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The Influence of Digital Financial Literacy on Interest in Investing in the Capital Market of Millennial Generation Students 1

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4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Gaol, Ford Lumban Christopher Dermawan, Gerry Rafif, Muhammad Resnandya Rubianto, Rully Matsuo, Tokuro Doctor of Computer Science Binus Graduate Program Jakarta Indonesia School of Information System Bina Nusantara University Alam Sutera Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9789811974021

ISBN: (纸本)9789811974014

In today's environment, financial knowledge is vital and should be possessed by everyone. Numerous people are financially disadvantaged as a result of the COVID-19 pandemic. This serves as a reminder of the critical nature of financial investment in the future. However, many Indonesians, particularly students, are still unfamiliar with capital market investing. In reality, investing in the stock market can provide a variety of benefits, including financial security, capital appreciation, and passive income. Financial literacy concerns can result in a lack of financial awareness among pupils. Indeed, financial literacy is widely available online and can be accessed at any time and from any location. We employed a quantitative method with a non-experimental research design for this study. A questionnaire survey is used to obtain data. The goal of this study is to determine whether there is a correlation between student interest in capital market investment and their digital financial literacy. As a result, extensive action is required to increase student interest in the capital market, which is precisely what this study wants to do. According to our findings, students who have access to digital financial and investment information have increased their financial literacy and developed a greater interest in investing in the capital market. Individuals are also encouraged by social media trends to try their hand at investing in the stock market. Additionally, students prefer to invest in stocks and mutual funds over other investing options. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Investments

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The Effect of E-Commerce on Increasing Consumption Behavior During a Pandemic 4th

The Effect of E-Commerce on Increasing Consumption Behavior ...

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4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Gaol, Ford Lumban Tompunu, Sherina Gloria Larissa, Jessica Adine Wijaksana, Arista Prajnawira, Ferdianto Matsuo, Tokuro Binus Graduate Program—Doctor of Computer Science Jakarta Indonesia School of Information System Bina Nusantara University Alam Sutera Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789811974014

The rapid development of technology and ease of information today greatly affects all aspects of life. During the COVID-19 pandemic, technology played an important role in maintaining community activities continued smoothly despite being hampered by several health policies during the COVID-19 pandemic. Many phenomena develop with new technologies in several fields, one in the economic field is e-commerce. E-commerce is a site that makes it easier for buyers to get the goods desired by the community, as well as the various interesting promos that are the attractiveness of the community to constantly shop in e-commerce so that the community applies consumptive behavior. So that the problem that will be discussed in this study is the development of e-commerce technology that affects people’s behavior in shopping which tends to be consumptive and hedonic. The purpose of this study is to determine the impact or effect of the use of e-commerce on consumptive behavior during the pandemic period and provide an overview of the potential regarding e-commerce in the future. This research method is quantitative. The data collection technique used for this study is to use a questionnaire. Thus, this research is expected to minimize the negative impacts that can be caused by the increasing consumptive behavior of society during the pandemic and provide wise e-commerce use. The results of this study can be seen from calculations using a formula that respondents tend to be more extravagant during the pandemic and for future potential, respondents will certainly enjoy the convenience provided by e-commerce. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Use of E-Wallet as a Substitute for Physical Money in Transactions at Malls 4th

Use of E-Wallet as a Substitute for Physical Money in Transa...

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4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Kurniawan, Achmad Rizky Imam, Rafif Zhalifunnas, Rifqi Farhan Putra, Armando Rama Gaol, Ford Lumban Matsuo, Tokuro School of Information System Bina Nusantara University Alam Sutera Jakarta Indonesia Binus Graduate Program—Doctor of Computer Science Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789811974014

Several challenges have emerged as a result of the rapid spread of Covid-19 in Indonesia. To combat the pandemic, the government has also issued general guidelines for avoiding/limiting direct contact with common people who do not live with them. However, in some unavoidable cases, such as cash transactions, the social distancing has not yet been followed properly. These type of challenges can be avoided by the introduction of e-wallets and e-money. This research study examines the transactions made using e-wallets and e-money and how these methods are increasing sales in public places. The proposed research study has attempted to utilize both qualitative and a quantitative method to observe the customer behavior while using e-wallet as a substitute for physical money when making a transaction in a shopping mall. The result of the proposed research work will be compared with the existing literature to provide a conclusion based on existing research data and literature. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic money

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The Social Media Implications on the Sales of Business Products 3

The Social Media Implications on the Sales of Business Produ...

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3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Jacia, Elvina Dyan, Gabriel Ifan, Amelia Gaol, Ford Lumban Matsuo, Tokuro Bina Nusantara University Alam Sutera Department of Information System Indonesia Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9781665464871

The use of technology nowadays does not feel strange. Everyday people who use technology for their daily needs, starting with each other, seeking knowledge, can even earn income by doing business using technology. Social media is a tool that can be used by people to become with each other by creating, sharing, and exchanging information virtually through a network. However, in the current era, the notion of social media is not only about exchanging information, but also to support business products. The problem is how to compete tightly in this modern era in increasing the selling value of business products. The positive impact of social media for sales in business products are huge such as, increase sales, increase product promotion, greater market distribution. On the other side, the negative impact also takes part such as, the more competitors, the products compete fiercely in the market. This research aims to identify more the impact of social media on sales of business products that focused on business, benefit, and factor. The research method that we used is quantitative which focuses on the number of samples of respondents to support the research results and the results are in the form of a graphic that shows the answers of the respondents to the questionnaire. © 2023 IEEE.

关键词： Sales

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Synthetic Batik Pattern Generator Using Wasserstein Generative Adversarial Network with Gradient Penalty 6

Synthetic Batik Pattern Generator Using Wasserstein Generati...

