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

作者： Lie, Yulius Perbangsa, Anzaludin Samsinga Pardamean, Bens Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The background of this research is the condition of the covid-19 pandemic which has an impact on online ticket sales. Meanwhile, when future of covid-19 pandemic is starting to become clearer, we are going to have a lot more infections but hopefully a lot fewer hospitalizations, people began to travel long distances that had been delayed by implementing health protocols. Airfare competition can influence the decision to buy airline tickets. Various things have been done to attract the attention of buyers. This research is a non-experimental quantitative research. Population in this research was people who have purchased plane tickets online during the covid-19 pandemic, and the sample were 113 respondents age between 15 and 64. Sampling technique used was simple random sampling. Analysis of the data used in this study using multicollinearity, heteroscedasticity, multiple linear, T-test, F-test, and coefficient of determination test. It was concluded that price, promotion, and lifestyle variables can influence online purchasing decisions. © 2023 IEEE.

关键词： Sales

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Performance Variations in Digital Talent: Gender-Based Insights from STEM and AI Education Backgrounds 4

Performance Variations in Digital Talent: Gender-Based Insig...

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

作者： Halim, Erwin Sari, Azani Cempaka Wairooy, Irma Kartika Ario, Muhamad Keenan Achmad, Said Fitrianah, Devi School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

In the digital era, digital talent development has become increasingly vital for organizations and economies to thrive. While much focus has been placed on technical skills, this report emphasizes the often-overlooked role of soft skills in developing digital talent. Through an extensive literature review and data analysis, the study identifies that soft skills are crucial in enhancing digital talent performance. This research explores the factors that influence Digital Talent Performance in Indonesia, specifically comparing the performance levels of male and female digital talents. Data were collected from college students in the Jabodetabek area (cities surrounding Jakarta, Indonesia) using a purposive sampling method, with an online questionnaire distributed to 378 respondents. The study utilized Structural Equation Modeling (SEM) to analyze the collected data using SmartPLS 4.1. software. Key variables examined include Communication Skills, Teamwork, Emotional Intelligence, Academic Achievement, Problem-Solving, Digital Readiness, Digital Skills, Digital Technology, and Digital Talent Performance. Out of the nine hypotheses tested, one was not statistically significant. The results suggest that a combination of both technical and soft skills significantly affects digital talent performance, with important gender-based differences in performance levels emerging from the analysis. This research contributes to understanding Indonesia's complex dynamics of digital talent development. © 2024 IEEE.

关键词： digital skill digital talent soft skills STEM AI talent performance

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White rice stem borer pest detection system using image-based convolution neural network 9th

White rice stem borer pest detection system using image-base...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Saufi, Akhmad Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Preventing agricultural resource loss caused by pests remains a crucial issue. While technological advancements are being achieved, the current agricultural management methods and equipment have yet to meet the required level for precise pest control, a huge portion of the pest population analysis process is still conducted manually. As a solution to this issue, the development of a White Rice Stem Borer pest detection system has been conducted by applying Convolutional Neural Network (CNN) technology to calculate the pest population count at the research location. This system has been specifically designed to detect the White Rice Stem Borer using available traps. The method involves training data from a direct dataset obtained from the field, categorized into two positive and negative classes of the White Stem Borer pests. Six models have been trained from this dataset, utilizing two different architectures. Out of the six trained models, four showed potential overfitting, one exhibited underfitting, and one model demonstrated optimal results. The highest accuracy in image detection achieved by the most optimal CNN model was 97.35%, with a training accuracy of 98.54%. This best-performing model utilized an architecture with three Convolution layers, 50 Epochs, and an automatic data split with an 80:20 training-validation data ratio. From the research findings, it is concluded that this study can assist in automatically analyzing the quantity of White Stem Borer pests in a specific area without directly counting the number of pests from existing traps. However, the study still encounters a limitation-the detection process still requires substantial server resources and cannot be directly processed on the Raspberry PI device installed in the trap. Consequently, the detection relies on transmitting image data from the field device to the server before the detection process can occur. © 2024 The Authors.

