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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Emmanuel, Gunadi Supangkat, Suhono Harso Gaol, Ford Lumban Ranti, Benny Bina Nusantara University Doctor of Computer Science Jakarta Indonesia Univ. Katolik Musi Charitas Fakultas Sains Dan Teknologi Palembang Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350339543

The phenomenon of urbanization in Indonesia is inevitable. The new residential and economic centers in suburban areas is also a problem in city development. The gradual planning and development of smart cities in a living lab require consideration of the right location for a living lab. This study wants to show that suburban areas as city buffer zones can become living laboratories for smart city development. The diversity of situations in cities and regencies across Indonesia, the potential for resources, and the problems faced are the challenges of developing a living lab-Garuda Smart City Framework. This research uses the method of reviewing the literature of research publications for the last five years (2019-2023) to obtain information on Smart City development in Indonesia. We collected selected articles from databases in Google Scholar, IEEE, and Scopus using the Publish and Perish 8. The search keywords used were garuda AND Smart city AND Framework. The findings show the potential and dynamics of buffer zones to become appropriate living laboratories. Smart city regional planning can more holistically involve neighborhoods and address urban issues. © 2023 IEEE.

关键词： Smart city

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Database Design for Indonesian Scholarship Recommender Systems 7

Database Design for Indonesian Scholarship Recommender Syste...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Elwirehardja, Gregorius Natanael Jason Dominic, Nicholas Pardamean, Bens BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

Following the evolution of technology, researchers have conducted several studies to propose reliable scholarship recommender systems. However, few have explored the data storage systems, which are highly beneficial to collect huge volumes of real data in training the recommender systems. In this paper, a database design for collecting such data was proposed, equipped with integer encodings for categorical variables and a method to normalize variables with varying ranges. Using Connoly and Begg's database design method, the final design consisted of a normalized Entity Relationship Diagram (ERD) and data dictionaries. The design was also constructed to support usages of various data for determining scholarship recipients, including education history, achievements, and organizational experiences. The proposed design can be implemented on information systems to allow easier information access for scholarships in Indonesia. © 2022 IEEE.

关键词： Recommender systems

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Convolutional Neural Network Using Res-Net For Organic and Anorganic Waste Classification 4

Convolutional Neural Network Using Res-Net For Organic and A...

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

作者： Girsang, Abba Suganda Yunanda, Rezky Syahputra, Muhammad Edo Peranginangin, Ezra Binus Graduate Program-Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399073

Convolutional Neural network is state of the art of image recognition or image classification. However to build the robust model using CNN needs many parameters adjusted, and choosing the good combination hyperparameter which impacts taking much computation time. Genetic algorithm is one method metaheuristic which is robust for choosing the combinatorial possible hyperparameter. This model also uses ResNet-50 which is a pretrained model of convolutional neural network that consists 50 layers. By using a pretrained like ResNet-50, it will increase the performance model. CNN-ResNet with efficient genetic algorithm (EGA) to optimize the hyperparameter. The EGA algorithm utilizes transfer learning techniques in its algorithm so that the optimization process on CNN can achieve unified accuracy values quickly. The best performance model optimized using EGA outperformed the ResNet-50 model and the model optimized using GA and VLGA in classifying organic and inorganic materials. The accuracy value obtained from EGA is 97.53% with a loss of 0.08. © 2022 IEEE.

关键词： Convolutional neural networks

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An Object Oriented Floating Fuzzy Decision Model for Evaluating the Student Performance

An Object Oriented Floating Fuzzy Decision Model for Evaluat...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Ditdit Nugeraha Utama Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

The concept of object-oriented (OO) serves is a fundamental approach in the development of models. The stages associated with this method contribute significantly to ensuring that the resulting model is both lucid and transparent. The primary objective of the study is to create a decision model for evaluating student performance. Floating fuzzy logic (FFL) is employed as a technique to handle fluctuating data within the model. Moreover, OO conception plays a central role in analyzing, designing, and constructing the model through the utilization of four distinct types of Unified Modeling Language (UML) diagrams: object, activity, state-machine, and sequence diagrams. The model itself is crafted using the Python programming language and executed in the Google Colab platform. Additionally, this model has the capability to simulate changes in students' performance on a semester-by -semester basis, exhibiting a variance of 15 % when compared to the conventional fuzzy logic model.

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Attention is Everything You Need: Case on Face Mask Classification 8

Attention is Everything You Need: Case on Face Mask Classifi...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Pratama, Nanda Harianto, Dody Filbert, Stefan Warnars, Harco Leslie Hendric Spits Muyeba, Maybin K. School of Computer Science Bina Nusantara University West Jakarta Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University West Jakarta Jakarta11480 Indonesia MIST Consulting ManchesterM410SA United Kingdom

Automated face mask classification has surfaced recently following the COVID-19 mask wearing regulations. The current State-of-The-Art of this problem uses CNN-based methods such as ResNet. However, attention-based models such as Transformers emerged as one of the alternatives to the status quo. We explored the Transformer-based model on the face mask classification task using three models: Vision Transformer (ViT), Swin Transformer, and MobileViT. Each model is evaluated with a top-1 accuracy score of 0.9996, 0.9983, and 0.9969, respectively. We concluded that the Transformer-based model has the potential to be explored further. We recommended that the research community and industry explore its integration implementation with CCTV. © 2023 The Authors. Published by Elsevier B.V.

