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Web Application based Ridesharing Your Transportation in Jakarta 2

Web Application based Ridesharing Your Transportation in Jak...

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

作者： Kusuma, Giovanni Randy Handana, Afan Rizqi Warnars, Harco Leslie Hendric Spits School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

The city of Jakarta is getting more and more congested and crowded by the number and types of vehicles. Lack of public awareness to use public transportation causes this congestion to worsen, especially during rush hours in the morning before going to work or school and in the afternoon when returning home. This is also caused by the lack of the government of the capital city of Jakarta in providing cheap, safe, and comfortable public transportation. It is not surprising that the Indonesian government has plans to move the capital city of the Indonesian government from Jakarta on the island of Java to East Kalimantan on the island of Kalimantan. Therefore, seeing this severe traffic jam, it is essential to propose an application that can reduce congestion so that drivers who bring their vehicles can share with people around them to travel to work as a routine activity during weekdays. The model of this vehicle sharing application was designed using use case diagrams, and the database design was designed using class diagrams. In addition, security issues are also significant considering that people meet in this vehicle sharing application, and some bad people may take advantage of it. Therefore user registration and moderation are essential. It is hoped that this vehicle sharing application will reduce congestion in the capital city of Jakarta and become an example in other cities. © 2022 IEEE.

关键词： Vehicles

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Enhanced Personality Prediction: Fusing Structural and BERT-Based Lexical Features on Augmented Twitter Data 7

Enhanced Personality Prediction: Fusing Structural and BERT-...

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

作者： Roslynlia Suhartono, Derwin 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

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

Conventionally, assessing people's personality done by psychology experts requires more time and effort. Hence automatic personality prediction is starting to get noticed by researchers. Nowadays, many people engage themselves in social media platforms, one of which is Twitter. This platform stores actions done in this media which reflect one's personality. This research focuses on improving the performance of personality prediction models based on machine learning algorithms, by augmenting the dataset using Easy Data Augmentation to overcome imbalance dataset, thereafter combining n-grams with POS-tag weighted by TF-IDF. Results show that this method outperforms other machine learning approaches and decreases bias, with a maximum of 72.62% of the best average accuracy and 72.72% best average Fl score using Support Vector Machine classifier. The second-best model is obtained by XGBoost and, lastly, Naive Bayes classifier. Overall, this study shows that data augmentation with the addition of POS-tag feature is evident in improving the performance of personality prediction models. However, larger data is needed. © 2022 IEEE.

关键词： Learning algorithms

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Single Image Perspective Grid System on Floor Surface for 2D to 3D Transformation 8

Single Image Perspective Grid System on Floor Surface for 2D...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Rusjdi, Darma Abdurachman, Edi Heryadi, Yaya Kusuma, Gede Putra Bina Nusantara University Computer Science Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453899

Our study discusses the application of the perspective grid concept as the basis for the process of transforming the 2D coordinates of the image into 3D coordinates in real space for estimating the location of objects in a single image scene. In several other studies in the field of robotics, AR use methods and media in the form of a chessboard box as a reference for camera calibration and location estimation. However, it does not show accurate results for the size of objects in the room. We saw the potential areas of Alberti's perspective grid, which is often used as a reference in making sketches for the arts and various engineering fields so that they are visually precise with reality. Our study uses 6 reference points in a room arrangement with a floor grid pattern as a reference for the formation of a perspective grid. Based on the triangle comparison approach to determine the x-ordinate, the average error is 0.2193. And through 3 interpolation approaches to get the most accurate results with the Lagrange and Newton methods on the 6th order with the average difference between the actual and predicted points of 1.5 cm from the y-direction distance of the camera from 2 meters to 6.80 meters. © 2022 IEEE.

关键词： Newton-Raphson method

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Digital Emotional Intelligence: Fostering Lifelong Learning in the Digital Age among Indonesian Zillenials (SDG 4 View) 3

Digital Emotional Intelligence: Fostering Lifelong Learning ...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Herlina, Maria Grace Triana, Ika Iskandar, Karto Godwin, Gelard Binus Business School Undergraduate Program Management Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Reyzone Incorporation Department of Digital Business New York United States

ISBN: (纸本)9798350367492

This study investigates the impact of digital emotional intelligence (DEI) on the achievement needs of Generation Z in Jakarta, with a focus on the interplay between digital self-awareness and digital relationship management. Utilizing a sample of 280 individuals, the method involves structural equation modelling to explore how digital relationship management mediates the relationship between digital self-awareness and the need for achievement. The objective is to highlight the necessity of integrating DEI into educational frameworks to support lifelong learning and personal development, aligning with Sustainable Development Goal 4 (SDG 4). Results reveal a significant positive correlation, suggesting that effective digital relationship management enhances achievement motivations by fostering better self-regulation and interpersonal interactions in digital environments. In conclusion, this research contributes to understanding DEI's role in educational and organizational settings, emphasizing its importance in the digital age. © 2024 IEEE.

