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1st International Conference on Smart Technology, Applied Informatics, and engineering, APICS 2022

作者： Indriani, Nyimas Muthia Arif, Azeli Carissa Arin, Ikrar Adinata School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia School of Information Systems Bina Nusantara University Computer Science Department Binus Graduate Program Computer Science Information System Department Jakarta Indonesia

ISBN: (数字)9781665489850

ISBN: (纸本)9781665489850

The advancement and application of information technology have provided significant benefits to society. Complete and quality information is needed to achieve efficiency and make it easier for people to find the right and trusted vacation partner. One way that can be done is to create an information system to find vacation companions or travel companions to travel to the same destination at the same time. This study aims to design and build a mobile application. Travemate is a mobile application that helps travelers find the right and trustworthy friends or partners to travel to the same destination. The development of this information system is expected to help the community, especially solo travelers, reduce the time spent looking for the right partner or traveler who meets their criteria. This application is built using a mobile apps platform that can be installed on devices connected to the internet via smartphones and also with the features available on travemate to facilitate and optimize user activities in finding partners for their travels so that with this application, all activities will be easy, fun, and efficient. In the long term, the application programs we create can touch people all over the world, making it easier for people to find friends to travel with. © 2022 IEEE.

关键词： Information systems

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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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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Alexander, Ivan Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

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. © 2023 IEEE.

关键词： Tweets

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Enhancing Efficiency in Yolov8n for Real-Time Detection of Oil Palm Fruits in Plantations Using Mobileone and Cross-Fusion

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SSRN 2024年

作者： Hugo, Gregory Mushlik, Muhammad Suharjito Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia

Accurate detection of fresh fruit bunches (FFB) ripeness in complex plantation environments is challenging due to occlusion, varying lighting, intricate visual features, and limited computational resources of mobile devices. To address this, a resource-efficient, real-time detection system was developed using an optimized YOLOv8n model. The model integrates MobileOne modules and Cross-Fusion blocks to enhance efficiency without compromising accuracy. Specifically, Conv and C2f modules were replaced with MobileOne modules, reducing parameters by 85% and 81%, respectively. Cross-Fusion improved feature fusion, and its combination with MobileOne (CF-MobileOne) achieved a 51% reduction in parameters while maintaining performance. The dataset comprised images from videos captured in plantations, annotated into five maturity stages: flower, unripe, underripe, ripe, and abnormal, and underwent augmentation with class balancing through manual undersampling. The model outperformed baseline YOLOv8 variants and heavier models like YOLOv9c and Faster R-CNN, achieving an mAP50 of 0.795 and an inference speed of 140.57 FPS. Hyperparameter tuning with a batch size of 64, SGD optimizer, and a learning rate of 0.01 yielded optimal results. Despite challenges in detecting certain classes due to visual similarities and environmental variations, the system effectively enhances productivity and efficiency in precision agriculture for the palm oil industry. © 2024, The Authors. All rights reserved.

关键词： Fruits

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Revalidating the Encoder-Decoder Depths and Activation Function to Find Optimum Vanilla Transformer Model

Revalidating the Encoder-Decoder Depths and Activation Funct...

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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota Bina Nusantara University Graduate Program- Computer Science Computer Science Department BINUS Jakarta11480 Indonesia Bina Nusantara University Online Learning Computer Science Department BINUS Jakarta11480 Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

The transformer model has become a state-of-the-art model in Natural Language Processing. The initial transformer model, known as the vanilla transformer model, is designed to improve some prominent models in sequence modeling and transduction problems such as language modeling and machine translation. The initial transformer model has 6 stacks of identical encoder-decoder layers with an attention mechanism whose aim is to push limitations of common recurrent language models and encoder-decoder architectures. Its outstanding performance has inspired many researchers to extend the architecture to improve its performance and computation efficiency. Despite many extensions to the vanilla transformer, there is no clear explanation of the encoder-decoder set out depth in the vanilla transformer model. This paper presents exploration results on the effect of combination encoder-decoder layer depth and activation function in the feed-forward layer of the vanilla transformer model on its performance. The model is tested to address a downstream task: text translation from Bahasa Indonesia to the Sundanese language. Although the value difference is not significantly large, the empirical results show that the combination of depth = 2 with Sigmoid, Tanh, and ReLU activation function;and d = 6 with ReLU activation shows the highest average training accuracy. Interestingly, d = 6 and ReLU show the lowest average training and validation loss. However, statistically, there is no significant difference between depth and activation functions. © 2023 IEEE.

关键词： Neural machine translation

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An Interactive Sign Language Based Mobile Application for Deaf People 7

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

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Setiawan, Vionna Thendyliputra, Raynaldo Santami, Sherly Angelina Hansen, Daniel Warnars, Harco Leslie Hendric Spits Ramdhan, Arief Doucet, Antoine School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Telkom University Bandung40257 Indonesia Universite de la Rochelle Laboratoire L3i France

ISBN: (纸本)9798350397284

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. © 2023 IEEE.

