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5th International Conference on Intelligent Information Technology, ICIIT 2020

作者： Fernando, Anthony Bryan Shreevasta Desprianto, Ferry Gaol, Ford Lumban Oktavia, Tanty Mobile Application and Technology-School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department-School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450376594

Cashless Transaction describes as a state where financial transactions are not bound by physical form such as banknotes or coins, but rather trough transfer of digital information (electronic representation of money). Cashless Transaction exists because of fast majorities of human transaction evolved towards digital environment, this creates an opportunity where legal money exists, recorded, and exchanged in digital form. Thus cashless transaction will make less effort in currency operation cost, keep on track with expenses, connected anytime everywhere as long as connected with internet, and eliminates the needs of banknotes or receipt. Cashless Transaction have done over the past few years making life easier by reducing a number of objects to merge into 1 functional object that could make payments faster and simpler starting with check the first mark of cashless transaction that's still being used nowadays, but cashless transaction doesn't stop evolving in this modern era where money is demonetized, cashless transaction is moving into the digital grid resulting a breakthrough mobile wallets and online transfer that offers people flexibility because its technology advancement makes it possible to make payments anytime and anywhere since it's only depend on a secure network(internet) and a platform, therefore cashless transaction could be done as fast as possible saving precious time. Cashless transaction has some of key components such as security, UX, Functionality, Performance and Data Integrity. Five key components could make cashless transaction become reality but also poses a different threat to cashless transaction. Hacking is one of them. Hacking could be done by anyone that have the ability to compromise the transmission, the transmission needs to have more data protection thus the cashless transaction could be done safely and free of risk. © 2020 ACM.

关键词： Personal computing

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Modified TF-Assoc Term Weighting Method for Text Classification on News Dataset from Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第1期48卷

作者： Izzah, Imroatul Khuluqi Girsang, Abba Suganda Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Text classification is a process of locating text documents automatically into categories based on the text content. In-text classification, there is a stage that has an important role in giving the value of importance to each document, that is term weighting. In the researchers’ previous study, a new supervised term weighting (TF-Assoc) was introduced with the concept of association to optimize term weighting distribution in a case of multiclass classification. To improve the performance of text categorization, this paper proposes a term weighting scheme with a modified association concept, that is mTF-IDF-Assoc. The proposed term weighting scheme considered Document Length (DL). DL was used to normalize the term frequency by dividing it by the length of the document's vector and then formulting IDF and Assoc in calculating the weight of each word. The results showed that mTF-IDF-Assoc implemented with SVM classifier and 10-fold cross-validation technique could outperform the TF-IDF, TF-ICF, and TF-Assoc weighting scheme with an average accuracy of 82.322%. © 2021, IAENG International Journal of computer science All Rights Reserved.

关键词： Classification (of information)

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Fit and Forecasting Indonesia Banking Stock Price During Covid-19 Pandemic Based on Logistic Differential Equation System 4

Fit and Forecasting Indonesia Banking Stock Price During Cov...

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2022 4th International Conference on E-Business and E-Commerce Engineering, EBEE 2022

作者： Noviantri, Viska Chandra, Andree Sulistio Yusof, Yuhani Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Centre for Mathematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Persiaran Tun Khalil Yaakob Gambang Pahang Kuantan26300 Malaysia

ISBN: (纸本)9798400700071

Banks are financial service companies that play an important role in channeling finances to folks who want capital for micro, small and medium establishments. The increase in bankrupt companies and unemployment in many sectors due to the uncertain condition during the Covid-19 pandemic will affect bank performance which may change their stock price dynamics. This study analyzes the stock price fluctuation in banking companies using a logistic differential equation model since the fluctuation changes rapidly and has a nonlinear pattern. This model was finished by the fit and forecasting process;and completed by Mean Average Percentage Error (MAPE) value to see how well the model captures the stock price fluctuation. Furthermore, the closing stock price during the pandemic of the four banks with the largest number of assets in Indonesia's banking sectors is applied here to complete the fit and forecasting simulation. The results show that the MAPE average value of this model in the fit process is 6.331%. In other words, the logistic equation accurately represents banking stock price fluctuation. Next, the forecast process conducts to predict the stock price fluctuation for some periods. Thus, the fluctuation of stock prices in Indonesian banking during the Covid-19 pandemic can analyze further. © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Forecasting

