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Investigation of player emotion and instinct on racing video game using 6-11 framework (Case study: split second (2010) single player modes)

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Procedia computer science 2024年 245卷 87-99页

作者： Galih Dea Pratama Andien Dwi Novika Elshad Ryan Ardiyanto Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta 11480 Indonesia

Racing is one of the most prominent genres in the modern video game industry, where the games within the genre enable players to use any vehicle of their choice and win through doing series of action within gameplay. Thus, it made the genre popular over the years, spawning many famous titles such as Split Second back in 2010. Like every other video game title, Split Second also invokes the player emotion and instinct during its gameplay session. To understand the emotion and instinct aspect present in players of the game, we will use 6-11 framework which focuses on the two aspects. As part of the conclusion, Split Second game involves instincts such as Competition, Collecting, and Survival, with emotions including Fear, Pride, Joy, and Excitement, which can be found from its vast single player game modes.

关键词： video games racing video games game analysis 6-11 framework

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E-Govchurch as a Digital Transformation Initiative for Nation-wide Christianity Organization 1

E-Govchurch as a Digital Transformation Initiative for Natio...

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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Paramita, Adi Suryaputra Prabowo, Harjanto Ramadhan, Arief Sensuse, Dana Indra Graduate Program Universitas Ciputra Surabaya Bina Nusantara University Surabaya Indonesia/Jakarta Information Systems Department/Doctor Of Computer Science Indonesia Doctor Of Computer Science Graduate Program Bina Nusantara University Jakarta Indonesia Universitas Indonesia Faculty Of Computer Science Depok Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

The process of digital transformation is part of the 4.0 industrial revolution. Through digital transformation, it is hoped that all processes inside an organization will function more efficiently. Internet technology is the major driver of an organization's digital transformation effort. Information Systems for the statewide scope of the two main religion-based organizations in Indonesia are required to satisfy the Indonesian government's National Medium-Term Development Plan for the years 2020-2024 in accordance with Indonesian President Decree No. 18 of 2020. The regulation mandates that the ministry of religious affairs provide information and data services. E-Govchurch, an abbreviation for Electronic-Governmental Church, is the suggested framework. E-Govchurch is a digital program for Information Systems that incorporates a framework for data governance. E-Govchurch will develop utilizing SSM, RBV, the Zachman Framework, and the DAMA framework. The purpose framework in research will lead the next phase of framework development process. © 2022 IEEE.

关键词： Information systems

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Weather Monitoring System AIoT Based for Oil Palm Plantation Using Recurrent Neural Network Algorithm 5

Weather Monitoring System AIoT Based for Oil Palm Plantation...

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5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Mimboro, Prasetyo Lumban Gaol, Ford Lesie Hendric Spits Warnars, Harco Soewito, Banefano BINUS University Computer Science Graduate Program Jakarta Indonesia BINUS University Computer Science Jakarta Indonesia

ISBN: (纸本)9781665401968

Artificial Intelligence of Things (AIoT) is a new computing technology that combines Artificial Intelligence (AI) and Internet of Things (AIoT) technologies that are used to improve data analysis systems on implanted devices. IoT can be an innovative solution in the development of more efficient embedded devices This research will delineate the description of the AIoT system using the Recurrent Neural Network (RNN) algorithm. Weather parameters used as input are humidity, temperature, wind direction, wind speed, and atmospheric pressure. Based on the insert received from the sensor, the accuracy of the RNN algorithm, will be known to the prediction results of weather conditions in oil palm plantations. The purpose of this research is to develop an IoT device with an artificial neural network algorithm embedded in the device to predict weather conditions in real-time. © 2021 IEEE.

关键词： Internet of things

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Intelligence Tutorial System for Learning Cultures and Local Languages in Indonesia 2

Intelligence Tutorial System for Learning Cultures and Local...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Veron, Juan Sebastian Tantry, Calvin Fladico, Naxwell Warnars, Harco Leslie Hendric Spits Bina Nusantara University School of Computer Science Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

Indonesia has so many local cultures and languages that spread from Sabang to Merauke, and most Indonesians do not know how to speak two or more regional languages in Indonesia. Most Indonesians only know familiar tribes that are only commonly heard. Because of this problem, governments in the Indonesian provinces have included lessons on Indonesian culture. The lesson will explain Indonesia's national and local culture for their area. In this way, the government can also help Indonesians learn more about their local language. This paper proposes a solution to create a learning portal website for users, both from Indonesia and other countries, to learn Indonesian culture and regional languages. In this way, the author hopes that users can learn about Indonesian local culture and language and contribute to preserving Indonesian local culture and language. This application was built on a web-based basis by applying the following HTML, CSS, and javascript using Personal Home Pages (PHP) for database connection using MySQL. The built application was designed using a use case diagram and class diagram to describe the database relations. © 2022 IEEE.

