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Enhancing Computational Thinking Based on Virtual Robot of Artificial Intelligence Modeling in the English Language Classroom 1

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Muthmainnah Obaid, Ahmad J. Al Yakin, Ahmad Brayyich, Mohammed Teacher Training and Education Faculty Civic Education Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq National University of Science and Technology Thi-Qar Iraq

ISBN: (数字)9789819965502

ISBN: (纸本)9789819965496

There has been a lack of both educational focus on computational thinking (CT) and research into effective methods of assessing CT competence. This research takes a comprehensive look at the methods used to grade CT in higher education. This study examines the use of educational robotics (ER) as a teaching strategy in higher education, with particular attention to how undergraduate students might respond differently to this kind of learning. Students collaborate using artificial intelligence to find out how to program robots (start installing, interact, sharing information, feedback, and evaluate). Of 181 undergraduates’ students from Indonesia, Saudi Arabia, Riyadh, Pakistan, the Philippines, Egypt, Algeria, Lesoto, Cameron, Bangladesh, Sudan, Uganda, Portugal, Ukraine, Pakistan, and England make up the study groups. These students are enrolled in the first semester of the 2022 academic year. In this work, researchers employed a quantitative method technique. During the exercise, the participants were closely monitored to assess their performance in relation to the core CT constructs in the robotics class. These constructs include confidence in dealing with complexity, persistence in problem-solving, tolerance for ambiguity, ability to handle open-ended problems, effective communication skills, and teamwork abilities. In order to enhance the implementation of strategic initiatives. To facilitate more strategic learning, this study found that the use of visual robotics of artificial intelligence helped to developing CT and the respondents strong believe it to increase students’ confidence in dealing with complexity, persistence in working through difficult questions, tolerance for ambiguity, ability to handle open problems, communication skills, and teamwork. It is thought that this study would be valuable to academics, curriculum designers, and teachers because students who play artificial intelligence games exhibit considerably greater cognitive flexibility than b

关键词： Students

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Rapid Monitoring for Flood Inundation Detection-Based Sentinel-1 SAR Image Data and Exposure Assessment in Urban Building Area: Makassar City, Indonesia 3rd

Rapid Monitoring for Flood Inundation Detection-Based Sentin...

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3rd International Conference on Radioscience, Equatorial Atmospheric science and Environment, INCREASE 2023

作者： Sekarjati, Dhanu Hanif, Muhammad Komala, Siffa Anastasya Giofandi, Eggy Arya Putri, Febita Safera Bunga, Brigitta Audrynne Rombe Putklang, Wasana Wilis, Ratna Geoinformatics Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Department of Geophysics and Meteorology Faculty of Mathematics and Natural Science IPB University Bogor Indonesia Graduate Program of Regional Planning Science Faculty of Agriculture IPB University Bogor Indonesia Department of Geography Faculty of Social Science Universitas Negeri Padang Padang Indonesia

ISBN: (纸本)9789819707393

Real-time monitoring remains a challenge in providing accurate information, especially in the case of urban flood inundation disasters. The accuracy of data for disaster risk reduction has a positive impact on reducing the loss of livelihood or the regional economy. Understanding flood levels and their impact on urban areas can be crucial information for flood mitigation and effective urban planning. The research aims to conduct a rapid monitoring investigation for flood inundation detection in Makassar City, Indonesia, using Sentinel-1 SAR image data and assess the exposure of urban building areas to flooding. By identifying flood inundation areas using backscatter values, it was determined that Tamalanrea District exhibits a high density and is prone to flooding as a result of urban expansion. Additionally, Biringkanaya District, which is adjacent to Tamalanrea, was also impacted by the flood. The most severely affected area was found to be Tamalanrea District, with an inundation area covering 73,114 m2. It also recorded the highest extent of built-up area affected by flooding, reaching 29,475 m2. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Floods

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Indonesian Agricultural-crops Classification Using Transfer Learning Model

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Procedia computer science 2023年 227卷 128-136页

作者： Mahmud Isnan Alam Ahmad Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Classification of Indonesian crops is a critical task in developing farming and getting more understanding of agriculture. However, there is no clear task in classifying types of crops in Indonesia. Transfer learning has been used successfully in a variety of image classification applications. Thus, in this paper, we collected images of Indonesian crops from the internet randomly and proposed a classification by using transfer learning of deep learning with four pre-trained models: EffficientNet- B0, ResNet18, VGG19, and AlexNet. In the experiment, augmentation techniques such as random horizontal flip, random vertical flip, and random affine were utilized to prevent the network from overfitting. The result found that EfficientNet-B0 outperformed other models with an accuracy of 82.55. Then, the model struggled to distinguish between crops in the same family. According to the results, although transfer learning can work well to classify images of Indonesian agricultural crops, some improvements are still required to address existing issues.

关键词： Agriculture Classification Crop Deep Learning Transfer learning

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A Bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Nirwantono, Rudi Assidqi, Khoirunnisa Hidayat, Alam Ahmad Pebrianto Trinugroho, Joko Pardamean, Bens Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Food Biotechnology Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Departement Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site. © 2023 IEEE.