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Data annotation system for intelligent energy conservator in smart building 3

Data annotation system for intelligent energy conservator in...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Pardamean, Bens Cenggoro, Tjeng Wawan Chandra, Bloomest Jansen Rahutomo, Reza Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department College of Letters and Science University of Wisconsin MadisonWI53706 United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

The concept of smart building includes the optimization of energy usage in a building. One of the possible solutions for this is to adaptively adjust appliances utilization according to activity level in the building. Thus, an intelligent activity estimation system needs to be developed. However, massive annotated dataset is necessary to train the system. Therefore, we propose a system that enables rapid data annotation to collect the massive dataset. With the proposed system, an image can be annotated within 4.8 seconds in average. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Historic preservation

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Analysis of deep learning architecture for patch-based land cover classification

Analysis of deep learning architecture for patch-based land ...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Hisyam Fahmi Wina Permana Sari Mathemathics Department Faculty of Science and Technology Universitas Islam Negeri Maulana Malik Ibrahim Malang Malang Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

In recent years, the usage of computer vision methods for mapping land cover and land use has increased. Patch-based change detection may produce much superior results than pixel-based change detection and generate accurate change maps. Convolutional Neural Network (CNN) is an excellent option for remote sensing applications using hyperspectral data. The training of models using deep learning techniques takes a lot of time. This research aims to determine the most efficient deep learning model for patch-based land-cover classification by examining and comparing three CNN architectural models for land cover classification: LeNet-5, VGG-16, and ResNet-50. EuroSAT data derived from Sentinel-2A remote sensing imagery are utilized in this work. Comparing the three CNN architectures indicates that ResNet-50 has the highest validation accuracy, with a testing accuracy of 0.877, and a training time that is neither too quick nor too slow. The LeNet-5 model has the quickest training time but the lowest accuracy. VGG-16 has the longest training period yet has the highest test score of 0.878.

关键词： Training Deep learning Electrical engineering computer architecture Convolutional neural networks Information technology Testing

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Recommendation Systems in Education : A Bibliometric Analysis

Recommendation Systems in Education : A Bibliometric Analysi...

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International Conference on Business and Industrial Research (ICBIR)

作者： Hendra Mayatopani Dina Fitria Murad Nor Adnan Yahaya School of Computing University Malaysia of Computer Science & Engineering Selangor Malaysia Information System Pradita University Tangerang Indonesia Information Systems Department Binus Online Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350383027

ISBN: (纸本)9798350383034

In today's digital age, recommendation systems have become an important tool in many sectors, including education. This study presents a comprehensive bibliometric analysis of the literature related to recommendation systems in an educational context. Using major scientific databases and bibliometric analysis tools, we evaluate publications from the last 5 years to identify research trends, collaboration patterns, and methodological developments. Our results show a significant increase in the number of publications and interest in this field, with a wide variety of research themes ranging from personalized learning to optimization of educational resources. The most dominant analytics techniques include collaborative filtering and content-based filtering, while hybrid and deep learning approaches show growing potential. In addition, we identified geographic and institutional concentrations of research, as well as a set of challenges such as data privacy and the need for adaptation to learner dynamics. This research underscores the importance of recommendation systems in improving tailored educational experiences and supporting data-driven academic decisions. We also offer insights for future research directions, including the need for interdisciplinary research and closer international collaboration.

关键词： Deep learning Filtering Collaborative filtering Education Bibliometrics Market research Information age International collaboration Recommender systems Optimization

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Self-Assessment for Personal Growth: Evaluating the Effectiveness of the BeeBest Mobile Application

Self-Assessment for Personal Growth: Evaluating the Effectiv...

