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Comparison of Training Function, Adaption Learning Function, and Transfer Function of Hidden Layers in Artificial Neural Network in Weather Prediction 9

Comparison of Training Function, Adaption Learning Function,...

引用

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

作者： Dewi, Adhe Lingga Setyoko, Dimas Elang Setyo Adhi, Canggih Gelar Afrilia, Nur Sitha School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Digital Language Learning Center Faculty of Humanity Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Weather prediction plays an important role in human life, such as agricultural commodities, aquaculture, trade, industry, tourism systems, airport/flight systems, mining industry, etc. This study is to compare the performance of several Training Function, Adaption Learning Function and Transfer Function in Artificial Neural Networks for weather prediction. The data used is historical weather that occurred in Semarang City, Indonesia from January 1, 2012, to December 31, 2022, as training data. Meanwhile, the data used as test data is historical weather data that occurred during the period from January 1, 2023, to December 31, 2023. The weather data consists of average temperature, humidity, and wind speed. Based on the training results, using trainlm, learngdm, and tansig which was trained using 30 hidden layers and 1000 epochs has the lowest MSE value of 5.08. Apart from that, the regression value of the training data also showed good results. That is, 0.99091 for details, 0.99102 for training, 0.98922 for validation, and 0.99263 for testing. The ANN architecture that has been trained using the configuration mentioned is used to make predictions on test data. The test results show that the ANN prediction error for temperature is 1.45% and humidity is 6.68%. Based on the results, it is proven that comparing three key factors of ANN for weather forecasting has a significant impact on the weather prediction ability of Artificial Neural Network. © 2024 IEEE.

关键词： Weather forecasting

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An Object-Driven Fuzzy Decision Model for Determining Fuel in Boiler Machines 4

An Object-Driven Fuzzy Decision Model for Determining Fuel i...

引用

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

作者： Utama, Ditdit Nugeraha Widyanto, Jeremia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

There are many alternative fuel types that can be used by a particular type of engine. Determining which fuel alternative is optimal for an engine presents its own challenges. This is especially true in an industrial setting, where certain standards are necessary to select the best fuel alternative to be used and operated. This study aims to develop a decision model for selecting a fuel type for boiler engines based on specific parameters or criteria. The decision model is designed using an object-oriented approach, utilizing three types of diagrams: object, activity, and sequence. An object-oriented model design approach allows for the decision model to be depicted transparently and very close to reality. Additionally, the analytical hierarchical process (AHP) and fuzzy logic are the core methods employed in constructing the decision model. Then, in developing this decision model, four main criteria or parameters are considered: cost, heating value, safety, and emissions. These four parameters were derived from a literature review and interviews with experts. Among the three evaluated fuel types (i.e., natural gas, industrial diesel oil or IDO, and coal), coal received the highest decision score of 0.78. Thus, based on this decision value-the outcome of the model's recommendation-it can be concluded that coal remains an objective choice as a fuel for use in boiler engines. © 2024 IEEE.

关键词： Coal

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Unlocking Consumer Minds: Navigating Purchase Intentions through the Lens of Augmented Reality and the Technology Acceptance Model 1

Unlocking Consumer Minds: Navigating Purchase Intentions thr...

引用

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

作者： Amanah, Dian Martha Nurrul Aini, Nurul Putri, Novinda Krisna Dewi, Adhe Lingga Soelistyoningrum, Jenia Nur Bina Nusantara University Binus Business School Undergraduate Program Digital Business Department Semarang Indonesia Bina Nusantara University School of Computer Science Computer Science Department Semarang Indonesia

ISBN: (纸本)9798350360554

As a relatively new technology, augmented reality can help business continuity. This technology can maximize various business activities, one of which is marketing. Unfortunately, this technology is still rarely used for business needs in Indonesia. Hence, this paper aims to investigate how the technology acceptance model affects augmented reality-based marketing applications and their impact on the intention to make a purchase. The research adopted a quantitative methodology, specifically employing survey techniques. The primary data gathered underwent analysis through Partial Least Squares Structural Equation Modeling (PLS-SEM). The process of data collection included the distribution of online questionnaires to individuals actively utilizing smartphones. These participants were subsequently requested to engage with one of the five selected augmented reality marketing applications: Shopee (Beauty Cam), Sephora (Virtual Artist), IKEA Place, and Dulux (Visualizer). To uphold result reliability, the study encompassed a total of 109 respondents. The study revealed that perceived usefulness, and perceived enjoyment has a positive influence on purchase intention. Besides that, perceived ease of use has a negative influence on purchase intention. Based on the results of this research, it is important for business to be able to capture profitable opportunities from adopting augmented reality technology. So that in the future they will be able to provide the best experience for consumers. © 2023 IEEE.