关键词： Sustainable development goals

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Detection of Narcotics Money Laundering in Suspicious Financial Transactions Using Convolutional Extreme Gradient Boosting (ConvXGB)

Detection of Narcotics Money Laundering in Suspicious Financ...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Leonardus Verrell Sayoga Kinantra Winarno Antoni Wibowo Computer Science Department Binus Graduate Program – Master of Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

With the development of narcotics problems that continue to increase, the Indonesian Government responds through the Badan Narkotika Nasional (BNN) with data showing the condition of narcotics tends to grow every year and is followed by the number of assets of money laundering (ML) crimes against narcotics cases. One of the banks in Indonesia as a reporting party for suspicious financial transactions (SFT) has obstacles in detecting narcotics ML due to complex and rarely found patterns. Some previous studies conducted experiments using Convolutional Neural Network (CNN), Extreme Gradient Boosting (XGBoost), and even a combination of both into Convolutional Extreme Gradient Boosting (ConvXGB), and improved model performance in several datasets. This paper designs a model using the ConvXGB algorithm by adopting the CNN architecture, LeNet-5, by applying several convolution layers and pooling layers as a baseline model for feature learning, and the XGBoost as feature classification. Three phases of research are the preprocessing phase by collecting data, transforming data, balancing data with a hybrid sampling technique, splitting data, and scaling data, followed by the implementation phase by creating a ConvXGB model, training and testing the model, then finally the evaluation phase by analyzing results and hyperparameter tuning. The dataset used is SFT from the bank during 2023. This ConvXGB has three convolution layers, a pooling layer, and a flattened layer. The performance test results are the accuracy value and F1-Score value of 99.11% each after hyperparameter tuning. By performing a hybrid model, the model performance results are better.

关键词： Training Representation learning Analytical models Convolution Government Boosting Data models Convolutional neural networks Tuning Testing

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Hate Speech Text Classification Using Long Short-Term Memory (LSTM) 4

Hate Speech Text Classification Using Long Short-Term Memory...

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

作者： Ariwibowo, Suminar Girsang, Abba Suganda Diana Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399073

This paper aims to build hate speech text classification model by applying a combination of LSTM and FastText. The features of hate speech & non-hate speech, target hate speech, and categories of the hate speech. Dataset of those features taken from previous research by Okky Ibrohim. FastText word embeddings is used for formation of text vectors that will be used as input of the LSTM training model. The evaluation results obtained by getting the level of accuracy using confusion matrix. The accuracy value of text classification in this study is 83.52% on the classification of hate speech, 78.44% on the classification of target labels for hate speech, 82.75% on the classification of the label for category of hate speech. © 2022 IEEE.

关键词： Long short-term memory

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Optimizing Big Data Implementation to Create Business Value and Architecture Proposed in the Banking Industry: A Systematic Review

Optimizing Big Data Implementation to Create Business Value ...

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

作者： Kristiana, Idha Meyliana, Meyliana Hidayanto, Achmad Nizar Prabowo, Harjanto Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Management Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The exponential growth of data is compelling organizations to employ data in decision-making. As one of the businesses with an ecosystem that contributes to data growth, banks have challenges in generating insight. A high level of data security is frequently linked to a high level of data access difficulties. This poses a challenge to the implementation of data-driven business, where taking control of our data is one of the best ways to ensure that we not only own data but also have the ability to process and use data to extract business value. Through literature review, a number of challenges to optimizing the implementation of big data analytics in the banking industry were discovered. Data governance refers to the methods and procedures that assist banks in managing and securing data. A big data architecture is presented to address the highlighted issues, particularly with a multi-tiered approach to big data structures. With the adoption of this architecture, it will be simpler to generate business-value-generating insights for the banking industry using big data with accessibility and protection of data. © 2023 IEEE.

