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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Oktavia, Tanty Roekman, Faza Fakhriansyah Putera, Muhammad Ikhsan Hidayah, Muhammad Alif Rafi, Muhammad Hosoda, Takaaki Gaol, Ford Lumban BINUS Graduate Program Information System Management Bina Nusantara University Information System Management Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan BINUS Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

Social media has now become a place for others to exchange information in the form of writing, photos, videos, and audio. Social media has become a place for humans to interact from all over the world. In the use of social media, attractive professional information is conceptualized as access to relevant information and opening up better career opportunities. User content about work and choosing personal information compilation strategies consistently predict information usefulness. This study discusses the effect of using a linked-in application on the success of its users in the professional world. It is hoped that it can provide insight into the use of this recruitment application, making it easier for users to find work. The research model used in this research is quantitative. The survey was conducted on connected users in Indonesia using an online questionnaire, and the data collected was analyzed using PLS-SEM. Of the 68 respondents who took the survey, it can be said that all research models have a positive relationship with the successful use of the linked-in platform. © 2022 IEEE.

关键词： Surveys

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Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest

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IEEE Access 2025年 13卷 84713-84726页

作者： Dias, Jean A. C. Guimarães, Pedro H. Do V. Pereira, Williane G. S. Tamasauskas, Leonardo De O. Gomes, Marivan S. Correa, Alan B. S. Figueiredo, Karla Costa, Gilson Costa, Gabriel Brito Costa, Fernando A. R. Seruffo, Marcos C. Da R. Operational Research Laboratory Belém66075-110 Brazil School of Technology Amazonas 69850-000 Brazil Belém66075-110 Brazil Institute of Mathematics and Statistics Department of Informatics and Computer Science Rio de Janeiro20550-013 Brazil Institute of Biological and Health Sciences II Department of Genetics Rio de Janeiro20550-013 Brazil Santarém68035-110 Brazil Federal University of Rio Grande do Norte Climate Sciences Post-Graduate Program Rio Grande do Norte Natal59078-970 Brazil Pará Santarém68035-110 Brazil Post-Graduate Program in Forest Science Technology and Innovation-PPGCTIF Pará Santarém68035-110 Brazil Pará Santarém68035-110 Brazil Pará Belém66075-110 Brazil Pará Castanhal68746-630 Brazil

This study presents a hybrid model, STL-ARIMA-ES-LSTM, developed to improve the accuracy of Gross Primary Productivity (GPP) forecasts in the Amazon region. The model integrates seasonal and trend decomposition using Loess (STL) with statistical methods (ARIMA and Exponential Smoothing-ES) and a machine learning technique (Long Short-Term Memory - LSTM). Applied to GPP data from the PE-QFR site, the hybrid model achieved significantly better error metrics, with RMSE of 1.69 gC/m2/day, MAE of 1.35 gC/m2/day, and MAPE of 0.20%, compared to the standalone LSTM (RMSE of 2.16 gC/m2/day, MAE of 1.78 gC/m2/day, and MAPE of 0.27%). Furthermore, the hybrid model showed stronger agreement with the observed data, with correlation coefficient r =0.62 and R2 =0.39 , whereas the LSTM alone yielded r =0.26 and R2 = –0.002. The STL decomposition allowed effective separation of trend, seasonality, and residual components, enabling tailored modeling of each, which contributed to the improved predictive performance. These results demonstrate the advantage of hybrid approaches in capturing the nonlinear and seasonal patterns of GPP, supporting enhanced environmental monitoring and more informed climate change mitigation strategies in the Amazon. © 2013 IEEE.

关键词： Carbon sequestration

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Literature Review: Analysis of Essential Metadata Attributes of Digital Goods for Transaction in The Virtual World

Literature Review: Analysis of Essential Metadata Attributes...

