the rating of judgement for cast accuracy, it exceeded 10% accuracy, which means high accurate from the prediction. For further checking, comparing the results of research from Victor's results, it almost hits the same accuracy percentage. Which means, these prediction are accurate enough to do time series forecasting. © 2023 IEEE.

关键词： Backpropagation

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Energy Sector Stock Price Prediction Using the CNN, GRU & LSTM Hybrid Algorithm

Energy Sector Stock Price Prediction Using the CNN, GRU & LS...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Sulistio, Bambang Warnars, Harco Leslie Hendric Spits Gaol, Ford Lumban Soewito, Benfano Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Nowadays, many people are starting to care about early investment. One of the most popular investments lately, especially for millennials, is a stock investment. In investing, there are advantages and risks of loss. One way to reduce the risk of loss is by using price predictions before investing in stocks. This paper proposes the use of deep learning in making stock predictions. We conducted research by calculating the performance of six deep-learning algorithms to predict stock closing prices. The application of the CNN-LSTM-GRU hybrid algorithm combination produces the best performance compared to other methods, based on the value: Root Mean Squared Error (RMSE) decreased by 1.100 by 14%, Mean Absolute Error (MAE) was successfully reduced by 0.798 by 13.4%, and R Square increased by 0.957 by 3.9%. In predicting stock prices on the Indonesian Stock Exchange, especially in the energy sector, CNN-LSTM-GRU is more appropriate for investors than using a single algorithm to make decisions in investing in stocks.. © 2023 IEEE.

关键词： Long short-term memory

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Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device 8

Objective Sleep Quality Measurement based on Fuzzy Logic and...

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

作者： Wijaya, Jason A. Andersen, Pauw D. Surantha, Nico Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method. © 2023 The Authors. Published by Elsevier B.V.

关键词： actigraphy artificial intelligences fuzzy logic sleep sleep quality

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Investigating the Impact on Data Recovery in computer Forensics 1

Investigating the Impact on Data Recovery in Computer Forens...

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1st IEEE International Conference on Cryptography, Informatics, and Cybersecurity, ICoCICs 2023

作者： Yulianto, Semi Soewito, Benfano Bina Nusantara University Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Graduate Program. Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339437

Defragmentation can potentially be employed as a tactic by perpetrators to conceal, misrepresent, or eliminate digital evidence. This study explores the effects of minor defragmentation, a potential method to conceal digital evidence, on recovering file system data in digital forensics. Our investigation sought to determine the influence of minor defragmentation on the effectiveness of data recovery and to identify methods that can augment the success rate post-defragmentation. We limited the scope of this study to defragmentation in Hard Disk Drives (HDDs), solid-state drives (SSDs), and USB drives. A mixed-method approach employs a literature review, case studies, and controlled experiments. Comparative analysis was used as the main data analysis technique to investigate its impact. Preliminary findings suggest that minor defragmentation hampers data recovery;however, certain strategies can augment success rates. These results can significantly influence the development of data recovery policies, particularly those of digital forensic analysts and law enforcement. The primary objective of this study is to bolster the efficiency and dependability of file system data recovery post-defragmentation while upholding ethical and legal standards. © 2023 IEEE.

关键词： Recovery

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Optimized LightGCN for Music Recommendation Satisfaction 6

Optimized LightGCN for Music Recommendation Satisfaction

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

作者： Hansel, Aaron Christian Adrianus Pradana, Lunardi Suganda, Abba Girsang Nugroho, Ariadi Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

With the large amount of information available on the internet, recommendation tasks have grown to be more crucial than ever. Businesses that store digital media on the internet such as video streaming and music streaming platforms, benefit a lot from recommendation systems. A simple yet powerful recommendation system that can give better recommendation performance is always being sought after. Light Graph Convolution Network (LightGCN) is a simplified version of Graph Convolution Network (GCN) for collaborative filtering in recommendation systems. LightGCN architecture includes only the most essential part of GCN for collaborative filtering that is the neighborhood aggregation, it removes the feature transformation and nonlinear activation because both of them contribute little to no effect to the recommendations. The focus of this research is to optimize LightGCN by tuning the hyperparameters using exhaustive search (grid search). The optimized LightGCN model is able to out-perform LightGCN by more than 140% in music recommendation © 2022 IEEE.

关键词： exhaustive search hyperparameter tuning LightGCN music recommendation Recommendation system

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