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4th IEEE International Conference of computer science and Information Technology, ICOSNIKOM 2022

作者： Pratiwi, Heni Ispur Kartowisastro, Iman Herwidiana Soewito, Benfano Budiharto, Widodo Doctorate Program School of Computer Science Bina Nusantara University Jakarta Indonesia Bina Nusantara University Binus Graduate Program Doctorate Program of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Faculty of Engineering Computer Engineering Department Jakarta Indonesia Bina Nusantara University Computer Science Binus Graduate Program Computer Science Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350399073

The voice patterns of gender and age have been used as speaker's identity and implied into sectors such as smart cards, healthcare, banking, and other security access controls. However, age, illness, and ambient noise factors exist challenging issues of voice changes in speech recognition systems to be resolved. This paper presents experiment results of observing those issues, and its goal was obtaining the elements of voice features to secure the speaker's identity. Mel-frequency Cepstral Coefficient (MFCC) was used to conduct observations on spectral centroid and bandwidth, it has effectively performed capturing features of individual speech signal. The experiment was conducted with data split into three groups: under 13, range 13 to 19, and above 19 years old for both male and female. The results showed that both gender under 13 have centroid voices reached to 98%, age of 13 to 19 remain with centroid reached 98% of bandwidth for male and passing over 100% for female, then above 19 for both genders speak lower with centroid 50% of bandwidth, and male above age of 19 remains to keep centroid about 50% and female changes to 95%. MFCC fits to expose the speech spectrum for the purpose of an analysis frame on specific local spectral properties of voices, and the finding is to form a security line or an individual profiling adoption. © 2022 IEEE.

关键词： Bandwidth

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Evaluation of REAL-ESRGAN Using Different Types of Image Degradation

Evaluation of REAL-ESRGAN Using Different Types of Image Deg...

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International Conference on ICT For Smart Society (ICISS)

作者： Kus Andriadi Muhammad Zarlis Yaya Heryadi Andry Chowanda Computer Science Department Binus Graduate Program Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science BINUS University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

This study evaluates the performance of the REAL-ESRGAN [1] model on images with varying levels of degradation using the DIV2K dataset [2], such as the Wild, the Mild, the Difficult, and the x8 subsets. REAL-ESRGAN was created to solve super-resolution problems and aims to produce high-resolution images from low-resolution images. Experiments were conducted at scales of x2 and x4, and performance was measured using Full-Reference metrics (LPIPS, PSNR, SSIM) and No-Reference metrics (NIQE, MANIQA, CLIPIQA, and PI). The Results were good, especially with the x2 scale; it has higher PSNR and SSIM scores, lower LPIPS and NIQE values, and enhanced visual and perceptual quality. The model faced more significant challenges with the wild and the difficult datasets because they have more complex degradations and compression artifacts; it can be seen with unstable results of Full-Reference and No-Reference metrics. On the contrary, the Mild and x8 datasets yielded better results in both metrics; not only that, even the computational cost for Mild and x8 outperforms the rest of the dataset. This study shows the strengths and limitations of REAL-ESRGAN in handling different levels of image degradation. For future research, the model needs enhancement to tackle the degradation format of the wild and the difficult dataset. It would be good if the REAL-ESRGAN improvement could also maintain the computational cost.

关键词： Degradation Measurement Visualization Computational modeling Superresolution Computational efficiency Image reconstruction

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Developing an online information portal for enhancing society awareness of RPTRA (A case study RPTRA MAYA ASRI 13) 7

Developing an online information portal for enhancing societ...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Arifin, Yulyani Gunawan, Elizabeth Paskahlia Ohyver, Margaretha Computer Science Departement Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The long COVID-19 pandemic has limited the activities of Ruang Publik Terpadu Ramah Anak (RPTRA) such as environment cleaning, repair of RPTRA infrastructure, learning and others, as well as the lack of public awareness about the existence and importance of RPTRA to the public and foreign tourists encouraging the RPTRA to make changes in the dissemination of information about the activities carried out in the RPTRA through the use of technology to survive and grow even during the Covid-19 Pandemic. The methodology used in this research is Group Discussion Forum with the administrators of the RPTRA Maya ASRI 13 to learn about the needs and expectations of the RPTRA administrators regarding the web portal to be built. Then, the implementation of the conceptual model, development, and evaluation of the effectiveness of the RPTRA information web portal was carried out. The contribution in this research is to implement a conceptual model on the information portal built on the platform of the Website with the PHP framework, which is then evaluated on the results of the pre-test and post-test about RPTRA the Wilcoxon-Signed Rank Test to test the effectiveness of the information web portal for general users. The results obtained from these tests indicate that the RPTRA Information Web Portal can help the public learn more about the existence of RPTRA Maya Asri 13. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Portals

