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2nd International Conference on Sciences and Technology Applications, ICOSTA 2020

作者： Fitriasari, N.S. Anzani, L. Widiyanto, K. Lukman Apriansyah, M.R. Setiawan, M.A. Asnawiah, L.P. Shafa, M.G. Marine Information System Study Program Universitas Pendidikan Indonesia Geography Study Program Universitas Indonesia

Knowledge repository is commonly used to store and manage explicit knowledge from knowledge worker. The development of internet of things (IoT) in Industry 4.0 enables the improvement of quality and quantity of knowledge repository. This study focuses on developing IoT-based knowledge repository as a means to support knowledge integration within The Marine information system study program. The study provides architectural designs of a knowledge repository that consists of 5 knowledge domains of marine information system and the design of IoT-based knowledge repository, both of which are based on reviews on relevant literatures and focus group discussion. The solution offered by the current study allows knowledge integration through Community of Practice as the provider and the user of the knowledge. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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The development of web-based virtual learning on computer network courses in assisting distance learning process in the information system study program the open university

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AIP Conference Proceedings 2024年第1期3048卷

作者： Andri Suryadi Dian Nurdiana Dyah Paminta Rahayu Information System Study Program Faculty of Science and Technology Open University South Tangerang Indonesia Mathematics Study Program Faculty of Science and Technology Open University South Tangerang Indonesia

Learning process at the information systems study program in the Open University is independently performed and integrated with printed teaching materials that have been provided for students. However, printed teaching materials alone are not enough to assist the students to learn detachedly. Many students need additional explanations related to the contents of the printed teaching materials. Therefore, an interesting virtual application is required to assist the students to conveniently learn the printed teaching materials, anytime anywhere. The expectant application is a web-based virtual learning application, while the courses that would be presented in the form of web-based virtual learning are those requiring virtual explanation like computer network courses. In making this web-based virtual learning application, the researcher would use the SDLC (software development lifecycle) method. Prior to making the application, a design is required in which this design would be sent to the application developer. In this study, the results of the conducted design are an application design document that could be understood 100% by the developer, so that it could be used as a reference in developing this virtual learning application.

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IoT-based Knowledge Repository Design for Supporting Knowledge Integration within the Marine information system study program

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Journal of Physics: Conference Series 2021年第1期1811卷

作者： N S Fitriasari L Anzani K Widiyanto Lukman M R Apriansyah M A Setiawan L P Asnawiah M G Shafa Marine Information System Study Program of Universitas Pendidikan Indonesia Geography Study Program of Universitas Indonesia

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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Exploring WeTV Application with Naïve Bayes, Decision Tree, and Random Forest Classifiers for Sentiment Analysis

Exploring WeTV Application with Naïve Bayes, Decision Tree,...

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2024 International Visualization, Informatics and Technology Conference, IVIT 2024

作者： Mareby, Yeremia Steven Putra Desanti, Ririn Ikana Universitas Multimedia Nusantara Information System Study Program Jakarta Indonesia

ISBN: (纸本)9798350354447

In 2023, Indonesia has experienced a notable increase in internet usage, particularly in the realm of digital content payments. This rise has consequently led to a substantial growth in the use of streaming applications, which are vital for providing entertainment to various communities by enabling content streaming on their devices. This research focuses on analyzing the general sentiments expressed in user reviews of WeTV, a popular streaming platform in Indonesia. To achieve this, the study employs three machine learning algorithms: Naïve Bayes, Decision Tree, and Random Forest. Among these algorithms, Naïve Bayes demonstrates the highest performance, achieving an accuracy of 79.1%, followed by Random Forest and Decision Tree. The results indicate that a significant portion of user sentiments towards the WeTV application are predominantly negative. The high accuracy of the Naïve Bayes model will be utilized for deployment on a basic website accessible to users, aiming to provide insights into user opinions and improve the overall user experience on the platform. © 2024 IEEE.

关键词： Adversarial machine learning

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Generation Z: How Acceptable Health Application for Us? 13

Generation Z: How Acceptable Health Application for Us?

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Mulia, Johan Kristian Wella Universitas Multimedia Nusantara Information System Study Program Tangerang Indonesia

ISBN: (纸本)9798350373332

Over the course of the last three years, the whole global population has been impacted by a COVID-19 epidemic. As a result of the ongoing pandemic crisis, all human activities have been halted, including remote office work and the closure of crowded locations to further prevent the spread of the virus. The COVID-19 pandemic has led to a significant increase in medical activities, thereby necessitating the employment of digital technology to provide support in managing the situation. One such technology is the Halodoc application, which enables the public to access medical aid without the need for direct contact. This study aims to examine the public's acceptance of the Halodoc application as a result of its usage. The intended participants are those belonging to Generation Z, born between the years 1995 and 2015. Generation Z was selected due to their status as digital natives, meaning they were born into a world where technology is prevalent and constantly evolving. The study utilized the Structural Equation Model (SEM) method to examine the level of acceptance of Generation Z towards the Halodoc application. The Technology Acceptance Model (TAM) was employed as a reference model for this analysis. The analysis will determine whether the model proposed in this study is accepted. This will be done by conducting tests using Structural Equation Modeling (SEM) and presenting graphical results from the questionnaire. By doing these tests, it may be determined whether the Halodoc application is deemed acceptable by Generation Z. © 2024 IEEE.