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6th International Seminar on Research of Information technology and Intelligent Systems, ISRITI 2023

作者： Andriadi, Kus Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Yogyakarta55281 Indonesia

ISBN: (纸本)9798350358346

Batik is an Indonesian world cultural heritage. Batik consists of many kinds of patterns depending on where the batik comes from, Batik-making techniques continue to develop along with technology development. Among the batik making techniques that are widely used are hand-written, stamping, and printing. Batik motifs have been widely used as research material, especially in the field of artificial intelligence. The diverse appearance of batik motifs has attracted many researchers to carry out research on making synthetic batik patterns, one of which uses a Generative Adversarial Network. This paper presents a synthetic batik pattern model based on the Wasserstein Generative Adversarial Network with Gradient Penalty. This model has been proven to create new synthetic batik patterns quite well and almost identical with images provided in the dataset, with the notes if the dataset provided is large. © 2023 IEEE.

关键词： Generative adversarial networks

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Diversity-Optimized Group Extraction in Social Networks

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IEEE Transactions on Computational Social Systems 2024年第1期11卷 756-769页

作者： Hsu, Bay-Yuan Chen, Yi-Ling Ho, Ya-Chi Chang, Po-Yuan Chang, Chih-Chieh Shia, Ben-Chang Shen, Chih-Ya National Tsing Hua University Department of Industrial Engineering and Engineering Management Hsinchu30013 Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Hsinchu31040 Taiwan Fu Jen Catholic University Department of Business Administration New Taipei City24205 Taiwan National Taiwan University of Science and Technology Management Undergraduate Program Taipei10607 Taiwan National Tsing Hua University Department of Computer Science Hsinchu30013 Taiwan

In this article, we propose to study a novel research problem to boost group performance, that is, social-aware diversity-optimized group extraction (SDGE), which takes into consideration the two important factors: 1) group diversity and 2) social tightness. We prove the NP-hardness of SDGE and propose an effective algorithm, named group shrinking for diversity maximization (GSDM) with a performance guarantee, that is, GSDM is a three-approximation algorithm to the SDGE problem studied in this article. We further propose three effective pruning strategies that are able to boost the efficiency of GSDM but do not deteriorate its performance. We conduct extensive experiments on multiple large-scale real datasets to evaluate the performance of GSDM. The experimental results show that our proposed GSDM outperforms the other baseline approaches significantly, in terms of solution quality and efficiency. Moreover, the experimental results also confirm that our proposed pruning strategies indeed boost the efficiency of the algorithm. © 2014 IEEE.

关键词： Costs

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Sentiment Analysis of Hashtag Activism on Social Media Twitter

Sentiment Analysis of Hashtag Activism on Social Media Twitt...

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2023 International Conference on computer Science, Information technology and Engineering, ICCoSITE 2023

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

ISBN: (纸本)9798350320954

Currently, the performance of the police in Indonesia is often in the spotlight of the public with cases that occur, both on a national and regional scale, including personal experiences who also feel disappointed with the performance of the police. The cases, such as the rape of three children by a biological father in East Luwu, and the barrage of other cases that have occurred, in their handling have left the public disappointed, giving rise to the hashtag percumlaporpolisi on social media, especially on twitter. This hashtag became trending and was widely responded to by the public. Using free hashtag keywords, tweet data is collected using Twitter's API for sentiment analysis. The datasets obtained from the crawling results is the latest data with a total of 100 datasets. This study applies the naïve bayes algorithm using textblob which aims to determine the value of polarity and subjectivity. From the results of the analysis carried out, the words that appear most often are useless police, police chief, and policeman. While the results of the sentiment analysis that has been carried out are 40 percent positive sentiment, 40 percent are neutral sentiment and 10 percent are negative sentiment from the hashtags used. From these results, according to the keywords used, the percentage of negative sentiment should be greater than positive and neutral sentiment. It is necessary to conduct a more in-depth study related to the model used in the next research. © 2023 IEEE.

关键词： Sentiment analysis

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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CIMulator: Evaluating Neuromorphic Accelerators with HZO FefinFET-Based Synaptic Device 7

CIMulator: Evaluating Neuromorphic Accelerators with HZO Fef...

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7th International Conference on Green technology and Sustainable Development, GTSD 2024

作者： Le, Hoang-Hiep Lu, Hao-Yu Baig, Md. Aftab De, Sourav Lin, Ing-Chao Lu, Darsen D. Minghsin University of Science and Technology Department of Computer Science and Information Engineering Hsinchu Taiwan National ChengKung University Intelligent Computing Industrial Doctorate Program Tainan Taiwan Institute of Microelectronics National ChengKung University Tainan Taiwan National ChengKung University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350386073

In this study, we use the CIMulator platform to evaluate the performance of neuromorphic accelerators with novel Hf0.5Zr0.5O2 (HZO) ferroelectric fin field-effect transistor (FefinFET) as synaptic device. The MNIST handwritten digit dataset is used for training and inference processes in multilayer perceptron (MLP) neural networks. With the highly optimized synaptic device, the results demonstrate high training and inference accuracy, achieving up to 97% for the MLP, approaching the accuracy of software implementations. © 2024 IEEE.

关键词： Multilayer neural networks

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Determining Variables Associated with Annual Oil Palm Yield: An Explainable Gradient Boosting Approach 8

Determining Variables Associated with Annual Oil Palm Yield:...

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

作者： Elwirehardja, Gregorius Natanael Suparyanto, Teddy Miftakhurrokhmat Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta55282 Indonesia Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta55584 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 RRʹ. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed. © 2023 The Authors. Published by Elsevier B.V.

关键词： Machine Learning Oil Palm Yield SHAP XAI XGBoost

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