关键词： Attention Convolutional Neural Network Deep Learning Face Mask Classification Transformer

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Random Forest Based QSAR Model Analysis for Predicting Drug Effectiveness Against Mycobacterium Tuberculosis Bacteria

Random Forest Based QSAR Model Analysis for Predicting Drug ...

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International Conference on ICT For Smart Society (ICISS)

作者： Alicia Josephine Ekosputri Amanda Ardianti Maria Susan Anggreainy Brychan Artanto Nora Fitriawati Fanny Angelia Valentina Vincent Kartamulya Santoso Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

This research focuses on the use of machine learning methods, especially Random Forest, in designing tuberculosis drugs more effectively and efficiently. The research process begins with the creation of a QSAR (Quantitative Structure-Activity Relationship) model using a dataset from ChEMBL that includes compounds with activity against Mycobacterium tuberculosis. This model aims to predict the pIC50 value, which is an indicator of a compound's biological activity as a tuberculosis drug. The results of the Random Forest model showed high accuracy, which is 92%. This accuracy indicates that the model has a good ability in predicting the effectiveness of compounds against Mycobacterium tuberculosis. With this result, the developed model can be used as an auxiliary tool in the discovery of tuberculosis drugs and can be further developed to increase the effectiveness in drug research.

关键词： Drugs Accuracy Tuberculosis Biological system modeling Linear regression Predictive models Compounds Reliability Random forests Overfitting

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ARtopia: A Mobile Application based Digital Crowdfunding Platform 2

ARtopia: A Mobile Application based Digital Crowdfunding Pla...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Daventa, Sherleen Setiawan, Andika Warnars, Harco Leslie Hendric Spits Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

Crowdfunding has become a popular term these days, and people usually do crowdfunding to raise a certain amount of money for a specific cause. There are already many popular crowdfunding portals in Indonesia where people can send support to anyone with money. When talking about crowdfunding, most people probably think of providing support to others in need, for example, those who are sick and poor. However, crowdfunding is not always like that. Crowdfunding can be applied to any field, especially in the creative industries, as the people in this report will discuss. If you do not know it beforehand, the creative industry sometimes struggles with copyright and online piracy issues. These problems will soon be very detrimental to the artists as creators. Another problem often sought is the artists themselves, especially those still amateurs. They are often hesitant to share their works and promote themselves because they do not know-how. In this case, crowdfunding application development will be necessary to facilitate all artists and content creators (such as content creators, vloggers, or even game developers) to gain support from their fans and promote themselves to become more popular. © 2022 IEEE.

关键词： Crowdsourcing

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The Pivotal Role of Red Teaming in Cybersecurity Drill Tests in Enhancing Indonesia's Cyber Defenses

SSRN

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SSRN 2024年

作者： Yulianto, Semi Soewito, Benfano Gaol, Ford Lumban Kurniawan, Aditiya Computer Science Department Binus Graduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

This study examines the pivotal role of red teaming within the context of cybersecurity drill tests, focusing on its contribution to enhancing Indonesia's cyber defenses. Through a detailed analysis, this research examines Red Teaming's capacity to elevate organizational readiness against cyber threats, identify latent vulnerabilities, and ensure compliance with Indonesia's rigorous cybersecurity regulations. Key findings revealed the profound effect of red teaming on fortifying cybersecurity protocols, showing its capability to discover and effectively counteract security vulnerabilities. This study positions Red Teaming as an essential strategy in the array of cybersecurity practices, offering valuable insights into its application and impact on meeting regulatory standards. Ultimately, this investigation underscores the indispensable role of red teaming in advancing Indonesia's cybersecurity posture amid the constantly evolving digital threat landscape. © 2024, The Authors. All rights reserved.

关键词： Regulatory compliance

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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 Automatic Speech Recognition with XLSR-53

arXiv

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arXiv 2023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 the XLSR-53 pre-trained model, the XLSR stands for cross-lingual speech representations. 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 a competitive Word Error Rate (WER). The total amount of data used in this study is 24 hours, 18 minutes, and 1 second: (1) TITML-IDN 14 hours and 31 minutes;(2) Magic Data 3 hours and 33 minutes;and (3) Common Voice 6 hours, 14 minutes, and 1 second. With a WER of 20%, the model built in this study can compete with similar models using the Common Voice dataset split test. WER can be decreased by around 8% using a language model, resulted in WER from 20% to 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. © 2023, CC BY-SA.

关键词： Statistical tests

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