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International Conference on ICT For Smart Society (ICISS)

作者： Yasinta Indrianti Sasmoko Sonya Rapinta Manalu Lukas Tanto Kurniawan Abu Yazid Abu Bakar Davy Ronald Hermanus Entrepreneurship Department Universitas Agung Podomoro Research Interest Group in Educational Technology Jakarta Indonesia Primary Teacher Education Department Faculty of Humanities Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Mobile application & Technology School of Computer Science Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Faculty of Education Universiti Kebangsaan Malaysia Malaysia Computer Science Department Bina Nusantara University Research Interest Group in Educational Technology Bandung Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Self-assessment in personal growth is an important element in evaluating the effectiveness of mobile applications such as BeeBest, which are designed to support the concept of smart education by helping students with time management and self-development. This study evaluates the relationship between multiple intelligences, entrepreneurial awareness, and students' academic performance through the use of the BeeBest application. Using the SEM-PLS method, the results show that Intrapersonal Intelligence (coefficient 0.55) and Logic Intelligence (coefficient 0.45) have a significant influence on academic achievement. Entrepreneurial Mindfulness also shows a positive contribution (coefficient 0.35), reflecting the importance of focus, creativity, and stress resilience in supporting academic success. This model explains 60% of the variability in academic performance, reinforcing the importance of a personalized approach in smart education. The BeeBest application is proven to be effective in supporting self-management and academic growth through features that monitor student potential and provide learning recommendations based on intelligence and entrepreneurial profiles.

关键词： Entrepreneurship Educational technology Mobile applications Logic Stress Monitoring Creativity Resilience

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IndoBERT for classifying hate speech in Twitter

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AIP Conference Proceedings 2024年第1期3026卷

作者： Hendri Santosa Fatahillah Rachman Stanley Armando Austen Christianto Abba Suganda Girsang Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Any form of communication that expresses hatred, prejudice, or hostility toward a particular individual or group of people based on attributes such as their race, religion, ethnicity, nationality, gender, sexual orientation, disability, or other protected characteristics is considered hate speech. Hate speech can be verbal, written, or symbolic. Hate speech can take many forms, and it often involves derogatory language, offensive stereotypes, or the incitement of violence or discrimination against the targeted individuals or groups. The content of hate speech is easy found in forum or discussion in social media include twitter. Twitter is a microblogging-based virtual entertainment where clients can peruse and compose text called tweets or tweets. This exploration executes order of disdain discourse in media Twitter utilizing IndoBERT. IndoBERT is the Indonesian form of BERT model utilizing over 220M words. It was a Convolutional Neural Network-based algorithm that had been modified. Th highlight extraction in Transformer isn’t finished by convolution utilizing a part like CNN, however includes an encoder and decoder. The outcome demonstrates IndoBERT’s excellent ability to categorize hate speech.

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Predicting User Preferences with Neural Network Learning to Rank Method 5

Predicting User Preferences with Neural Network Learning to ...

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5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Qomariyah, Nunung Nurul Fajar, Ahmad Nurul Bina Nusantara University Faculty Of Computing And Media Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program-Master Of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

User preference learning has been around for many years. This is a common problem arise in e-commerce system, where the companies need to understand their customers in order to sell the correct products to their target customers. The user preference is also needed in the movie domain area, as the film companies who produce the movie also need to understand how the customers value their movie products, whether the market like it or not. This can help them to predict the best components for the future movie. In order to produce the recommendation to the customer, we can show a list of item in a correct order, sorted from the most relevant/the most liked one to the most irrelevant item. In this paper, we utilize the Learning to Rank (LTR) approach from information retrieval domain area to learn the user preference in recommender system domain. We applied LTR with pairwise approach from the two most popular algorithms in this area, i.e. RankNet and LambdaRank, on 25, 345 user ratings from 5, 000 movie dataset and evaluate both algorithms. We used a Neural Network architecture as the learning mechanism. We also measuring the speed performance of the two algorithms. © 2021 IEEE.

关键词： Sales

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Application of waterfall methods in a product registration

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AIP Conference Proceedings 2024年第1期3024卷

作者： Kirani Qothrunada Aswin Felix Indra Kurniadi Samsul Arifin Department of Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Registration of a new product is one of the tiring jobs if done manually. A company’s product registration process starts with collecting registration data and finally obtaining a Marketing Permit Number (NIE) certificate. Unfortunately, the process of getting NIE is not done just once but repeatedly. This is what makes researchers develop a system that monitors the product registration process. The system created will be called Product Registration Monitoring phase 2 (Promon-2). Promon-2 itself is a development of the previous version by adding a validation feature. Making this system is done by the Waterfall method. The waterfall method is used because the process carried out in making this system needs proper design so that the system will not regularly experience major updates. The choice of the waterfall model is also because a rapidly changing system will mess up documents or the ongoing registration process. The results of the Promon-2 system are running well according to user feedback, namely PT. Kalbe Farma Tbk.

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