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Leveraging Social Learning for Improved Cybersecurity Maturity: A Case Study Using the NIST Framework

Leveraging Social Learning for Improved Cybersecurity Maturi...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Marcel Meyliana Hendric Spits Warnars, Harco Leslie Nugraha Mursitama, Tirta BINUS Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Krida Wacana Christian University Information Systems Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta Indonesia BINUS Business School Bina Nusantara University Management Department Jakarta Indonesia

ISBN: (纸本)9798350379839

This research shows that social learning can be used to increase an organization's cybersecurity maturity level. Using a literature study and case study approach. Literature studies are used to identify social learning's key success factors (KSF) and challenges in general. Then, the case study was performed in the context of assessing the maturity level of information security using the NIST Cybersecurity framework at ABC Software House, a small-scale software development company in Indonesia. Assessment activities are performed through FGDs which then also identify the most relevant social learning' KSF to increase the organization's information security maturity level. Next, referring to the selected KSF, a process of identifying and mapping social learning' challenges was carried out. The integration of social learning can help organizations gain better focus and understanding in nontechnical contexts to increase their maturity level. This research highlights that integrating social learning with the NIST cybersecurity framework can provide organizations with better focus and perspective for formulating non-technical solutions. This study also enriches the existing literature on social learning in cybersecurity and offers practical insights for organizations looking to improve their cybersecurity resilience. © 2024 IEEE.

关键词： case study cybersecurity maturity level NIST social learning

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Parallelization of LSTM-GRU Architectures for Multivariate Prediction of Stock Prices

Parallelization of LSTM-GRU Architectures for Multivariate P...

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International Conference on Information Management and Technology (ICIMTech)

作者： Gabriel Asael Tarigan Eric Hermawan Abba Suganda Girsang Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

Anticipating stock market movements is challenging, as hasty prediction risks large financial losses. Stock price prediction is complex due to the high volatility and many influencing external factors. Recurrent Neural Network (RNN) models are made to learn time series data making it ideal for stock prediction based on records. Well-known RNN architectures for forecasting problems are Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). However, past studies only explored standalone models and have not generated convincing performance results. By integrating the efficiency of GRU to effectively handle shorter data sequences with LSTM's ability to capture long-term dependencies, our model aims to improve stock price forecasting accuracy by collaborating the strength between both architectures. The proposed model was used to forecast multivariate stock prices and evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). We found that parallelization of LSTM-GRU offers better prediction, up to 9.1% improvement compared to sequential LSTM-GRU and Bidirectional LSTM (Bi-LSTM).

关键词： Analytical models Sentiment analysis Accuracy Time series analysis Predictive models Logic gates Data models Information management Forecasting Stock markets

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A Review on Collaborative Filtering Using Geographical Information 8

A Review on Collaborative Filtering Using Geographical Infor...

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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Pohan, Husni Iskandar Sutoto Heryadi, Yaya Prabowo, Harjanto Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Indonesian Hospital Accreditation Commision Jakarta Indonesia Jakarta12960 Indonesia Binus Business School Doctor of Research in Management Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350344691

Since the corona pandemic, many internet users have maximized their interactions with the internet to minimize face-to-face meetings. As a result, many collaborative filtering algorithms, which are part of recommender systems, have been optimized so that users can obtain the best results. Collaborative filtering is an approach to providing recommendations that relies on the interactions and preferences of past users. Essentially, recommendations are generated by considering how users with similar preferences and behaviors have interacted. There are two primary forms of collaborative filtering: user-user and item-item. User-user collaborative filtering suggests recommendations based on users who share similar preferences with the current user. On the other hand, item-item collaborative filtering suggests recommendations based on items that are frequently selected together by other users. Both types of collaborative filtering rely on user interaction and preference data to deliver effective results. The latest trend related to collaborative filtering involves the use of geographic information. This paper aims to map the research developments related to collaborative filtering with geographic information in recent years, including the algorithms used, implementation issues, and challenges faced. Our hope is that this research can provide an overview of where this technology is heading. © 2023 IEEE.

