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Classifying Tweet Sentiment Analysis on Airlines using Random Forest Classifier, Naïve Bayes Classifier and K-Nearest Neighbors Classifier Algorithm

Classifying Tweet Sentiment Analysis on Airlines using Rando...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Ivan Alexander Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Departement Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

Opinion has always affected businesses and individuals especially from the Public. People react through social media and spread it incompletely. The situation was then accepted as public opinion. There are three categories of opinion i.e., positive, negative, and neutral. To investigate public opinion on a particular issue of interest, sentiment analysis might be employed. By offering a solution to the text classification challenge, this experiment focuses on the perception of airline tweets among the American public. We offer a method to classify tweets as positive, negative, or neutral using three machine learning classifiers: Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbors Classifier. We examined the performance of three machine learning classifiers in terms of accuracy, average precision, average recall, and average f-1 score. The results demonstrate that the Random Forest Classifier, which produces 82% accuracy, 80% average precision, 82% average recall, and 80% average f1-score, performs the best among the machine learning classifiers.

关键词： Training Sentiment analysis Accuracy Social networking (online) Text categorization Nearest neighbor methods Naive Bayes methods Random forests Robots Airline industry

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An Interactive Sign Language Based Mobile Application for Deaf People

An Interactive Sign Language Based Mobile Application for De...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Vionna Setiawan Raynaldo Thendyliputra Sherly Angelina Santami Daniel Hansen Harco Leslie Hendric Spits Warnars Arief Ramdhan Antoine Doucet Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Telkom University Bandung Indonesia Laboratoire L3i Universite de La Rochelle France

Disability is a complex problem affecting many lives in Indonesia, especially people with hearing impairments. Disability is also a complex phenomenon, starting from the type of disability, degree of disability, and age of persons with disabilities to interactions and environmental impacts on disability. Moreover, there are many testimonies from around the world that people with disabilities are more likely to be weak and less likely to get an education. This paper aims to present a mobile application that offers sign language images and videos of any text in a learning environment that is shaped like a dictionary module to increase their vocabulary, learn a new sign language, and pronounce a word. This mobile application is specially designed for deaf people who have limitations in learning and educational training. This mobile application is equipped with several features such as user registration for the deaf and normal people who like to learn sign language, discussing through forums, using a sign language dictionary containing sign language text and pictures, using interactive learning videos, registering for an event activity, including giving input, and also make a donation.

关键词： Training Vocabulary Dictionaries Gesture recognition Auditory system Assistive technologies Market research

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Stock Price Prediction Using LSTM and Search Economics Optimization

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2020年第4期47卷 1-7页

作者： Girsang, Abba Suganda Lioexander, Fernando Tanjung, Daniel Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Long Short-Term Memory (LSTM) has been one of the most popular methods in time-series forecasting. Within the LSTM architecture, there are hyperparameters present that need to be optimized in order to achieve the optimum results. In order to optimize these hyperparameters, a metaheuristic optimization method was used. A metaheuristic algorithm was used as a way to reduce both time and computational complexity. We used the Search Economics algorithm because we found the algorithm quite interesting while being unpopular. The evaluation was carried out by using Root Mean Square Error (RMSE) as the primary metric used for the optimization. The dataset being used in this experiment is the stock price of one of the most well-known financial institutes in Indonesia. The SE-Optimized LSTM was able to create a prediction that did not overfit with RMSE of 538.92, which was great compared to the unoptimized LSTM model with RMSE of 661.041 and ARIMA model with RMSE of 2809.015. © 2020. All Rights Reserved.

关键词： Long short-term memory

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AMSunda: A Novel Dataset for Sundanese Information Retrieval

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Data in Brief 2025年

作者： Aries Maesya Yulyani Arifin Amalia Zahra Widodo Budiharto Computer Science Department BINUS Graduate Program—Doctor of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Information Retrieval is crucial in many areas, including Search Engines, Information Systems, and Databases. As an indigenous language, the Sundanese corpus from West Java in Indonesia suffers from limited data availability, especially for Information Retrieval tasks. Previous efforts to build the Sundanese dataset mainly focused on text classification and generation, leaving information retrieval tasks underexplored. To address this gap, we named the AMSunda dataset. The AMSunda dataset was introduced as the first resource designed explicitly for fine-tuning and evaluating embedding models in the Sundanese language. AMSunda dataset consists of two dataset types: (1) triplet data containing a query passage, a positive, and a negative response aimed for fine-tuning embedding models, and (2) BEIR-compatible data structured for evaluating embedding models on retrieval tasks. The dataset consists of 1499 documents generated using GPT-4o-mini LLM, resulting in 7492 triplet passages and 7491 BEIR-format queries. This dataset enables further development of Sundanese-focused models in Information Retrieval.

关键词： Sundanese language Sundanese dataset Information retrieval Text embedding Natural language processing

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Contribution of Virtual Reality Technology to Increase Student Interest in Vocational High Schools

Contribution of Virtual Reality Technology to Increase Stude...