关键词： COVID-19

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Customer Loyalty Prediction and Customer Segmentation: A Random Forest Prediction and PCA-Integrated K-Means++ Approach 4

Customer Loyalty Prediction and Customer Segmentation: A Ran...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Natalia, Geovanny Cellyn Desanti, Ririn Ikana Universitas Multimedia Nusantara Information System Study Program Tangerang Indonesia

ISBN: (纸本)9798331506490

This research discusses the prediction of company loyalty. Every company typically has a loyalty program in place. Customer loyalty is a key driver of business success, especially in competitive retail environment. Understanding the condition of customer loyalty presents a unique challenge for companies. It is also crucial for companies to understand the characteristics of their loyal customers. This research proposes a predictive model to assess the loyalty of a company's customers and builds a clustering model from the predicted loyal customers. Using the company's transaction data from January 1, 2023, to September 31, 2024, a predictive Random Forest model is developed. The Random Forest model is evaluated using ROC-AUC, accuracy, F1 Score, and Recall, showing ROC - AUC 0.93, accuracy of 87%, a F1 Score of 91 %, and a recall of 92 %. performance. The Random Forest model will generate customers predicted to be loyal and those predicted to churn. K-Means++ clustering will be built using the data of customers predicted to be loyal by the Random Forest model. Clustering is implemented by PCA (Principal Component Analysis) to reduce data dimensionality. Loyal customers are divided into 3 clusters, each displaying its distinct characteristics. This research identifies customers who have the potential to become loyal, and the clustering results reveal the characteristics of each cluster. Companies can leverage the findings of this research to develop better strategies to enhance customer loyalty. © 2024 IEEE.

关键词： Sales

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Topic Modeling of Raja Ampat Tourism on TripAdvisor Sites Using Latent Dirichlet Allocation 4th

Topic Modeling of Raja Ampat Tourism on TripAdvisor Sites Us...

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4th International Conference on Electronics, Biomedical Engineering, and Health Informatics, ICEBEHI 2023

作者： Sugiarto, Dedy Mulya, Dimmas Sari, Syandra Ariwibowo, Anung B. Mardianto, Is Aulia, Muhammad Azka Putri, Fitria Nabilah Jubaidah, Ida Maulana, Arfa Nadia, Alya Shafa Information System Study Program Trisakti University Jakarta Indonesia Informatics Study Program Trisakti University Jakarta Indonesia

ISBN: (纸本)9789819714629

In response to the burgeoning interest in tourist destination sentiment analysis, this study focuses on Indonesia’s renowned Raja Ampat. Our primary objective is to employ Latent Dirichlet Allocation (LDA), a topic modeling technique, to delve into sentiments expressed in a corpus of 5,227 TripAdvisor documents concerning Raja Ampat. Leveraging the Vader library for data preprocessing, our analysis reveals a dominant trend of positivity, with 80% of sentiments classified as positive, while 15.8% are neutral, and 4.2% are negative. Through systematic experimentation with Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) feature representations, two major topics emerge: ‘Environmental Tourism’ and ‘Snorkeling & Services.‘ Remarkably, the LDA model attains coherence scores of 0.6005 (BoW) and 0.4148 (TF-IDF), underscoring the robustness of our analysis. These findings not only contribute valuable insights into the prevailing sentiments and dominant topics associated with Raja Ampat tourism but also offer practical implications for the industry. The tourism sector in Raja Ampat can leverage these insights to enhance services and experiences, ultimately facilitating sustainable tourism development in this ecologically vital region. This research thus serves as a significant step toward promoting informed decision-making and improving tourist experiences in Raja Ampat and similar destinations, fostering sustainable growth and preservation of natural beauty. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sentiment analysis

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Increasing Customer Loyalty in Food and Beverage MSMEs Through Social CRM 4

Increasing Customer Loyalty in Food and Beverage MSMEs Throu...

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4th International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Gui, Gabriel Condrobimo, Andreas Raharto Sriwardiningsih, Enggal School Of Information System Bina Nusantara University Information System And Management Jakarta Indonesia School Of Information Systems Bina Nusantara University Information Systems Study Program Jakarta Indonesia Business School Bina Nusantara University Management Study Program Jakarta Indonesia

ISBN: (纸本)9798350309508

Micro, Small, and Medium Enterprises (MSME) in the food and beverage (F&B) industry have mostly used social customer relationship management (SCRM) approaches to promote customer loyalty (CL). This method is extensively adopted in the digital era since it is thought to improve consumer satisfaction. The primary goal of this study is to examine how SCRM affects customer loyalty through the mediation of customer engagement (CE), trust (TR), and enhanced customer service (ICS). This research received data from 350 respondents using Google Form and analyzed it using SmartPLS (SEM-PLS). The obtained data reveal that all hypotheses have a favorable and substantial effect on SCRM, CE, TR, ICS, and CL. © 2024 IEEE.

关键词： Sales

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