关键词： Augmented reality

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Transformer and CNN Comparison for Time Series Classification Model 15

Transformer and CNN Comparison for Time Series Classificatio...

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

作者： Sonata, Ilvico Heryadi, Yaya Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350383829

In real life, many activities are performed sequentially. These activities must be carried out sequentially, such as the assembly process in the manufacturing production process. This series of activities cannot be reduced or added so that the main goal of the series of activities is achieved. Apart from that, there are also time series events that occur naturally, such as rainy and hot conditions in a certain area. The classification process of time series activities is very important to see the possibility of anomalies occurring. The significant development of machine learning models in recent years has made the process of classifying time series data increasingly researched. Several previous studies stated that deep learning models were more accurate in classifying time series data. In this paper, we will compare Convolutional Neural Network (CNN) and Transformer deep learning models in classifying time series data. Experimental results using the same public datasets for CNN and Transformer model show that the CNN model is more accurate than the Transformer model. The results of measuring accuracy using confusion matrix show that CNN has an accuracy of 92% and Transformer has an accuracy of 80%. © 2023 IEEE.

关键词： Time series

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Testing How Dating Apps Recommend a Potential Matches for User 8

Testing How Dating Apps Recommend a Potential Matches for Us...

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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Soemardi, Calvin Hartono Radityaputra, Mario Indra Rahman, Raiza Santoso Gunawan, Alexander Agung Saputra, Karen Etania School of Computer Science Bina Nusantara University Bandung Campus Computer Science Department Jakarta11480 Indonesia School of Computer Science Binus Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350344691

In this age of globalization, the internet has gradually entered every aspect of our daily life even in our love lives. Dating has already entered the digital realm where people around the world could find the person of their type by swiping through multiple people on an application or even a website. Dating apps even have the feature of providing several recommendations based on the user's type. When a user created an account, they would be given several questions related to their type of people they want to date to know which type of person to recommend but sometimes, the application itself would recommend a user, a person that does not meet the initial requirements of the user itself. The goal of this research is to find the accuracy of the recommendation algorithm from the dating apps with the tag or interests that the user set with the recommended match and the fluctuation of the accuracy to conclude the most accurate matching based on aligned interest. © 2023 IEEE.

关键词： aligned interests dating apps Elo Rating System Elo Score Gale-Shapley Algorithm

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Preventing Fraudulent Activities Using Graph Neural Networks 8

Preventing Fraudulent Activities Using Graph Neural Networks

引用

8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

作者： Devlin Lauwira, Andrew Zakkiyah, Alfi Yusrotis School of Computer Science Bina Nusantara University Mathematics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368970

Fraud is a problem found by a lot of people from individual to government institution. Credit card fraud, can cause substantial financial losses. To prevent fraud, it is crucial to develop models that can detect fraudulent activities early by analyzing transaction patterns. Graph Neural Networks, also known as GNNs, helps to deal with this problem. GNNs utilizes the complex relationship in transactions to identify intricate patterns, then label the suspicious patterns as fraud. In this study, we used a GNN-based model for credit card fraud detection that achieves an accuracy of 99.81%. Additionally, we identify the most important features utilized by the GNN model. Our finding demonstrates the effectiveness of GNN in detecting credit fraud and provides valuable insight into feature selection for fraud detection models. © 2024 IEEE.

关键词： Graph neural networks

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Detection of Employee Fatigue Based on Image Processing Using Deep Learning Model 4

Detection of Employee Fatigue Based on Image Processing Usin...

引用

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

作者： Ethan, Michael Kusuma, I. Gede Putra Bina Nusantara University Computer Science Department Binus Graduate Program Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

Fatigue in workplace is a common thing shared by all employees. Continuous exposure of fatigue could lead to negative productivity for companies. Current research on fatigue detection mostly focused to detect fatigues from a single 2D image data, meanwhile research on fatigue detection using each frames from video data is needed for better detection for fatigue process. The main advantage of using each timesteps of frames from video data which makes the model is able to predict long term dependency of fatigue person. To solve this problem, a deep learning model is proposed that could detect employee fatigue based on image processing using video data. Three models are used to train data using Convolutional Neural Network (CNN), which are Time Distributed CNN LSTM, 3D CNN, and 3D CNN LSTM. Out of those three models, the best model to detect fatigue person from video data is Time Distributed CNN Model with F-1 Score value of 0.77 and Accuracy Score of 0.76 for testing data. The model that gives best inference time is also Time Distributed CNN Model with the average value of 382 miliseconds to detect fatigues from 58 testing data with each of the data has duration of 3 seconds and 12 frames. © 2024 IEEE.