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2021 International Seminar on Intelligent Technology and Its Application, ISITIA 2021

作者： Maulana, Fairuz Iqbal Pangestu, Gusti Sano, Albert Verasius Dian Purnomo, Agung Rahmadika, Sandi Widartha, Vandha Pradwiyasma Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia Graduate School Pknu Pukyong National University Interdisciplinary Program of Information Security Busan Korea Republic of Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia

ISBN: (纸本)9781665428477

This research investigates the feasibility of school-based learning using Virtual Reality technology in Electric Power Generation learning during the COVID-19 pandemic. This research employs a qualitative approach. Vocational High School Nurul Jadid Paiton Probolinggo is the site of this study. Data was gathered through observation, literature review, and interviews. In the Electrical Engineering section, interviews were conducted with a teaching instructor. Triangulation was used to investigate the data's authenticity, in addition to further interviews with the curriculum staff and instructors at the school. The teacher's assessment of learning media using Virtual Reality application technology shows valid criteria with an average value of 3.6. In comparison, after using the Virtual Reality application learning media, the assessment of students obtained an average value of 3.77 compared to conventional assessments with an interval of 2.71. The use of Virtual Reality technology has proven that it can help and facilitate student learning. The use of technologies of Virtual Reality as a means of learning in classrooms will also be an alternate option for virtual laboratories during the COVID-19 pandemic. © 2021 IEEE.

关键词： Virtual reality

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A systematic literature review of internet of things cybersecurity

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AIP Conference Proceedings 2023年第1期2594卷

作者： B. L. Lawu Filbert K. Asih Suharjito J. Ohliati Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480

As the latest technology, IoT is growing rapidly in the communications environment, enabling it to communicate with other devices such as sensors and radio frequency identification (RFID) devices. This research aimed to provide an overview of cybersecurity that will form the basis for future technological developments. This research applied a systematic mapping study as the methodological approach. Based on the chosen search strategy, 29 articles were selected for a closer examination. This research reveals cybersecurity attacks on the IoT, particularly RFID and WSN technologies, which are the basic technologies for the development of the IoT. It is found that RFID requires access control to prevent attackers from misusing information and that key management must be compatible with WSN technology to predict attackers.

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Hidayaturrahman Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Computer Science Bina Nusatara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies.

关键词： computer science Adaptation models Neural networks Feature extraction Data models Data mining Information technology

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Drones computer Vision using Deep Learning to Support Fishing Management in Indonesia 3

Drones Computer Vision using Deep Learning to Support Fishin...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Liawatimena, Suryadiputra Atmadja, Wiedjaja Abbas, Bahtiar Saleh Trisetyarso, Agung Wibowo, Antoni Barlian, Erland Hardanto, Lilik Tri Triany, Natassya Afdalena Faisal Sulistiawan, Jaka Yojana, Albert Cahya Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School - Doctor of Research in Management Bina Nusantara University Jakarta11480 Indonesia

It is necessary to study technical factors such as bait used, oceanographic conditions of fishing areas and skipjack tuna trade patterns (Katsuwonus pelamis) as well as other factors in Sulawesi Fisheries. Supporting data to study these technical factors need to be obtained so that it can be improved throughout the year as a skipjack fishing standard with weights taken by the skipjack weights. Therefore, drones and underwater drones with cameras with high-resolution video and images to be fed and processed by computer vision. These drones are operated via Huhate ships which are then used to test efficient technical factors as expected, can be used in determining the size, shape, and colour as well as the analysis of skipjack fish that are in the Fish Aggregating Devices (FADs or Rumpon) in order to manage the management of fisheries managed in Indonesia. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Drones

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Application internet of things in healthcare Covid-19: A systematic review

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AIP Conference Proceedings 2023年第1期2594卷

作者： H. Citrawati G. P. L. Barnabas T. A. Santoso Suharjito Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480

Coronavirus, or Covid-19, is a new virus first discovered in Wuhan, China, at the end of 2019. This virus attacks the human respiratory area, which can cause respiratory problems and even loss of life. Covid-19 virus transmission is also high-speed and very easy; it is transmitted through airborne and human droplets. Therefore, to reduce the transmission rate of this virus, several researchers revealed that social distancing is one way to suppress the transmission of the Covid-19 virus. In the era of technology, The Internet of Things (IoT) is one of the systems that can help to solve this problem. The Internet of Things (IoT) cancan use one or more devices connected to the network, and it can exchange data and information effectively and efficiently. In this study, the researchers review several IoT technologies that can be used to detect COVID-19 symptoms and implement social distancing rules based on the latest literature. The review results show that IoT technology has been used to monitor the progress of COVID-19 patients in real-time with various sensors connected to geographic positioning systems, mobile devices, and cloud computing. Several research proposals for the development of IoT technology have been identified, such as the detection of COVID-19 patients in crowds, prediction of the spread of the virus in an area, and implementation of health protocols in various crowded locations.

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A Review of Implementation and Obstacles in Predictive Machine Learning Model at Educational Institutions 2

A Review of Implementation and Obstacles in Predictive Machi...

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2nd International Conference on computer, science, Engineering, and Technology, ICComSET 2019

作者： Ranggadara, Indra Sari, Yunita Sartika Dwiasnati, Saruni Prihandi, Ifan Sfenrianto Computer Science Department Mercu Buana University Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

Machine learning is a system that can learn by itself by using training data and testing data testing. In a variety of machine learning research has various obstacles in implementation especially in education institution, in its application, many things need to considered for more information to enrich research in the area of machine learning by way of comparing various obstacles in implementation in similar research. So that in this study is to find similarities and anticipations made in the initial stages of pre-processing to visualisation, as well as the contribution that can give in this study explains the best alternative for researching following the experience of several researchers in conducting machine learning research. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Machine learning

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