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International Conference on Information Management and Technology (ICIMTech)

作者： Gede Indra Raditya Martha Harjanto Prabowo Ford Lumban Gaol Richard Wiputra Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

In online-based virtual worlds such as Metaverse, Online Games, and other online digital spaces, the virtual/digital goods (digital items / digital assets) are fundamental things that must be available to be able to do business & create an economy in the virtual world through monetization & transactions from these digital goods. This research focuses on summarizing and exploring the attributes used in digital goods in the virtual world by extracting attributes in digital goods from other related studies using the Systematic Literature Review methodology & PRISMA Framework comprehensively so that it can be a reference for developers in designing the properties and functions of digital goods in the virtual world that are being developed, which in this case will be in the form of an NFT in the future. Based on research that has been carried out, 3 essential metadata attributes have the most influence on transactions, number 1 Visual Design / Aesthetic (Representative), number 2 Effect (Utilities), and number 3 Statistics / Performance (Utilities) which must be considered in developing/making digital goods in the form of NFTs in the virtual world so that the value of digital goods can attract users to buy or transact them in the virtual world.

关键词： Visualization Metaverse Buildings Games Metadata Information management Business

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DEEP LEARNING-BASED CHANNEL ESTIMATION FOR RIS-ASSISTED UAV-ENABLED WIRELESS COMMUNICATIONS SYSTEM

DEEP LEARNING-BASED CHANNEL ESTIMATION FOR RIS-ASSISTED UAV-...

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2023 International Conference on Innovation, Communication and Engineering, ICICE 2023

作者： Tesfaw, Belayneh A. Lin, Hsin-Piao Tarekegn, Getaneh B. Juang, Rong-Terng Jeng, Shiann-Shiun Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Institute of Aerospace and System Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu30010 Taiwan Department of Electrical Engineering National Dong Hwa University Hualien Taiwan

ISBN: (纸本)9781839539954

Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. This problem can be effectively addressed by employing reconfigurable intelligent surfaces (RIS). To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communications system. Simulation results showed that LSTM can effectively improve the channel estimation performance of RIS-assisted UAV-enabled wireless communications. © The Institution of Engineering & Technology 2023.

关键词： Channel estimation

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Modified SHA-512 Algorithm for Secured Password Hashing 3

Modified SHA-512 Algorithm for Secured Password Hashing

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3rd IEEE International Virtual Conference on Innovations in Power and Advanced Computing Technologies, i-PACT 2021

作者： Bemida, Philip Joseph F. Sison, Ariel M. Medina, Ruji P. Technological Institute of the Philippines Graduate Program Quezon City Philippines School of Computer Studies Emilio Aguinaldo College Manila Philippines

ISBN: (纸本)9781665426916

The secure hash algorithm (SHA) is a widely used function for password hashing. This study introduced a modified SHA-512 algorithm by modifying the message scheduling, hash construction, and compression function, and reducing the iterations, as well as examined the modified algorithm's computational efficiency according to the avalanche effect and attack resistance capacity by performing brute-force, rainbow table, dictionary, and online cracking attacks via the Cain and Abel password-cracking tool and CrackStation. The modified SHA-512's avalanche percentage reached 50.84 percent, which is 0.84 percent higher than the target of 50 percent. During experimentation, password cracking attacks failed to crack the hashes generated by the modified algorithm. In addition, the modified algorithm's runtime speed reached an average elapsed time of 3, 485 ms, while the original SHA-512 attained 5, 384 ms. Hence, the modified SHA-512 algorithm is an efficient and secure method of hashing passwords. © 2021 IEEE.

关键词： Computational efficiency

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Control and Data Integrity are Important Factors of Data Governance Technology

Control and Data Integrity are Important Factors of Data Gov...

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International Conference on ICT For Smart Society (ICISS)

作者： Yakob Utama Chandra Harjanto Prabowo Ford Lumban Gaol Betty Purwandari Information Systems Department BINUS Graduate Program Doctor of Computer Science School of Information Systems Bina Nusantara University Jakarta Indonesia Management Department Doctor of Research Management BINUS Business School Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

The proliferation of data has become an inevitable consequence of the digital age as individuals across all domains increasingly rely on data for various purposes. Both companies and governments have a significant need for data. However, the sheer volume and complexity of this data, along with the potential risks associated with mishandling it, necessitate the establishment of a framework known as data governance. The primary objective of this research paper is to examine the factors that contribute to the effective implementation and utilization of Data governance. This study employs qualitative methods and adopts a systematic literature review approach to address the research inquiry: “What are the important factors that determine the utilization of data governance technology?” Through the systematic literature review, a total of 41 key factors were identified from 20 research literatures that have been found to significantly impact the effective implementation of data governance. There are thirteen primary technological factors that hold significance in the realm of data governance. These factors encompass technology, application and use, storage of data, sharing, archiving, and preservation, big data algorithmic systems, data flow, deletion, diversity of data, mechanisms, size of data, control, and data integrity.

