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检索条件"机构=Computer Science Department BINUS Graduate Program Computer Science"

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Model Prediction Using Random Forest Algorithm and Time Series Analysis Parts Distribution with in Indonesia Automotive Industry

Model Prediction Using Random Forest Algorithm and Time Seri...

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International Conference on Informatics and Computing (ICIC)

作者： H Andy Achmad Arief Ramadhan H Warnars Harco Leslie Widodo Budiharto Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia

Car spare parts are parts of a car that consist of components that form a certain unity and function, there are some parts that are damaged more often and therefore must be replaced more often. In this research, to predict the availability of spare parts using the times series moving average prediction method with random forest. The dataset used in this research is 19,068 consisting of location, transaction number, transaction date, part number, quantity and odo-km. Distribution of spare parts classified based on recapitulation of spare part distribution is RAE=54.5541% and RSE=63.8888%, distribution of spare parts based on location is RAE=55.5541% and RSE=65.2118%, maintenance based on ODO-KM is RAE=33.0949% and RSE=41.2241 % and distribution quantity RAE=33.749% and RSE=36.2317%. The results of spare part distribution analysis using the Confusion Matrix with probability accuracy are TP Rate=0.903, FP Rate=0.067, Precision=0.911, Recall=0.903, F-Measure=0.893, MCC=0.857, ROC Area=0.983 and PRC Area=0.948. The aim of this research is to determine the factors needed to build a machine learning-based spare parts distribution model, implementation, measurement and prediction in the automotive industry in Indonesia.

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Overview of tourism digital model: General factors and variables 6

Overview of tourism digital model: General factors and varia...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Sari, Ratna Gaol, Ford Lumban Prabowo, Harjanto Hastiadi, Fithra Faisal Meyliana School of Information Systems Bina Nusantara University Computer Science Department BINUS Graduate Program - Computer Science Information Systems Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Business School Undergraduate Program Bina Nusantara University Computer Science Department BINUS Graduate Program - Computer Science Management Department Jakarta11480 Indonesia Faculty of Economics and Business Universitas Indonesia Depok Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Tourism continues to be developed, as one of important sectors to support foreign exchange revenue and also support the economic sectors. The purpose of this research finds the mapping factor from digital economy and its implementation in tourism sector as known as tourism digital model. The Methodology of this research used literature approach from previous research of digital economy characteristics factors and digital tourism. The results of this research were generated variable in Tourism Digital Model that can help develop and support in implementation of digital tourism. © 2021 IEEE.

关键词： Tourism

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Preventing Cyber Crime in Electronic Medical Records Using Encryption Data 1

Preventing Cyber Crime in Electronic Medical Records Using E...

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1st International Conference on Technology, Engineering, and Computing Applications, ICTECA 2023

作者： Budiman Mantoro, Teddy Ratmono, Barito Mulyo Persadha, Pratama Dahlian National Intelligence College Post Graduate Program of Medical Intelligence Bogor Indonesia Sampoerna University Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350360554

With the advancement of technology, information systems have become increasingly necessary in almost all areas, including healthcare. One of the technologies used to facilitate this progress is electronic medical records (EMRs). Although EMRs have improved service delivery and patient care in healthcare, they also pose risks to patient data due to cybercrime attacks, which can result in data theft and alteration of medical records. Medical identity theft has become a major issue in healthcare systems around the world, While technology has been beneficial in improving service delivery and patient care in healthcare, it has also introduced numerous risks to patient safety and ultimately impacted patient health. Cybersecurity issues have advanced with technology, thus increasing the rate at which medical identity thefts occur. The healthcare industry lags in curbing cybersecurity issues, and thus, it is vulnerable to many cyberattacks. To address these risks, techniques, and methods are needed to ensure the security of EMR systems against cybercrime attacks. This study used a literature review method and identified eleven articles that met inclusion and exclusion criteria. Through analysis of these articles, several techniques and methods were identified to secure the privacy system in EMRs, such as mIBE-AES, DES, and RSA methods. The literature review revealed that Encryption Data is the safest technique to use, as it is standardized, widely accessible, and cost-effective. Furthermore, mIBE-AES is the safest method for EMRs because it is both secure and efficient for storing medical record documents. Performance evaluations were conducted to test the level of security against cybercrime attacks. © 2023 IEEE.

