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

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

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Ditdit Nugeraha Utama Jeremia Widyanto Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

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.

关键词： Fuzzy logic Object oriented modeling Oils Coal Analytic hierarchy process Boilers Safety Engines Standards Systematic literature review

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On Efficient Processing of Queries for Live Multi-Streaming Soiree Organization

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IEEE Transactions on Services Computing 2023年第4期16卷 2812-2826页

作者： Kankeu Fotsing, C.P. Shen, Chih-Ya Huang, Liang-Hao Chen, Yi-Shin Lee, Wang-Chien Yang, De-Nian International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei115 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu300 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan Pennsylvania State University Department of Computer Science and Engineering State CollegePA16801 United States

Real-time social interactions and multi-streaming are two critical features of live streaming services. In this paper, we formulate a new fundamental service query, Social-aware Diverse and Preferred Organization Query (SDSQ), that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers for organization of a live multi-streaming soiree. We prove that SDSQ is NP-hard and inapproximable within any factor, and design SDSSel, a 2-approximation algorithm with a guaranteed error bound. Moreover, we study SDSQ-T, a special case of SDSQ, where the social graph is a threshold graph, and propose TDSSel, a 2-approximation algorithm without any error to SDSQ-T. We propose two pruning strategies, PCP and CDP to boost SDSSel and TDSSel. We further propose a more challenging but practical service query, Generalized Social-aware Maximum Preferred and Diverse Query (GSPQ), a generalization of SDSQ. We design GPDSel, a 4-approximation algorithm for GSPQ with a guaranteed error bound. We propose a strategy to improve the approximation ratios of the proposed algorithms. A user study on Twitch validates SDSQ, and the large-scale experiments on real datasets demonstrate the superiority of the proposed algorithms over several baselines for live-streaming services. © 2008-2012 IEEE.

关键词： Approximation algorithms

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News Classification Based On News Headline Using SVC Classifier 16

News Classification Based On News Headline Using SVC Classif...

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16th International Conference on Telecommunication Systems Services and Applications, TSSA 2022

作者： Leonard, Goldius Sisnadi, Fukriandy Wardhana, Nicholas Vigo Aziz Al-Ghofari, Muhammad Abdul Girsang, Abba Suganda School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665455947

News gives new insight and information from all over the world. News has many categories, such as politic, economy, science, and other common news categories. Every news will have their own category based on its content. The classification of news is usually done manually by inputting the category during the news posting. Some of the categories may be inputted incorrectly. The news classifier can be the solution for problem, but the news classifications out there are usually based on the news content. The classifier will receive the word vector inputs that are taken from the news content and try to classify it into one of certain categories. Unfortunately, news contents can be longer and harder to be processed rather than processing the news headline. The news headline is shorter and packs a decent information for the classifier to find out what category it is. Besides the news headline usage, the classifier also needs to be chosen correctly. In this paper, the SVC model will be tested using the news headline data to classify the news and compare with several other models, such as Linear Regression, Multinomial Naive Bayes, Decision Tree, and Random Forest. The common variables to be compared are the accuracy, recall, and precision to evaluate the SVC model. © 2022 IEEE.

关键词： Classification (of information)

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Deep neural networks and weighted word embeddings for sentiment analysis of drug product reviews 7

Deep neural networks and weighted word embeddings for sentim...

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

作者： Suhartono, Derwin Purwandari, Kartika Jeremy, Nicholaus Hendrik Philip, Samuel Arisaputra, Panji Parmonangan, Ivan Halim Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

One of the major tasks of natural language processing is sentiment analysis. The web is a source of unstructured and rich informa-tion with thousands of opinions and reviews. Individuals, businesses, and governments can all benefit from recognizing sentiment. As part of this study, we propose a deep learning-based approach for sentiment analysis on drug product review data obtained from the UCI machine learning repository. As an alternative to deep learning models, this architecture integrates glove word embedding with convolutional neural networks (CNN). Word2vec and GloVe word embedding schemes have been evaluated empirically for their predictive performance in CNN architectures. Based on a comparison of the deep learning architecture with RoBERTa, itcan be seen that BERT architecture outperforms both of them in training and validation. However, CNN models using Glove word embedding provided superior results in testing. © 2022 Elsevier B.V.. All rights reserved.

关键词： Embeddings

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Dual Word Embedding Framework for Gender-Based Writing Style Analysis Using Word2Vec and BERT

Dual Word Embedding Framework for Gender-Based Writing Style...

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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Aditya Riyanto Amalia Zahra Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

Social media has evolved into a vast and multifaceted data repository, presenting valuable opportunities to investigate gender-based variations in writing styles. This study aims to enhance the precision of gender classification on Twitter text by leveraging a combined word embedding approach. The paper proposes a framework that utilizes two distinct word embedding models - a combination of GloVe and BERT, as well as GloVe and Word2Vec - to capture both global and contextual linguistic nuances within the text. The dataset comprises a comprehensive collection of tweets and user profile descriptions harvested from Twitter, which underwent meticulous preprocessing and word representation steps before being further processed using the robust Random Forest algorithm. The experimental findings indicate that the combination of GloVe and BERT outperforms the other combination of GloVe and Word2Vec, and also surpasses the performance of single embedding models, with 62.93% accuracy being the highest achieved result. This study provides valuable insights into the potential of combined word embedding techniques for enhancing the accuracy of gender classification in social media text analysis.