关键词： Environmental impact

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DARTS-Drone and Artificial Intelligence Reconsolidated Technological Solution for Increasing the Oil and Gas Pipeline Resilience

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International Journal of Disaster Risk science 2022年第5期13卷 810-821页

作者： Premkumar Ravishankar Seokyon Hwang Jing Zhang Ibrahim X.Khalilullah Berna Eren-Tokgoz Department of Industrial and Systems Engineering Lamar UniversityBeaumontTX 77710USA Construction Management Program Lamar UniversityBeaumontTX 77710USA Department of Computer Science Lamar UniversityBeaumontTX 77710USA

The need for safe operation and effective maintenance of pipelines grows as oil and gas demand ***,it is increasingly imperative to monitor and inspect the pipeline system,detect causes contributing to developing pipeline damage,and perform preventive maintenance in a timely ***,pipeline inspection is performed at pre-determined intervals of several months,which is not sufficiently robust in terms of *** research proposes a drone and artificial intelligence reconsolidated technological solution(DARTS) by integrating drone technology and deep learning *** solution is aimed to detect the targeted potential root problems-pipes out of alignment and deterioration of pipe support system-that can cause critical pipeline failures and predict the progress of the detected problems by collecting and analyzing image data *** test results show that DARTS can be effectively used to support decision making for preventive pipeline maintenance to increase pipeline system s afety and resilience.

关键词： Artifcial intelligence Asset management Drone application Midstream industry Pipeline resilience Predictive maintenance

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

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

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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Indonesian-Sundanese Language Machine Translation using Bidirectional Long Short-term Memory Model

Indonesian-Sundanese Language Machine Translation using Bidi...

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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program - Doctor of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia BINUS Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Bina Nusantara University Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of two models: the Long Short-term Memory and the Bidirectional Long Short-term Memory models as machine language translation models involving Bahasa Indonesia and the Sundanese language. The empiric results showed that the Bidirectional Long Short-term Memory model achieves higher performance as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.95 and 0.95 average training accuracy respectively;and 0.90 and 0.89 average testing BLEU scores respectively) than the Long Short-term Memory model as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.93 and 0.92 average training accuracy respectively;and 0.91 and 0.88 average testing BLEU scores). These results validate some previously reported studies that claim the Bidirectional Long Short-term Memory model potentially outperform the Long Short-term Memory model when it is used to process a sequence dataset. © 2023 IEEE.

关键词： Computational linguistics

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Involution symmetry quantification using recurrences

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Physical Review E 2024年第2期110卷 024203-024203页

作者： Gabriel Marghoti Thiago de Lima Prado Sergio Roberto Lopes Yoshito Hirata Physics Department Degree Program in Computer Science Interdisciplinary Center for Science Technology and Innovation CICTI Departament of Physics - Telegraphenberg 14473 Potsdam Germany Institute of Systems and Information Engineering

Symmetries are ubiquitous in science, aiding theoretical comprehension by discerning patterns in mathematical models and natural phenomena. This work introduces a method for assessing the extent of symmetry within a time series. We explore both microscopic and macroscopic features extracted from a recurrence plot. By analyzing the statistics of small recurrence matrices, our approach delves into microscale dynamics, facilitating the identification of symmetric time series segments through diagonal macroscale structures on a recurrence plot. We validate our approach by successfully quantifying involution symmetries for three-dimensional dynamical models, specifically, order-2 rotational symmetry in the Lorenz '63 model, and inversion symmetry in the Chua circuit. Our quantifier also detects symmetry breaking in the modified Lorenz model for El Niño phenomenon. The method can be applied in a versatile manner, not only to three-dimensional trajectories but also to univariate time series. Symmetry quantification in time series is promising for enhancing dynamical system modeling and profiling.

关键词： Chaos Dynamical systems Time series analysis

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Folding angle and wing flexibility influence the flight performance of origami winged fruits

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Nonlinear Dynamics 2024年第13期112卷 10995-11011页

作者： Cai, Jing-Fang Hsu, Ya-Chun Yen, Yu-Chen Lee, Yun-Chung Peyton, Bernie Huang, Mei-Jiau Yu, Hon-Tsen Juang, Jia-Yang Department of Mechanical Engineering National Taiwan University Taipei106319 Taiwan Department of Life Science National Taiwan University Taipei106319 Taiwan Berkeley CA United States Program in Nanoengineering and Nanoscience Graduate School of Advanced Technology National Taiwan University Taipei106319 Taiwan Degree Program of Genome and Systems Biology National Taiwan University Taipei106319 Taiwan

Winged fruits possess a unique flight mechanism relying on simple geometric structures rather than neuromuscular control. This study proposes an innovative approach using origami techniques to create three-winged model fruits, serving as a proxy for understanding the flight dynamics of natural winged fruits. Paper is employed to simulate inherent wing flexibility, with the option to add plastic frames for rigidity. We comprehensively investigate the free fall motion of both rigid and flexible winged fruits through experimental and numerical analyses. Velocity–time curves reveal a three-stage descent pattern—acceleration, deceleration, and steady states. The overshoot phenomenon is attributed to rapid lift increase due to leading-edge vortices attaching stably to the wings. This insight sheds light on aerodynamics governing fruit flight. Compared with rigid wings, flexible wings exhibit two key properties—slower descent and higher self-orienting capability—that facilitate a more stable and longer-distance dispersal of seeds under crosswind conditions. Our study demonstrates the potential benefits of flexible wings in natural seed distribution. This study advances our understanding of winged fruit flight dynamics, utilizing origami as a powerful tool for biomimetic investigations. The findings have broad implications, from improving aerodynamic designs to developing efficient micro air vehicles and electronic microfliers, and understanding seed dispersal evolution. Graphical abstract: (Figure presented.). © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Fruits

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