关键词： Big data

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Twitter-based Sentiment Analysis for Indonesian Drug Products using Supervised Feature Engineering 5

Twitter-based Sentiment Analysis for Indonesian Drug Product...

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5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022

作者： Purwandari, Kartika Jeremy, Nicholaus Hendrik Arisaputra, Panji Philip, Samuel Suhartono, Derwin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665455121

Social media communications offer valuable feedback to firms about their products. Twitter users share their opinions about e-commerce products on social networking sites. This paper reports a study in sentiment classification for Indonesian drug products for automatically classifying Twitter data as expressing positive or negative sentiments or opinions. The study investigates the effectiveness of using a Decision Tree, Support Vector Machine, Random Forest, Na rve Bayes, and Logistic Regression, along with the term frequency-inverse document frequency used to classify 531 tweets into recommended (positive sentiment) and not recommended (negative sentiment). Among all traditional machine learning models, the highest average accuracy value is 88.6 in the RF model and the lowest average accuracy value is 80.2 when using the LR model. Judging from the average accuracy obtained, the DT and RF model used has a considerable performance © 2022 IEEE.

关键词： Sentiment analysis

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Sentiment Analysis of User Review on Indonesian Food and Beverage Group using Machine Learning Techniques

Sentiment Analysis of User Review on Indonesian Food and Bev...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Yanfi, Yanfi Heryadi, Yaya Lukas, Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University Binus Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9781665468763

A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches of AI have focused on developing various algorithms that can learn from input data without explicitly being programmed. In the food industry, notably in restaurants, smartphone technology is typically used to collect data from customers efficiently such as orders or feedback toward the services provided. The use of smartphones by some restaurants is motivated by their convenience and efficiency. Furthermore, various promotions, discounts, and membership benefits offered by cellular service providers have attracted the use of smartphones as devices for improving restaurant services to their customers. This study aimed to conduct a sentiment analysis related to multi-brand restaurants in Indonesia based on customer feedback. The data retrieved from the Google Play Store consists of 756 reviews from Boga, Champs, Hangry, Ismaya, Kulo, and Union groups. In this study, TF-IDF was used as a vectorizer to represent customer feedback as numeric vectors. The polarity sentiment of customer feedback was recognized using classifier models based on machine learning such as Logistic Regression, Naive Bayes (NB), Random Forest, and Support Vector Machine (SVM) as the classifier. The empirical results showed that SVM has the best accuracy of 93.10 % average accuracy followed by Logistic Regression with 92.41 %, Random Forest with 84.14 % average accuracy, and Naive Bayes with 76.55 % average accuracy. © 2022 IEEE.

关键词： Sentiment analysis

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Building Damage Assessment Using Deep Learning: Bibliometric Analysis 7

Building Damage Assessment Using Deep Learning: Bibliometric...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Sidharta, Sidharta Warnars, Harco Leslie Hendric Spits Gaol, Ford Lumban Soewito, Benfano Binus Graduated Program - Doctor of Computer Science Computer Science Department Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

Natural catastrophes have become one of the hottest subjects as a result of the harm they have caused to human structures. In recent years, research pertaining to the discipline of building damage assessment (BDA), which seeks to study the effect of damage on structures, has grown substantially. This bibliometric study seeks to determine the state of the art in the subject of Building Damage Assessment Using Deep Learning by examining several papers. The method used (Donthu et al., 202) divides the steps of bibliometric analysis into four parts: identifying the field of research to be analyzed, identifying the methods used in bibliometric analysis, collecting research data with similar fields, and executing and analyzing the results of bibliometric analysis on the collected data. In the data collecting and analysis portion (Garza-Reyes, 2015), the identification of data search keywords, the execution of data searches, the enhancement of search results, statistical data processing, and data analysis are carried out. Several research questions pertaining to trends, influences, themes, authors, and other crucial components of doing research in the area of building damage assessment are answered using these techniques. The outcome is the mapping and resolution of all research problems, as well as the acquisition of bibliometric analysis-related data. The provided findings have been mapped into several types of visualization, including network % clustering graphs, overlay graphs, and density graphs, as well as other forms of visualization that will aid researchers in comprehending the study circumstances in the field. It is envisaged that this bibliometric study would make it simpler for scholars to do research in related topics. © 2022 IEEE.

关键词： Damage detection

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