关键词： Websites

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Comparison of K-Nearest Neighbor and Support Vector Regression for Predicting Oil Palm Yield 1

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

作者： Pardamean, Bens Suparyanto, Teddy Sinaga, Gokma Sahat Tua Elwirehardja, Gregorius Natanael Firmansyah, Erick Ginting, Candra Mawandha, Hangger Gahara Putra, Dian Pratama 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 Faculty of Agriculture Institute of Agriculture STIPER Yogyakarta55282 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

With the high demand for oil palm production, implementations of Machine Learning (ML) technologies to provide accurate predictions and recommendations to assist oil palm plantation management tasks have become beneficial, such as in predicting annual oil palm. However, different geographical and meteorological conditions may result in different scales of influence for each variable. In this research, K-Nearest Neighbors (KNN) and Support Vector Regression (SVR) were used in predicting oil palm yield based on data collected in Riau, Indonesia. Pearson’s correlation coefficient was also calculated in selecting the input features for the models, whereas normalization and standardization were used in scaling the data. By setting the minimum absolute correlation threshold to 0.1 and using standardization, both models managed to obtain more than 0.81 R2, with SVR obtaining the overall best performance with 0.8709 R2, 1.372 MAE, and 1.8025 RMSE without hyperparameter fine-tuning. It was also discovered that the oil palm yield in the previous year is the variable with the most influence in estimating oil palm yield in the current year, followed by the number of plants and soil types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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LLM2Vec Sentence Embeddings Analysis in Sentiment Classification

LLM2Vec Sentence Embeddings Analysis in Sentiment Classifica...

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2024 IEEE International Conference on Data and Software Engineering, ICoDSE 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Isnan, Mahmud Kurnianingrum, Dian Ratnapuri, Chyntia Ika 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 Entrepreneurship Department Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331506407

Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment classification is LLM2Vec, which allows Transformer decoder-based models to generate sentence embeddings for text representation. Its capability is deemed language-agnostic, which, in this study, the framework is leveraged for Tokopedia tweet sentiment analysis to prove the claim. The base decoder models used in the LLM2Vec framework were Llama 3 8B, Llama 2 7B, Sheared Llama 1.3B, and Mistral 7B. Two BERT-based models, which are the Indonesian SBERT model and IndoBERT trained with the SimCSE approach, were employed as a comparison. The generated embeddings were classified using logistic regression, SVM, and MLP Classifier. Classifiers using embedding generated by LLM2Vec with Llama 3 8B and Mistral 7B achieves on-par performance with classifiers that utilize IndoBERT SimCSE embeddings, while classifiers using embeddings generated by LLM2Vec with Llama 2 7B and Sheared Llama 1.3B achieves much lower performance. Classifiers with Indonesian SBERT embeddings achieve the highest F1 score performance. Despite slightly lower performance, this study has proven the language-agnostic capability of LLM2Vec, especially with Llama 3 8B and Mistral 7B in colloquial Bahasa Indonesia sentiment analysis, since none of the base decoders were ever trained using the Bahasa Indonesia corpus. © 2024 IEEE.

关键词： Decoding

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Product Label Clustering Based on Search Keywords for Product Name Recommendations in Ecommerce

Product Label Clustering Based on Search Keywords for Produc...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Bryan Daniel Alexander Maria Susan Anggreainy Devi Fitrianah Ajeng Wulandari Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduiate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduiate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

One of the key challenges in e-commerce is how to provide relevant and personalized product recommendations to users. To achieve this, data analysis and text processing techniques are essential. This research aims to analyze product descriptions based on purchase history and product labels on e-commerce platforms. The objectives of this study are: Analyzing and clustering product descriptions using clustering techniques such as K-Means Clustering. This helps in understanding the patterns and relationships between the various product descriptions present. Using the results from clustering to develop an effective recommendation engine. This recommendation engine will utilize collaborative filtering techniques, where product recommendations are suggested based on similarities between the user's purchase history and relevant product clusters. By analyzing data and developing a recommendation engine based on product descriptions, it is expected to improve the relevance and accuracy of product recommendations for users. This will not only improve the user's shopping experience but can also increase sales and customer satisfaction on e-commerce platforms.

关键词： Technological innovation Data analysis Accuracy Clustering algorithms Electronic commerce History Reliability Engines Recommender systems Text processing

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Trends of Digital Transformation in the Property Management Industry: A Systematic Literature Review

Trends of Digital Transformation in the Property Management ...

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Prasetya Cahya Saputra Harjanto Prabowo Arief Ramadhan Evaristus Didik Madyatmadja Computer Science Department BINUS Graduate Program-Computer Science BINUS University Jakarta Indonesia Management Department BINUS Business School Doctor of Research in Management BINUS University Jakarta Indonesia Telkom University Bandung Indonesia School of Information System BINUS University Jakarta Indonesia

After the Covid 19 pandemic, the property management industry is slowly starting to revive. Even though the occupancy rate has not returned to normal like the Covid19 pandemic, its growth is starting to move in a positive direction. The Covid19 pandemic has provided many lessons for all stakeholders in the property management industry. Digital transformation has become a part that has received important attention in the property management industry worldwide. With digital transformation in the property management industry, all stakeholders are expected to receive various benefits, directly or indirectly. The implementation of digital transformation will involve 6 components, namely: Strategy, Organization, Culture, Customer, People, and Technology. The implementation of digital transformation in the property management industry will require support from many parties, not only internal parties but also external parties. Technological developments, IoT, and demands from residents and property owners are challenges that must be answered by property managers. This systematic literature review will examine articles that have been published in the Scopus database regarding the development of digital transformation in the property management industry from various countries. The purpose of this SLR is to be able to see how the digital transformation trend and the application of its components are in the current property management industry.

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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

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

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Pangestu, Gusti Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University School of Computer Science Computer Science Department Binus Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9781665468763

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising. © 2022 IEEE.

关键词： Convolutional neural networks

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