关键词： Cryptography

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Comparing the Effect of Attention Mechanism and Aspect Embedding on the Performance of Long Short-term Memory Model for Aspect-Based Sentiment Analysis

Comparing the Effect of Attention Mechanism and Aspect Embed...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Yaya Heryadi Bambang Dwi Wijanarko Dina Fitria Murad Cuk Tho Kiyota Hashimoto Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta Indonesia Computer Science Department BINUS Online Learning Jakarta Indonesia Information Systems Department BINUS Online Learning Jakarta Indonesia Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Prince of Songkla University Phuket Thailand

The attention mechanism is one of the key enablers which have positioned transformer models as the state-of-the-art models in Natural Language Processing. By having the attention mechanism, the first version (vanilla) transformer model can retrieve the hidden state but at the same time can maintain a low requirement on the context vector dimension by selectively choosing to which encoder hidden states to give priority attention. The objective of this study is to evaluate the effect of modified Long Short-term Memory models architecture on the model performance as a predictive model. The first model is modified by adding an attention mechanism to its architecture; whilst, the second model is modified by adding additional input to its architecture. The performance of both models is compared by assigning each model the same task: to address the aspect-based sentiment analysis task. The experiment results show that adding an attention mechanism is not significantly increase the performance of the Long Short-term Memory model in comparison to adding input to the same model. In particular, the average training accuracy of the former model is 0.885; whilst, the average training accuracy of the latter model is 0.942.

关键词： Training Analytical models Sentiment analysis Computational modeling Memory architecture Predictive models Transformers

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Aspect-based Sentiment Analysis using Long Short-term Memory Model for Leveraging Restaurant Service Management

Aspect-based Sentiment Analysis using Long Short-term Memory...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

In general, the hospitality industry has been acknowledged as a major sector that gives a high contribution to economic development in many countries including Indonesia. For that reason, many initiatives have been implemented to help the growth of the hospitality industry in many countries including Indonesia to rebound from the harsh impact of the Covid-19 Pandemic. One such initiative is improving restaurant services as the main sector of the hospitality industry. This paper presents empirical results of sentiment analysis as a means to assess the quality of restaurant services as the first step to improving service quality. In particular, this study explores the aspect-based sentiment analysis method to identify some aspects of restaurant service which need improvement by learning the polarity of customers toward the restaurant services without having to meet the customers directly. By using the aspect-based sentiment analysis method, the customer sentiments comprising opinions, sentiments, evaluations, attitudes, and emotions from restaurant service can be analyzed using customers’ online reviews as input. The main experiment findings showed that the Long Short-term Memory model can achieve high performance in predicting aspect polarization in restaurant service reviews. Other findings suggest that Sigmoid as an activation function achieved 0.97 average training accuracy and 0.69 average testing accuracy giving a better performance to the model in comparison to ReLU, Tanh, and ELU activation functions.

关键词： Industries Training Economics Sentiment analysis Pandemics Computational modeling Predictive models

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An Analysis of Hate Speech Detection Techniques

An Analysis of Hate Speech Detection Techniques

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Ricky Krisdianto Ivana Apriani Peter Miracle Halim Maria Susan Anggreainy Afdhal Kurniawan Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Hate speech is any act of provoking or insulting another person or group based on their ethnicity, religion, race, gender, sexual orientation, physical ability, or other characteristics. This can be done in a variety of ways. Because of this, a study was conducted that focused on ascertaining the most effective technique for identifying hate speech. It was discovered that LSTM, a form of recurrent neural network (RNN) that is particularly good at modeling sequential data, is the most successful technique to apply. Many natural language processing (NLP) activities may be performed with it, including hate speech detection, which makes LSTM have advantages over other techniques, both in terms of long-term memory mechanisms. LSTM, also able to handle long and complex text sequences, capable of recognizing more complex patterns, able to understand and consider the context in the text, to recognize the meaning of a word or phrase in a broader context. Then, based on the dataset obtained from the Kaggle website with a total of 24783 data, then the dataset is divided into training data and validation data, with a ratio of training data to validation data. of 80%: 20%. Then, the testing step results in an accuracy value of 87.10%.

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Technology Acceptance Readiness Analysis in the Context of Digital Tourism Village using Machine Learning Approach

Technology Acceptance Readiness Analysis in the Context of D...

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International Conference on Information Management and Technology (ICIMTech)

作者： Indra Prawira Nur Afny Catur Andryani Ebnu Yufriadi Mass Communication Program Communication Department Faculty of Digital Communication and Hotel & Tourism Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

Digital tourism village potentially accelerates rural development hence improving a country's economy. The Indonesian government has developed hundreds of tourism villages in recent years but has yet to popularize the development of digital villages. Technology design and development are pivotal factors in tourism villages' digitalization. Therefore, the development digital tourism village must consider village readiness by measuring technology adoption readiness or technology acceptance of the corresponding stakeholders. This paper presents the technology acceptance analysis in the context of a digital tourism village. We use a quantitative analysis approach using the TAM-UTAUT model with data collected from a questionnaire of 101 villagers (n=101) in Pasanggrahan Village, West Java, Indonesia. This research uses machine learning with clustering procedures to analyze the level of technology adoption readiness by implementing K-means Clustering algorithm. In addition, to observe the significance of each influencing factor to the technology adoption readiness level, feature importance analysis approach using Extra tree classifier algorithm is employed. The research finds that attitude toward use, perceived ease of use, and facilitating condition-have a considerable impact on the degree of readiness for technology adoption. On the other hand, the perceived utility and social impact are found to have minimal bearing on the preparedness level for technology adoption. Perceived ease of use and facilitating conditions are the most important factors to afford the acceleration of technology adoption readiness. The research contributes to the development of a digital tourism village and the analysis of technology adoption readiness using machine learning.