关键词： Convolutional neural networks

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Confidentiality. Integrity, and Availability (CIA) Approach for Private Dataset Management System Design 9

Confidentiality. Integrity, and Availability (CIA) Approach ...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Ariansyah, Dedy Rahutomo, Reza Pardamean, Bens School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Dataset management systems are essential for assisting research and development (R&D) organizations in applying data governance protocols, especially in managing the utilization of datasets. In R&D, datasets are vital sources for producing analysis, machine learning prediction, and supporting decision-making. As such, it is important to deploy dataset management systems with considerations regarding the aspects of Confidentiality, Integrity, and Availability (CIA) which is a design principle that emphasized information protection and collaboration facilitation among dataset users. The study proposed an analysis and a design for a dataset management system that takes CIA aspects into account. In the future, the proposed design will be utilized as a benchmark for building a dataset management system. In this study, the Waterfall method was adopted. A use case diagram, an ERD (Entity Relationship Diagram), and an activity diagram were constructed in the design stage. In the project implementation stage, the Laravel framework including Laravel Jetstream was considered to be used. © 2024 IEEE.

关键词： Data integrity

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Gender Classification on Twitter Based on Feeds and User Descriptions Using Xlnet-Fasttext

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第20期48卷 71-80页

作者： Alandeta, Vito Rozaan Suhartono, Derwin Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

This study proposes a gender classification method for Twitter data using a hybrid XLNet-fastText model. The objective is to enhance gender classification accuracy by leveraging the contextual understanding of XLNet and the semantic richness of fastText embeddings. Computational experiments were conducted on a dataset derived from Kaggle, focusing on user account descriptions and Twitter feeds. The proposed method achieved an accuracy of 71.4%, precision of 77%, recall of 60.9%, and F1-score of 68% for gender classification based on Twitter feeds. For user account descriptions, the scores were 72.4% for accuracy, 75.1% for precision, 63.24% for recall, and 68% for F1-score. These results outperform the baseline XLNet model and demonstrate the potential of the XLNet-fastText combination in improving text-based classification tasks. Our approach highlights a viable pathway to enhance gender classification on social media platforms, suggesting further improvements through multi-modal data integration. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Tweets

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Spatial Temporal Changes in Mangrove Forests on Tarakan Island, North Kalimantan, Indonesia 3rd

Spatial Temporal Changes in Mangrove Forests on Tarakan Isla...

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

作者： Giofandi, Eggy Arya Sekarjati, Dhanu Arshad, Mazlan Rahman, Henzulkifli Tjahjono, Boedi Graduate Program of Regional Planning Science Faculty of Agriculture IPB University Bogor Indonesia Geoinformatics Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Graduate Program of Coastal and Ocean Resource Management Faculty of Fisheries and Marine Science IPB University Bogor Indonesia Graduate Program of Tropical Biodiversity Conservation Faculty of Forestry and Environment IPB University Bogor Indonesia Department of Soil Science and Land Resources Faculty of Agriculture IPB University Bogor Indonesia

ISBN: (纸本)9789819707393

Mangroves play an important role in providing essential ecosystem services and resources for fisheries, offering protection against climate change and coastal erosion. Understanding the changes in mangrove forests is essential for the sustainability of the coastal ecosystem. The study aims to analyze the spatiotemporal changes of mangrove forests in Tarakan Island using Sentinel-1 and Sentinel-2 images from 2018 and 2023. The analysis shows changes in land cover, with an increase in aquaculture land and a decrease in other land types. The results of the accuracy assessment show good results, with an accuracy value of 0.82 and a Kappa value of 0.78 in the mangrove areas in 2023. These values indicate that the classification of land cover in the study area was reliable and accurate. The conversion of mangrove forests into aquaculture land poses a challenge for their conservation. Therefore, preserving and conserving mangrove forests in the study area is essential for maintaining the ecological balance and supporting the livelihoods of local communities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Aquaculture

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Clustering Preachers and Mosques for Effective Religious Outreach in Kampar Using DHC-PCA Algorithm 2

Clustering Preachers and Mosques for Effective Religious Out...

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2nd IEEE International Conference on Electrical Engineering, computer and Information Technology, ICEECIT 2024

作者： Kurniawan, Rahmad Surya, Natania Adela Fatayat Mahdiyah, Evfi Husti, Ilyas Arisman Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (纸本)9798331504373

Preacher assignments to mosques in Kampar Regency have often been inefficient due to geographical distance and compatibility issues, limiting effective religious outreach. This study aims to optimize preacher assignments by applying the Divisive Hierarchical Clustering (DHC) algorithm. Analysis was conducted on a dataset of 250 records, clustering preachers and mosques based on geographical proximity and relevant attributes. Initial clustering modelling produced the highest silhouette score of 0.336. Dimensionality reduction using Principal Component Analysis (PCA) significantly improved the silhouette score to 0.8197, reflecting enhanced clustering quality. The elbow method validated eight clusters as optimal, with a value of 292.33. Research findings demonstrate that preachers can be effectively placed in geographically closer mosques aligned with their profiles, reducing inefficiencies. Business intelligence techniques visualized the clustering results, providing decision-makers with insights into geographical distribution and facilitating improved assignment strategies. These findings hold substantial implications for optimizing religious outreach in Kampar by minimizing logistical challenges and increasing engagement effectiveness. © 2024 IEEE.

