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Multiple-Clustering Comparison in Spatial Data for Additional Accuracy Measurement in Land-use Classification 4

Multiple-Clustering Comparison in Spatial Data for Additiona...

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4th International Conference on Informatics and Computing, ICIC 2019

作者： Herlawati Heryadi, Yaya Hendric Spits Warnars, Harco Leslie Gaol, Ford Lumban Abdurachman, Edi Soewito, Benfano Bina Nusantara University BINUS Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728122076

Satellite imageries have been widely used to analyze a region by planners. Data from the satellite usually have lower accuracy than other expensive methods e.g. drone, aerial view, etc. However, the data from satellite have wide range area and sufficient enough for modeling a land use. The accuracy assessment, therefore, becomes a vital task to ensure the model from the satellite imagery meets the minimum requirement. Validating the classification result by comparing to the real location or by other higher resolution images is needed. The paper proposed additional validation by comparing the classification result by another result in different date through the cross-tabulation method. Two satellite imageries in the same year were processed before classification to get the land-use and land-cover classification. Comparing two land-use classified images gave the accuracy statistics using cross-tabulation. The kappa statistic and accuracy showed the classification performance of 0.7592 that similar to the sampling-based accuracy assessment (0.75390). Therefore, the proposed method was appropriate as the accuracy assessment. © 2019 IEEE.

关键词： Satellite imagery

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Object-Driven Fuzzy Decision Model for Assessing the Employees' Work from Home Eligibility 4

Object-Driven Fuzzy Decision Model for Assessing the Employe...

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

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

ISBN: (纸本)9798331506490

Work from Home (WFH), especially in the post-pandemic era, has emerged as an alternative work system adopted by many companies. Efficiency and effectiveness are the two main reasons why WFH is favored. In addition, the implementation of the WFH system in conducting business processes within the company allows for greater flexibility in work methods. For certain types of jobs or specific industry domains, this WFH system can be quite reliable. Workers can perform their tasks from anywhere and at any time. Also, monitoring systems enable employees to be remotely tracked by the company. However, the question remains: is an employee's work environment suitable enough to ensure the successful implementation of WFH? To answer this, a standardized model is needed to assess the level of eligibility for WFH implementation. This study aims to develop a fuzzy-based decision model to evaluate the eligibility of an employee's work environment for implementing WFH. An object-oriented approach is used to describe the design of the decision model, while fuzzy logic serves as the primary method for building the model. The model successfully simulated 24 raw employee data points, yielding a highest eligibility score of 88.61 with a similarity index of 0.78 when compared to the model proposed in previous research. © 2024 IEEE.

关键词： Personnel

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Study of runway crosswind and tailwind potential for airport sustainability: A study of Soekarno Hatta airport, Cengkareng, Indonesia 2

Study of runway crosswind and tailwind potential for airport...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Sardjono, W. Zulkarnain Kusnoputranto, H. Soesilo, T.E.B. Utama, D.N. Sudirwan, J. Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management BINA NUSANTARA University Jakarta11480 Indonesia Post Graduate Program School of Environmental Science University of Indonesia Jakarta Indonesia Faculty of Public Health University of Infonesia Jakarta Indonesia Post Graduate Program School of Environmental Science University of Indonesia Jakarta Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Department BINUS Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia

Airport runway designs must consider wind climatology to reduce the potential for crosswind and tailwind events that can cause accidents and aircraft delays. The large potential of crosswind and tailwind on a runway at the airport is very detrimental to aircraft passengers and airlines. Therefore, it is necessary to know the potential of crosswind and tailwind on the runway of Soekarno Hatta Airport and find the right parameters for the approximate direction and speed of the wind on the airport runway. Analysis was carried out using the direction and wind speed data from the Soekarno Hatta meteorological station in 2007-2017 to determine the potential for crosswind and tailwind on the runway of Soekarno Hatta airport and run a Weather Research and Forecasting (WRF) weather model to determine the parameterization of the right weather model for wind forecast at Soekarno Hatta airport. The highest maximum crosswind component in the 2007-2017 period occurred in August 30.53 knots, while the highest maximum tailwind of 25 knots occurred in January for a 250-degree runway and 24 knots occurred in August for a 070-degree runway. The potential for crosswind and tailwind is different every month. The scheme produces forecasts of wind direction and speed with a strong level of correlation with observations of wind direction and speed from the Soekarno Hatta meteorological station, the correlation value is 0.61. The most significant social impact of plane delays is time loss for passengers. While the economic impact on one of the crosswind events at Soekarno Hatta airport was an economic loss for one airline of US $ 43,392. © Published under licence by IOP Publishing Ltd.

关键词： Losses

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BERT and ULMFiT Ensemble for Personality Prediction from Indonesian Social Media Text

BERT and ULMFiT Ensemble for Personality Prediction from Ind...