关键词： Amino acids

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Count-Data Mixed Models of Topical Tweets: A Case of Indonesia Flood Events

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Procedia computer science 2023年 227卷 805-812页

作者： Alam Ahmad Hidayat Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Examining topic-level variability in modeling Twitter data can potentially yield more comprehensive insights into public perception during critical periods, thereby enhancing natural disaster mitigation and surveillance efforts. In this study, we utilized generalized linear mixed models (GLMMs) to illustrate the variability in tweet counts related to specific topics in Indonesia during the flood events that occurred in February 2021. The glmmTMB library in R was employed for this purpose. The data were assumed to follow two distinct exponential distributions: Poisson and Negative Binomial. To incorporate random effects, random intercepts and random slopes were introduced, allowing them to vary randomly across topics in the initial two models. Additionally, the final model addressed issues related to dispersion and zero-inflation. By evaluating the Akaike Information Criteria scores, we determined that a model based on the Negative Binomial distribution with random zero-inflation intercepts best fit the data. The chosen model formulation and the estimated parameters have the potential to forecast topic-specific trends in Indonesian flood-related Twitter data.

关键词： count data disaster surveillance floods mixed models Twitter

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series Data

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Procedia computer science 2023年 227卷 1062-1069页

作者： Alam Ahmad Hidayat Arif Budiarto Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters.

关键词： deep learning forecasting sleep quality time series wearable devices

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Convolutional Neural Network Approach for Sentiment Analysis of GoFood Services

Convolutional Neural Network Approach for Sentiment Analysis...

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computer sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Zaenal Abidin Adzqi Aulia Ryan Randy Suryono Permata Akmal Junaidi Wamiliana Wamiliana Doctoral Program of Mathematics and Natural Sciences Universitas Lampung Bandarlampung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia Department of Computer Science Universitas Lampung Bandarlampung Indonesia Department of Mathematics Universitas Lampung Bandarlampung Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

In the age of swift digital advancement, social media has emerged as a vital and readily available information source for users. Individuals utilize social media to communicate, articulate thoughts, and disseminate their viewpoints, both textually and visually. Twitter is a social media platform that people extensively utilize to disseminate both favorable and unfavorable information regarding GoFood services. This study intends to apply the Convolutional Neural Network (CNN) algorithm within the GoFood service on Twitter. This research also seeks to assess the accuracy of the CNN algorithm in relation to GoFood services. Employing deep learning techniques, particularly the convolutional neural network algorithm, to analyze Twitter user sentiment can enhance the efficiency, accuracy, and simplicity of classifying answers to GoFood services. The findings indicated that the Convolutional Neural Network technique was effectively employed to classify positive, negative, and neutral feelings. The optimal accuracy rate was attained through preprocessing with Stemming, yielding 51% accuracy for multiclass classification and 72% for binary class classification. The accuracy percentage for classification without stemming was 49% for multiclass classification and 70% for binary classification. This research holds possible consequences for Gojek firms and can serve as evaluative and assessment material for GoFood services, enabling them to implement necessary corrective measures.

关键词： Sentiment analysis Accuracy Social networking (online) Convolution Blogs Text categorization Feature extraction Classification algorithms Convolutional neural networks Testing

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Optimizing Insurance Strategies through Disaster Risk Assessment Models: A Case Study of Earthquakes in Japan

Optimizing Insurance Strategies through Disaster Risk Assess...

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Computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Qiyuan Luo Xingnan Zhang Yuanxin Zhang Burra Venkata Durga Kumar Meifeng Liu Program in Computer Science & Technology Xiamen University Malaysia Kuala Lumplur Malaysia Program in Software Engineering Xiamen University Malaysia Kuala Lumplur Malaysia Program in Mathematics and Applied Mathematics Xiamen University Malaysia Kuala Lumplur Malaysia

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

With the increasing frequency of extreme weather events and natural disasters, insurance providers around the world are facing enormous challenges. This study addresses insurance claims resulting from these disasters by developing a comprehensive disaster insurance risk assessment model, focusing on Japan, a country prone to frequent earthquakes. The model integrates a Catastrophe Model, the Analytic Hierarchy Process (AHP), and an expert scoring method to optimize the risk assessment and compensation process for historic buildings. The results show that the model has strong predictive power, which provides valuable insights for insurance companies, policymakers, and urban planners in mitigating the impact of disasters and protecting cultural heritage.

关键词： Analytical models Disasters Earthquakes Buildings Insurance Predictive models Risk management Cultural differences Meteorology Context modeling

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification 4

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Fitrianah, Devi Suryaningrum, Kristien Margi Sagala, Noviyanti Tri Maretta Ayumi, Vina Lim, Siew Mooi School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Mathematics Statistics Department Jakarta Indonesia Universitas Mercu Buana Informatics Engineering Program Faculty of Computer Science Jakarta Indonesia Tunku Abdul Rahman University College Faculty of Computing and Information Technology Computer Science Department Kuala Lumpur Malaysia

ISBN: (纸本)9781665473279

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%. © 2022 IEEE.

关键词： Fish

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