关键词： Video analysis

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Fine-Tuning Fasttext Using Bayesian Optimization for Movie Review Sentiment Analysis 1

Fine-Tuning Fasttext Using Bayesian Optimization for Movie R...

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

作者： Halim, Rico Girsang, Abba Suganda Riftiarrasyid, Mohammad Faisal Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350360554

The advancements in technology during the 20th century resulted in the onset of the digital computer era. This study investigates the relative importance of earlier algorithms in comparison to more recent ones. Various research projects suggest improving vectorization by combining conventional and modern techniques, while others suggest optimizing it through algorithmic methods. This study primarily focuses on employing Bayesian optimization to optimize hyperparameters, hence improving the performance evaluation of TF-IDF FastText sentiment classification models. This study presented four models: the initial model utilized FastText with a preprocessing step that involved removing stopwords, the second model employed Fast-Text without any optimization using Bayesian optimization, the third model utilized FastText with optimization using Bayesian optimization, and the fourth model combined FastText with TF-IDF and was further optimized using Bayesian optimization. The Support Vector Machine (SVM) technique will be employed to evaluate all of the models. The findings suggest that the model's performance stays unchanged when stopwords are eliminated (Precision, Recall, F1: 0.9007). The model demonstrates a slight enhancement through the utilization of Bayesian optimization, resulting in a 0.02% increase, reaching a final accuracy of 0.9019%. Bayesian optimization is frequently employed to improve performance. The performance of Bayesian optimization is affected by the magnitude of the learning rate, and models that do not utilize Bayesian optimization demonstrate inferior performance. Both the TF-IDF FasText model and FastText model yielded comparable outcomes, attaining an F1 score of 0.9019 after being tuned by Bayesian optimization. © 2023 IEEE.

关键词： Sentiment analysis

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Count-Data Mixed Models of Topical Tweets: A Case of Indonesia Flood Events 8

Count-Data Mixed Models of Topical Tweets: A Case of Indones...

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

作者： Hidayat, Alam Ahmad Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Examining topic-level variability in modeling Twitter data can potentially yield more comprehensive insights into public perception during critical periods, thereby enhancing natural disaster mitigation and surveillance efforts. In this study, we utilized generalized linear mixed models (GLMMs) to illustrate the variability in tweet counts related to specific topics in Indonesia during the flood events that occurred in February 2021. The glmmTMB library in R was employed for this purpose. The data were assumed to follow two distinct exponential distributions: Poisson and Negative Binomial. To incorporate random effects, random intercepts and random slopes were introduced, allowing them to vary randomly across topics in the initial two models. Additionally, the final model addressed issues related to dispersion and zero-inflation. By evaluating the Akaike Information Criteria scores, we determined that a model based on the Negative Binomial distribution with random zero-inflation intercepts best fit the data. The chosen model formulation and the estimated parameters have the potential to forecast topic-specific trends in Indonesian flood-related Twitter data. © 2023 The Authors. Published by Elsevier B.V.

关键词： count data disaster surveillance floods mixed models Twitter

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Literature Review: Analysis of Essential Metadata Attributes of Digital Goods for Transaction in The Virtual World 9

Literature Review: Analysis of Essential Metadata Attributes...

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

作者： Martha, Gede Indra Raditya Prabowo, Harjanto Gaol, Ford Lumban Wiputra, Richard BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Business School Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In online-based virtual worlds such as Metaverse, Online Games, and other online digital spaces, the virtual/digital goods (digital items / digital assets) are fundamental things that must be available to be able to do business & create an economy in the virtual world through monetization & transactions from these digital goods. This research focuses on summarizing and exploring the attributes used in digital goods in the virtual world by extracting attributes in digital goods from other related studies using the Systematic Literature Review methodology & PRISMA Framework comprehensively so that it can be a reference for developers in designing the properties and functions of digital goods in the virtual world that are being developed, which in this case will be in the form of an NFT in the future. Based on research that has been carried out, 3 essential metadata attributes have the most influence on transactions, number 1 Visual Design / Aesthetic (Representative), number 2 Effect (Utilities), and number 3 Statistics / Performance (Utilities) which must be considered in developing/making digital goods in the form of NFTs in the virtual world so that the value of digital goods can attract users to buy or transact them in the virtual world. © 2024 IEEE.

关键词： Virtual environments

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