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2022 International Symposium on Information Technology and Digital Innovation, ISITDI 2022

作者： Halimawan, Noptovius Suhartono, Derwin Gema, Aryo Pradipta Yunanda, Rezki Binus Graduate Program-Master Of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School Of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Epigene Labs Paris France

ISBN: (数字)9781665461160

ISBN: (纸本)9781665461160

Predicting personality is a growing topic in the field of natural language processing. The study of personality prediction has been proven to benefit the development of recommender systems and automated personality assessments by previous studies. Additionally, the widespread usage of social media in Indonesia such as Twitter has served as a potential source of data for developing such models. Existing personality prediction models has explored the implementation of both traditional machine learning models and deep learning models, with the latter proven to perform better with more data. Despite so, there is not much development of deep learning-based personality prediction models in the domain of Indonesian text. We propose a deep learning-based ensemble model based on Universal Language Model Fine Tuning (ULMFiT) and Bidirectional Encoder Representations from Transformers (BERT), the state-of-the-art pre-trained models for text classification. The model is trained with Indonesian tweets labelled with Five Factor Model (FFM) personality traits along with the users' metadata. The model is tested with five predetermined scenarios to prove the robustness of its performance. The final evaluation of the model proves that it has successfully achieved optimal performance of 76.41 % macro-average accuracy and 75.55% macro-average F-1 score, exceeding the state-of-the-art BERT and ULMFiT © 2022 IEEE.

关键词： Classification (of information)

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Development of Sundanese Stemmer Based on Morphophonemics 10

Development of Sundanese Stemmer Based on Morphophonemics

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Maesya, Aries Arifin, Yulyani Zahra, Amalia Budiharto, Widodo BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Binus Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

The Stemming Process is used to get the base word by removing the word affixes. The purpose of this study is to improve the algorithm in previous research by incorporating the Stemming Process into a morphophonemic approach for language processing based on rules (Rule Based) which is named AM Sundanese Stemmer (AMS). The AM Sundanese stemmer algorithm will utilize morpheme information to perform stemming on Sundanese words and analyze the phonemes in a word to identify the morphemes therein contained. The experimental process was carried out with 11,885 existing data and a sample of 546 words were taken to be stemmed. The test was carried out by comparing the algorithms of Nazief and Adriani, UG 18 and AMS. The test results showed that the AMS Algorithm was better than the Nazief Andriani Algorithm and the UG18 Algorithm with a high F-1 score of 87.52% and the comparison of the execution time of the AMS Stemmer Algorithm was 0.041403 seconds faster than the Nazief Andriani and UG 18 Algorithms. © 2023 IEEE.

关键词： Rule Based Stemming Sundanese Stemmer

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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...

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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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The Effect of Management Accounting Information Systems and Decision Making on Managerial Performance 5

The Effect of Management Accounting Information Systems and ...

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5th International Conference on Software and e-Business, ICSEB 2021

作者： Meiryani, Meiryani Gilberta, Gisela Arshanty, Risky Lindawati, A.S.L. Liawatimena, Suryadiputra Department of Accounting Faculty of Economics and Communication Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781450385831

This purpose of this study is find empirically The effect of management accounting information systems and decision making on managerial performance. The research method used is quantitative research methods with primary data obtained from questionnaire data which is measured using a likert scale. The result found that management accounting information system and decision making influence managerial performance. The results showed that the smaller the management accounting information system and decision making owned by the company, the smaller the percentage of managerial performance carried out in the company. The higher the management accounting information system, the higher the company's managerial performance. © 2021 ACM.

关键词： Decision making

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Leveraging BERT with Extractive Summarization for Depression Detection on Social Media 23

Leveraging BERT with Extractive Summarization for Depression...

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23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022

作者： William, David Achmad, Said Suhartono, Derwin Gema, Aryo Pradipta Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Epigene Labs United States

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

In our current time, the well-being of a person is not only determined by the physical health, but also by their mental health. A lot of focus and effort have been spent into raising the awareness of this issue. One such effort comes from the field of computer science by utilizing data from social media to provide additional information in detecting these mental disorders. In this research, authors proposed the Bidirectional Encoder Representations from Transformers (BERT) with extractive summarization to preprocess data obtained from popular social media platform such as Reddit and Twitter. The proposed method evaluated by compare it to several other methods in text classification to Figure out which method is best classifying whether someone is depressed or not. This research also contributes to evaluate whether applying extractive summarization to a document prior to feeding the data into BERT will contribute to an increase in performance when compared to the current method of data preprocessing, by measuring the accuracy, precision, recall and F1-score of each model. The evaluation result found that our proposed method by applying extractive summarization to reduce the sequence length achieved a better result compared to BERT base model. © 2022 IEEE.

关键词： Text processing

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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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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Riyanto, Aditya Zahra, Amalia Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331508579

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. © 2024 IEEE.

关键词： Tweets

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The Model of Personalized Machine Learning to Enhance Students’ Achievements 6th

The Model of Personalized Machine Learning to Enhance Studen...

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6th International Conference on Intelligent Sustainable Systems, ICISS 2023

作者： Rusdi, Mikail Rifqi Enrico, T. S. Gregorio Salomo, Austin Ordell Gaol, Ford Lumban Matsuo, Tokuro Bina Nusantara University Undergraduate Program-Computer Science Jakarta Indonesia Computer Science Department Binus University BINUS Graduate Program – Doctor of Computer Science Jakarta11480 Indonesia Advanced Institute of Industry Technology Tokyo Japan

ISBN: (纸本)9789819917259

Global technological improvements are accelerating and influencing numerous industries, including education. Using machine learning, technological innovations can be utilized in the education industry. Machine learning is a subset of artificial intelligence (AI) that enables computers or teaching machines to make intel-ligent decisions by learning from all past data. The framework for machine learning involves acquiring and retaining a large quantity of data and translating it into orga-nized knowledge. AI advancements enable educators to acquire a deeper knowledge of their students’ learning progression. Using Personalized Machine Learning can increase the efficiency of student learning by analyzing their performance in real time and modifying teaching techniques and the curriculum based on this information. It helps to have a tailored approach that adapts to the individual in order to provide a better education. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Machine learning

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