关键词： Java Machine learning algorithms Correlation Statistical analysis Government Clustering algorithms Classification algorithms Information management Stakeholders Random forests

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Evaluation of EIRLYS's E-Commerce Website from a User Experience Perspective using a User Experience Questionnaire (UEQ)

Evaluation of EIRLYS's E-Commerce Website from a User Experi...

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2022 International Conference on Electrical Engineering and Informatics, ICELTICs 2022

作者： Gabrila, Amara Nova Ramadhani, Mirza Fadila, Fujishiro Larasati Izdihar, N.S. Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia Bina Nusantara University Computer Science School of Computer Science Jakarta11480 Indonesia

ISBN: (数字)9781665487665

ISBN: (纸本)9781665487665

Fashion is one of the industrial sectors that never die. However, the majority of brands in this industrial sector are slowly forgetting the existence of Indonesian culture. EIRLYS is a company engaged in the fashion industry that produces clothing with the theme of Indonesian culture. Customers will be able to get clothes that have elements of Indonesian culture but still have a relaxed and informal impression. Clothing from EIRLYS emphasizes the principles of simplicity, minimalism, and elegance. These three principles want to be carried out in the design and user experience of the EIRLYS website to provide convenience for customers in transacting. Almost all industries, whether large, medium, or small, have used technology to promote their products in various ways, including using e-commerce websites. However, to ensure that EIRLYS has provided the best service through its website, it is important to know whether EIRLYS has met the overall User experience (UX) dimension. User experience Questionnaire (UEQ) is a technique that will be used to assess and test whether EIRLYS has met the expectations of users based on the User experience dimension before it can be used by customers. Based on the results of the user experience questionnaire filled out by EIRLYS users, it can be seen that EIRLYS has been able to meet user needs and expectations from all dimensions, ranging from attractiveness, perspicuity, efficiency, accuracy, stimulation, and novelty. © 2022 IEEE.

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Data mining for epidemiology: The correlation of typhoid fever occurrence and environmental factors 7

Data mining for epidemiology: The correlation of typhoid fev...

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

作者： Asadi, Faisal Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Typhoid fever is an endemic disease that burdens Indonesia and has a potentially fatal infection multisystem. Salmonella typhi bacterium is responsible for typhoid fever disease. Poor sanitation, crowding, and slums are the main factors of increasing typhoid fever incidences. Environmental factors directly connected to meteorological factors are the main factor in breeding the Salmonella typhi bacterium. This study aims to identify the correlation between meteorological parameters and typhoid fever disease occurrence. The study was carried out in Jakarta, Indonesia, and the Bureau of Meteorological, Climatology, and Geophysics (BMKG) provided the meteorological parameter data. In addition, the Jakarta health surveillance office provided information on typhoid fever hospitalizations from 2019 to 2021. Pearson's concept was utilized d to investigate the correlation between typhoid fever incidences and the meteorological parameters. Humidity, precipitation, and wind speed are the meteorological parameters that significantly affect in contribute to the occurrence of typhoid fever disease. These findings might be used as a reference for Indonesia's government in making public policy to prevent typhoid fever in Indonesia. © 2022 Elsevier B.V.. All rights reserved.

关键词： Data mining

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AI in EEG-Based BCI for the Diagnosis of Mild Cognitive Impairment: A Mini Review 13

AI in EEG-Based BCI for the Diagnosis of Mild Cognitive Impa...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Zaitsev, Vasilii Wei, Chun-Shu EECS International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9798331521929

Mild Cognitive Impairment (MCI) is a condition that often precedes dementia, making early diagnosis critical for delaying cognitive decline. Electroencephalography (EEG) has emerged as a non-invasive, cost-effective tool for monitoring brain activity and detecting MCI. This paper overviews recent advancements in machine learning (ML) and deep learning (DL) models for EEG-based MCI diagnosis. Traditional ML approaches, such as support vector machines (SVM) and K-nearest neighbors (KNN), have been widely used but rely on manually extracted features and face challenges with the complex nature of EEG signals. In contrast, DL models like convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and transformers have shown promise in automatically learning features and capturing temporal and spatial information from EEG data. Despite these advancements, issues such as small dataset sizes and variability in EEG recordings remain barriers to clinical application. This paper discusses these challenges and highlights potential future directions for improving the diagnosis of MCI. © 2025 IEEE.

关键词： Convolutional neural networks

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