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International Conference on Computing, Engineering, and Design (ICCED)

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

Opening or closing dam-gate activities manually conducted in Manggarai dam to control the dam water level. The controlling action operated to avoid the flood possibility occurring in Jakarta city (the Indonesian capital). The study was conducted to develop a smart model for flood controlling based on service or called a service-oriented smart model (SOSM). The water-flow algorithm (WFA), fuzzy logic, object and service-oriented are four main methods operated in the study. The WFA is a central method to model the real water flow in the river coming from Katulampa dam (in Bogor city) until Manggarai dam (in Jakarta). The fuzzy logic functioned to simulate the dam’s water level and the gate open/close decision should be decided by avoiding the bias value. The object-oriented model analysis and design approach, where the unified modelling language (UML) tools are operated to analyze and design the constructed model. Then, the service-oriented conception is used to integrate all sides in implementing the model. Finally, the constructed model can simulate the flood status in Jakarta via status value in decimal numbers with 6 numbers behind the point.

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Easy Data Augmentation for Handling Imbalanced Data in Fake News Detection

Easy Data Augmentation for Handling Imbalanced Data in Fake ...

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Technology, Engineering, and Computing Applications (ICTECA), International Conference on

作者： Rilo Chandra Pradana Setiawan Joddy Abba Suganda Girsang Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

Detecting fake news in the digital era is challenging due to the proliferation of misinformation. One of the crucial is-sues in this domain is the inherent class imbalance, where genuine news articles significantly outnumber fake ones. This imbalance severely hampers the performance of machine and deep learning models in accurately identifying fake news. Consequently, there is a compelling need to address this problem effectively. In this study, we delve into fake news detection and tackle the critical issue of imbalanced data. We investigate the application of Easy Data Augmentation (EDA) techniques, including back-translation, random insertion, random deletion, and random swap to mitigate the adverse effects of imbalanced data. This study focuses on employing these techniques in conjunction with a deep learning framework, specifically a Bidirectional Long Short-Term Memory (BiLSTM) architecture. The results of the EDA techniques will be systematically compared to see their effectiveness and their impacts on model performance. This study reveals that various EDA techniques, when coupled with a BiLSTM architecture, yield significant improvements in fake news detection. Among the experiments, it shows that Random Insertion, with an impressive accuracy rate of 81.68%, a precision score of 89.38%, and an F1-Score of 87.77% emerges as the most promising technique. The study also highlights the exceptional potential of Back-translation stands out with an 87.16% recall performance.

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Multi-Criteria based Decision Model for Evaluation

Multi-Criteria based Decision Model for Evaluation

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

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

Addressing multiple criteria and parameter issues in computer modelling presents a significant challenge. Several factors including data types, parameter behaviours, and purposes, must be taken into account to enhance computer modelling capability; particularly in evaluation cases. Through the utilization of a multi-criteria and method approach, a decision model was effectively developed to assess a case of environmental sustainability level of a building. One method operated in the study is the curve method for handling membership function form in realizing fuzzy logic. This innovative model demonstrates superior performance. It achieves an impressive accuracy rate of 96%, surpassing the previous model that employed a trapezoidal approach to describe fuzzy membership functions hy 1%.

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Mobile Application to Help Abandoned Pets 3

Mobile Application to Help Abandoned Pets

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Rahajeng, Salicca Dewi Nurfitriyani, Siti Julianingsih Akbar, Zakin Izzani Leonardo, Calvin Warnars, Harco Leslie Hendric Spits Warnars, Laurens 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)9798350354348

As social beings, humans like to interact with each other, including other creatures of God, such as animals, and keep them as pets. Many pets, such as dogs, cats, birds, fish, rabbits, and so on, include unusual pets such as reptiles, pigs, insects, etc. As a result of the lack of human awareness of pets and the nature of keeping animals, which is only for fun, it is not soulful to keep animals. So when people are bored, they abandon their pets, which causes new problems in human life today. In this case, we created a mobile application that can be shared with pet lovers where they can share information about abandoned pets and the possibility of owning pets from these abandoned pets. Mobile applications were designed using use case diagrams to show business processes, class diagrams to show database models, and user interfaces to present a similar interface to users. This mobile application will become a platform that can gather all people who care about pets and make pets a part of humans. © 2024 IEEE.