关键词： Principal component analysis

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Combating Covid19 Using a Mobile App 3

Combating Covid19 Using a Mobile App

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3rd International Conference on Modeling, Simulation, Optimization and Algorithm, ICMSOA 2021

作者： Wijaya, A.H. Andhini, T.M.W. Rahmadi, Y.P. Warnars, H.L.H.S. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

Through this pandemic, the world has experienced two major crises, health crisis, and economic crisis. It would be dangerous for us to continue with our "normal"daily lives. We have been forced to stay at home, and some have been dismissed from their jobs;even street vendors are forced to close. The primary purpose of this paper is to represent our solution idea on the troubles regarding COVID-19 to help and support people by providing them all the information and services needed about the COVID-19, such as the high-risk COVID-19 area, statistical growth, safety tips, online swab test booking, etc. through a mobile-based application. In this paper, we designed a model with the use case and class diagrams and implemented using Android Studio. © Published under licence by IOP Publishing Ltd.

关键词： COVID-19

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Multi-robot Map Exploration Based on Multiple Rapidly-Exploring Randomized Trees

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SN computer science 2024年第1期5卷 31页

作者： Mukhopadhyay, Shayok Umari, Hassan Koirala, Koshish Electrical & Computer Engineering and Computer Science Department University of New Haven 300 Boston Post Rd West Haven 06516 CT United States Department of Electrical Engineering American University of Sharjah Sharjah P.O. Box 26666 United Arab Emirates Mechatronics Engineering graduate program American University of Sharjah Sharjah P.O. Box 26666 United Arab Emirates

A map is necessary for tasks such as path planning or localization, which are common to mobile robot navigation. However, a map may be unavailable if the environment in which a robot navigates is unknown. Creating a map requires an exploration algorithm. Such algorithms guide robots to boundaries that separate known portions of a map from the unknown portions. Such boundaries are known as frontiers. There are image processing-based algorithms that detect frontiers. The authors’ previous work uses two, i.e., local and global, rapidly exploring random trees (RRTs) for two-dimensional exploration using a single robot. The present work applies the above to a multiagent system with three robots. A market-based strategy is used to allocate exploration tasks to the robots. Results show that the developed exploration strategy successfully explores a map in a reasonable amount of time compared to image processing-based approaches, and also reduces the map exploration cost. This strategy is readily usable for three-dimensional exploration with drones. It is also shown that the usage of a local frontier detector along with a global frontier detector helps reduce exploration time compared to simply using a global frontier detector alone. As an outline to future work, the paper also shows an extension of the proposed approach towards three-dimensional mapping using a single robot. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Exploration Mapping Multi-agent Navigation ROS RRT

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Evaluation of an agent-based reinforcement learning framework for model-based testing 7

Evaluation of an agent-based reinforcement learning framewor...

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7th International Congress on Ambient Intelligence, Software Engineering, and e-Health and Mobile Health, AmITIC 2024

作者： Ramírez-Méndez, Jose Quesada-López, Christian University of Costa Rica Graduate Program in Computing and Informatics San Pedro Costa Rica University of Costa Rica Department of Computer Science and Informatics San Pedro Costa Rica

ISBN: (纸本)9798350364538

This study evaluates an agent-based reinforcement learning framework for model-based testing (MBT). The framework's performance was assessed on three key metrics: effectiveness and efficiency in achieving model coverage objectives, the quantity and uniqueness of generated test cases, and code coverage. The results show improved metrics for the framework compared to traditional testers in model coverage evaluation and test case metrics. Specifically, the framework achieved higher effectiveness and efficiency, generating a higher average number of test cases with a substantial proportion of unique cases, indicating more diverse and thorough testing. Additionally, the framework, along with random and greedy testers, achieved over 70% coverage across branch, method, and line code metrics. The framework showed slightly higher values in method and line coverage compared to other testers. The evaluation highlights the use of agent-based reinforcement learning to support model-based testing in planning test case generation, exploring models using multiple strategies, and learning guided by testing metrics. Future work will focus on further refining the framework learning model, including error coverage evaluation, and testing its applicability across different software systems. © 2024 IEEE.

关键词： Adversarial machine learning

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