关键词： Biometrics Text analysis Accuracy Social networking (online) Blogs Supervised learning Writing Linguistics Natural language processing Random forests

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A Decision Model for Smart Village in Measuring Socioeconomic Inequality

A Decision Model for Smart Village in Measuring Socioeconomi...

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International Conference on ICT For Smart Society (ICISS)

作者： Ditdit Nugeraha Utama Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Making socio-economic decisions is critical as they profoundly impact communities. It is imperative to approach these decisions with academic rigour and objectivity to effectively address prevalent community-wide issues. One such challenge often encountered by communities is socio-economic inequality. This study attempts to tackle this challenge by creating a decision model to assess socio-economic inequality in a region. The research was performed via four crucial stages, leading to the creation of a fuzzy decision model based on the Williamson Index (WI) method. This carefully developed model was designed and constructed using an object-oriented approach. The simulation results indicated that the integrated inequality index value for Sawah, an urban village in Ciputat, Indonesia, is 0.65. This em-pirical result provides valuable insights, enabling policymakers and stakeholders (as decision-makers) to design targeted policies and programs to reduce inequality and enhance community prosperity.

关键词： Object oriented modeling Simulation Indexes Stakeholders Socioeconomics

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An Object-Oriented Fuzzy Decision Model for Determining the Fittest Depot to React a Natural Disaster Emergency

An Object-Oriented Fuzzy Decision Model for Determining the ...

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Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC), IEEE

作者： Ditdit Nugeraha Utama Ayu Kristianty Ferina Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331515881

ISBN: (纸本)9798331515898

The domain of natural disaster procedure and management is highly challenging. The Indonesian government has proposed various strategies to address this issue. One of the strategies is making depots in every province to treat a natural disaster. This study aims to develop an object-driven decision model to identify the most suitable depot for mobilization to disaster areas. The model design and construction utilize objectoriented and fuzzy logic approaches. Ultimately, the constructed model can simulate 35 depots in Indonesia and identifies depot number 3, with a decision value of 50.40, as the most suitable for mobilization to the natural disaster in Pasaman, West Sumatra Province, which occurred on February 25, 2022.

关键词： Fuzzy logic Disasters Object oriented modeling Engineering management Government Asia Object recognition

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Parallelization of LSTM-GRU Architectures for Multivariate Prediction of Stock Prices

Parallelization of LSTM-GRU Architectures for Multivariate P...

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International Conference on Information Management and Technology (ICIMTech)

作者： Gabriel Asael Tarigan Eric Hermawan Abba Suganda Girsang Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

Anticipating stock market movements is challenging, as hasty prediction risks large financial losses. Stock price prediction is complex due to the high volatility and many influencing external factors. Recurrent Neural Network (RNN) models are made to learn time series data making it ideal for stock prediction based on records. Well-known RNN architectures for forecasting problems are Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). However, past studies only explored standalone models and have not generated convincing performance results. By integrating the efficiency of GRU to effectively handle shorter data sequences with LSTM's ability to capture long-term dependencies, our model aims to improve stock price forecasting accuracy by collaborating the strength between both architectures. The proposed model was used to forecast multivariate stock prices and evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). We found that parallelization of LSTM-GRU offers better prediction, up to 9.1% improvement compared to sequential LSTM-GRU and Bidirectional LSTM (Bi-LSTM).

关键词： Analytical models Sentiment analysis Accuracy Time series analysis Predictive models Logic gates Data models Information management Forecasting Stock markets

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Web Application based Ridesharing Your Transportation in Jakarta 2

Web Application based Ridesharing Your Transportation in Jak...

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

作者： Kusuma, Giovanni Randy Handana, Afan Rizqi Warnars, Harco Leslie Hendric Spits School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

The city of Jakarta is getting more and more congested and crowded by the number and types of vehicles. Lack of public awareness to use public transportation causes this congestion to worsen, especially during rush hours in the morning before going to work or school and in the afternoon when returning home. This is also caused by the lack of the government of the capital city of Jakarta in providing cheap, safe, and comfortable public transportation. It is not surprising that the Indonesian government has plans to move the capital city of the Indonesian government from Jakarta on the island of Java to East Kalimantan on the island of Kalimantan. Therefore, seeing this severe traffic jam, it is essential to propose an application that can reduce congestion so that drivers who bring their vehicles can share with people around them to travel to work as a routine activity during weekdays. The model of this vehicle sharing application was designed using use case diagrams, and the database design was designed using class diagrams. In addition, security issues are also significant considering that people meet in this vehicle sharing application, and some bad people may take advantage of it. Therefore user registration and moderation are essential. It is hoped that this vehicle sharing application will reduce congestion in the capital city of Jakarta and become an example in other cities. © 2022 IEEE.

关键词： Vehicles

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