关键词： Mobile applications

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Predicting Stock Market Movements Using Long Short-Term Memory (LSTM) 4

Predicting Stock Market Movements Using Long Short-Term Memo...

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Fauzan, Achmad Susananggreainy, Maria Nathaniel, Nicholas Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

This paper explores using artificial intelligence (AI) to predict stock market movements and build optimal portfolios. The research methodology involves using LSTM networks to predict stock performance. The study aims to combine AI with human expertise to develop an intelligent trading system. The findings emphasize the importance of selecting appropriate AI approaches for accurate predictions and optimal portfolio management. The results of this study state that with LSTM, we can predict stock prices that are very close to their real prices. The average LSTM method in predicting stock prices is about 97.2938%. Average error obtained when using LSTM was 2.3223%. © 2023 IEEE.

关键词： Long short-term memory

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Concept, Technology, Barriers To Smart Village: A Systematic Literature Review 11

Concept, Technology, Barriers To Smart Village: A Systematic...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Utamajaya, Joy Nashar Meyliana Prabowo, Harjanto Hidayanto, Achmad Nizar Ramadhan, Arief Bina Nusantara University Computer Science Department Binus Graduate Program - Computer Science Department Jakarta11480 Indonesia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350305968

Smart villages that utilize digital technology are a solution to assist rural community services in meeting their needs, especially for developing countries. However, financial problems, namely the high cost of infrastructure, the size of the informal economy and governance are challenges in limiting the government's aspirations regarding the smart village concept. The presentation of this article examines the state of smart village development in developing countries, which includes conceptual understanding, motivations, drivers and barriers to smart villages. The purpose of this study is to find answers to research questions by reviewing several selected study articles. The method used was to search for selected studies and there were 60 selected studies identified from a search of five literature databases which initially contained 3394 articles. The results of the data were analyzed using synthetic analysis and thematic analysis. The results of this study found data related to Concept, Technology, Barriers To Smart Village which came from several articles by answering questions from the research, namely RQ1: How many articles from the 10 most populous countries discussed Smart Village, RQ2: What are the basic dimensions of Smart Village, RQ3: What new technologies are discussed in Smart Village digitization, RQ4: What are the Challenges in Smart Village. The results of this SLR can also later be used as a reference for further researchers to find out the background of research related to smart villages. The conclusion obtained in this research is the end of the explanation of this article concludes that there are still many shortcomings in this research and it is hoped that in the future many studies will be able to explore other aspects such as regulations and others that are not discussed in the article so that it is expected to be a reference for further researchers to examine information related to smart villages. in order to find an ideal and up-to-date con

关键词： Developing countries

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Diagnosing Diabetes Using Soft Voting Ensemble Machine Learning Model 10

Diagnosing Diabetes Using Soft Voting Ensemble Machine Learn...

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

作者： Kusuma, Christian Chandra Chandra, Nathalia Utomo, Galang Nurbudi Parimartha, Putu Agus Hidayat, Jessica Anggreainy, Maria Susan Parmonangan, Ivan Halim School of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350339543

Diabetes is a metabolic disorder caused by a dysfunction in insulin secretion which leads to an increase in blood sugar levels in the body. The disease has dangerous risks for those who suffer from it, as it can lead to various complications and even death. Because of the dangerous risks associated with diabetes, early detection of the disease is important. Machine learning can help the process of diagnosing diabetes quickly and accurately because of its ability to learn and process data independently. The goal of this research is to develop a diabetes detection system using ensemble learning with a soft voting classifier. In predicting diabetes, this research uses 14 types of algorithms with the help of the PyCaret library, including extra trees classifier, random forest, light gradient boosting, machine, gradient boosting classifier, decision tree, adaboost classifier, logistic regression, linear discriminant analysis, k-neighbors classifier, quadratic discriminant analysis, naive bayes, support vector machine-linear kernel, dummy classifier, and ridge classifier. From the evaluation process, the five best algorithms are chosen with an accuracy level of 91.3%, 91.3%, 89.5%, 92.2% and 85.3 % These five algorithms are then blended using ensemble learning with a soft voting classifier. The accuracy produced by the soft voting classifier method is higher than using a single classifier, at 97.3%. © 2023 IEEE.