关键词： Recommender systems

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Towards the Fun Therapy on People with Mental Disorder - Analysis on Serious Game for Mental Therapy

Towards the Fun Therapy on People with Mental Disorder - Ana...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Galih Dea Pratama Haryono Soeparno Yulyani Arifin Ford Lumban Gaol Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Mental health is one of the states of well-being which enables people to cope with various things in everyday life. Despite that, mental health is often overlooked, driving the emergence of numerous mental disorders on people. Along the presence of technological advancements, there is serious game which is utilized to entertain and achieve non-game goals related to real-life issues. Due to that, serious game can be leveraged as a media put into health sectors, particularly in presenting fun therapy to people diagnosed with mental disorders. This research presents systematic literature review on publications discussing the utilization of serious game in providing fun therapy for people with mental disorders, focusing on matters like the mental disorders emphasized within, the game elements presented in each publication, and ways to evaluate the serious game. The result indicates overall well-received responses regarding the usage of serious game in serving the purpose, albeit with different ways on evaluation process based on the mental disorders each publication addressed for.

关键词： Mental disorders Medical treatment Games Mental health Media Mobile handsets Serious games Usability Intelligent systems Systematic literature review

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Regional Classification of Indonesian Folk Songs Based on K-NN and PCC-LDA Model

Regional Classification of Indonesian Folk Songs Based on K-...

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

作者： Mahardhika, Ferdinand Warnars, Harco Leslie Hendric Spits Nugroho, Anto Satriyo Budiharto, Widodo Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Organisasi Riset Elektronika Dan Informatika Jakarta10340 Indonesia Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

One of the biggest challenges in Music Information Retrieval (MIR) is classification. MIR is a subset of Machine Learning (ML), where the methods found in computer science can be used to solve problems in the music domain area. Indonesia is an archipelago country inhabited by around 1,340 ethnic groups spread across 38 provinces. This ethnic group has a variety of cultures, including folk songs, and almost every area in Indonesia has folk songs. In this study, we conducted a regional classification of 500 Indonesian folk songs from 10 regions in Indonesia. We build a model for regional classification tasks using the MIR approach. This model combines feature selection and dimension reduction capabilities. By using hyperparameter tunning and a cross- validation of 5 folds, the result of our proposed model for regional classification of Indonesian folk songs using K-NN with PCC-LDA can obtain an accuracy rate of 84.7%. © 2023 IEEE.

关键词： Music

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Recommendation News for Joumalist Based on Twitter Data Using CNN 6

Recommendation News for Joumalist Based on Twitter Data Usin...

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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Harvy, Ikrar Girsang, Abba Suganda Saragih, Melva Hermayanty Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

Getting the update news fast is important for journalist. This study uses Twitter data which is fast and rich information for get the update news and useful for journalist. This study starts from collecting data from Twitter, then predicting the category of tweet using convolution neural network (CNN) algorithm. With convolution neural network data, Twitter can categorize 10 news categories that mention news to official Twitter media on Twitter social media. Scenario data in predicting news by dividing the data based on 70% training, 15% validation, and 15% testing. Then the data is converted into vector form where the convolution neural network predicts the data in vector form. The results show that, the convolution neural network is able to conduct the good performance based on the values of accuracy, precision, recall and f1 score. It also compares by the other methods. © 2022 IEEE.

关键词： classification news convolution neural network deep learning journalist Social media

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Serverless utilization in microservice e-learning platform

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Procedia computer science 2023年 216卷 204-212页

作者： Bryan Andreas Nday Gede Putra Kusuma Renaldy Fredyan Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Since cloud computing becoming the trend, the way servers being implemented slowly moves to the cloud. Companies did not need to buy a physical server machine to deploy an app. Having a private server on cloud infrastructure indeed already reduce some cost for on-premise server maintenance. However, there is still a cost for usage when the server is inactive or having low to no traffic at all. Serverless deployment offer function as a service where application is deployed as a function and cost is billed per function call. This paper proposed a solution where there are two deployment that works in turn between infrastructure as a service and function as a service deployment. This dual deployment offered the system to use the virtual private server or deployed instance on active hours, and switch to serverless functions on inactive hours. Switching to serverless on low traffic hours will cut the usage and cost of the microservice app by the least 25%, while having performance slightly comparable to microservice app deployed to instances.

关键词： Cloud Computing E-Learning Function as a Service Hybrid Deployment REST API

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