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Location Prediction Using Named Entity Recognition for Indonesia Natural Disasters in Data Twitter

SSRN

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SSRN 2022年

作者： Girsang, Abba Suganda Noveta, Bima Krisna Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Indonesia is in an area prone to natural disasters, there were 4,650 natural disasters that occurred in 2020. However, the community does not have spatial data regarding the location of the disasters that occurred. The purpose of this study is to provide the location of natural disasters that are poured into maps to the public by utilizing Twitter data which has a fast distribution with the fifth most users in the world. This study proposes a system for geolocation of types of natural disasters obtained from Twitter, which are classified into eight classes of natural disasters using the Support Vector Machine (SVM) method. Then the Twitter text is extracted to find the location in the Twitter text using Named Entity Recognition (NER). NER uses six special classes based on the regional level in Indonesia with an accuracy rate of 85.65%. The result of geolocation using ArcGIS maps is 87,5%. It is hoped that with this research, people will get spatial data to see the points of natural disasters that occur using maps. So, the public can anticipate future disasters, and the authorities can send aid to the location. © 2022, The Authors. All rights reserved.

关键词： Location

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Indonesian Automatic Speech Recognition with Xlsr-53

SSRN

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SSRN 2022年

作者： Arisaputra, Panji Zahra, Amalia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This study focuses on the development of Indonesian Automatic Speech Recognition (ASR) using XLSR-53 pre-trained model. The use of this XLSR-53 pre-trained model is to significantly reduce the amount of training data in non-English languages required to achieve competitive WER. The total amount of data used in this study is 26 hours: (1) TITML-IDN 14,5 hours;(2) Magic Data 3,5 hours;and (3) Common Voice 8 hours. With a WER of 22 %, the model built in this study can compete with similar models using the Common Voice dataset split test. WER can be decreased by around 10% using a language model, resulting in a WER of 12 %. Thus, the results of this study have succeeded in perfecting previous research in contributing to the creation of a better Indonesian ASR with a smaller amount of data. © 2022, The Authors. All rights reserved.

关键词： Statistical tests

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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Literature study of stunting supplementation in Indonesian utilizing text mining approach

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Procedia computer Science 2023年 216卷 722-729页

作者： Digdo Sudigyo Alam Ahmad Hidayat Rudi Nirwantono Reza Rahutomo Joko Pebrianto Trinugroho Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta 11480 Indonesia

Numerous research on stunting supplementation interventions in Indonesia have been published. The information can be extracted through data mining, especially from academic research databases. In this paper, we presented a text mining-based literature review strategy to create a pipeline that researchers can use to accelerate the development of stunting supplementation intervention research in Indonesia. Utilizing various NLP (Natural Language Processing) techniques, data were crawled, processed, and visualized using Python. The crawling dataset used a module from the Pubmed API (Application programming Interface) to collect literature papers. The NLTK (Natural Language Toolkit) module and itertools were used to process text data. The n-grams model was applied to process tokens into bigrams and trigrams. Text information was visualized using Matplotlib and Word cloud packages. There is an increasing number of publication in stunting supplementation intervention according to our result, which was observed from 2015 to 2021. West Java was the province where most of the stunting research has been conducted, as determined by research abstracts. Top occurrences obtained from the bigram and trigrams models calculation produced different terms. The word pairings that occurred the most frequently in the bigram and trigram model analyses were "child-aged" and "iron-folic-acid," respectively. The findings of this study are expected to help researchers to obtain the latest research topics related to stunting supplementation interventions in Indonesia.

关键词： text mining data mining literature study stunting supplementation Indonesian

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