关键词： Learning systems

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Efficiency of Multi-Language Data Serialization for Hybrid Cloud-Edge HRIS

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Procedia computer science 2024年 245卷 28-38页

作者： Andreas Winata Haryono Soeparno Ford Lumban Gaol Yulyani Arifin Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In hybrid cloud-edge systems, data processing is distributed between cloud servers and edge servers, this makes data serialization critical for efficient data transfer between servers. This study evaluates the efficiency of multi-language data serialization of Human Resources Information System (HRIS) in a hybrid cloud-edge environment. Because lack of existing studies that specifically evaluate multi-language data serialization for hybrid cloud-edge HRIS, this study conducted a Systematic Literature Review (SLR) to get the list of current state of the art of data serialization methods and then used this result to evaluate the methods. By focusing on HRIS employee data from the employee management module, this study measures both data serialization and deserialization using performance metrics of time, data size, and CPU usage. The results indicate that no methods excel in all metrics, but FlatBuffers, Protocol Buffer, and Apache Avro can provide balanced effectivity across the metrics. The study gives valuable insight for selecting data serialization methods, architecture, and systems requirements in hybrid cloud-edge HRIS to ensure the performance efficiency of the system.

关键词： Data Serialization Human Resources Information System Hybrid Cloud-Edge Performance Metrics Employee Management

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Perfomance Improvement of mpdCNN as Ensemble Learning Model on Low Resolution Image-Face Recognition System

Perfomance Improvement of mpdCNN as Ensemble Learning Model ...

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International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Daniel Anando Wangean Derwin Suhartono Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Low resolution image-face recognition system is one of the challenging aspects of face recognition models' development. From machine learning, deep learning, and into ensemble learning are implemented to develop face recognition models. Including mpdCNN that published by Mishra in a journal article. This mpdCNN already achieved 88.6% accuracy score on SCFace dataset while published. Based on literature review, ensemble learning model can be improved by modifying parallel layers. The purpose of this research is to improve mpdCNN's performance by implementing some modifications include adding parallel layers, alternating the fusion layers, expanding the layers, and adding residual connections. Some modifications were successfully improved mpdCNN's accuracy score. By adding parallel layers and residual connections into mpdCNN's architecture, the modified mpdCNN that proposed in this research achieved 92.33% accuracy score, measured by Rank-k evaluation metric.

关键词： Measurement Deep learning Accuracy Image resolution Image recognition Face recognition Instruments Robustness Ensemble learning Systematic literature review

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Cultivating Social Media Utilization by Television Stations: An Analysis of Instagram Practices in Czech Republic 17

Cultivating Social Media Utilization by Television Stations:...

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Balcha, Daniel Messele Rahyadi, Irmawan Krisnawati, Ayu Agung Mirah Bestari, Rifa Czech Life Science University Faculty of Business and Economics Psychology Department Prague Czech Republic Binus University Binus Graduate Program-Master of Strategic Marketing Communication Communication Department Jakarta Indonesia Binus University Faculty of Economisc and Communication Communication Science Department Jakarta Indonesia

ISBN: (纸本)9781665453486

In this study, the utilization of social media platforms was investigated out of three well-known Czech television stations using daily-based data on the Instagram official accounts of the stations, which involves a lengthy sample period from July 2021 to August 2022. The main contribution of this paper is that the study is a novel examination of Czech Republic television stations on the popular image-sharing social media while taking into account trend and entertainment conditions, and their presentability over different content and messages. The results indicate that several content strategy patterns such as how snapshots of TV program sets can produce a significant positive engagement from fans of the programs over certain parts of the shows. In addition, the results show that none of the content posted on the accounts which were examined contained a low-quality image. However, without considering trends and entertainment conditions, this study finds that social media is another media over digital spaces that has superior benefits if it is applied as an extension media for the television industry. The robustness of our results is confirmed based on qualitative analysis and several television stations samples. The findings of this study are beneficial for the social media practitioners engaged in the practice of diverse social media. © 2023 IEEE.

关键词： Television stations

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