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检索条件"机构=Information Systems Study Program Universitas Multimedia Nusantara"

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A Comparative study of Traditional and Deep Learning Models Using Emotional Customer Data in Automotive Sales Forecasting

A Comparative Study of Traditional and Deep Learning Models ...

引用

2025 International Conference on Advancement in Data Science, E-learning and information System, ICADEIS 2025

作者： Godata, Gempar Bambang Irmawati Universitas Multimedia Nusantara Information Systems Study Program Banten Indonesia

ISBN: (纸本)9798331513320

This study investigates the effectiveness of traditional and deep learning models in predicting automotive sales outcomes using customer acoustic and emotional parameters extracted from sales call recordings. A primary dataset of sales conversations was processed using an internal tool, which converts audio into numerical data based on 11 acoustic features such as pitch, volume, and speech rate. Traditional machine learning models, including AdaBoost, Support Vector Machine (SVM), and Random Forest, were compared with deep learning models like Dense Neural Networks (DNN) and TabNet. The optimized DNN model achieved the highest performance, with an F1-Score of 0.7298, balancing precision and recall effectively. TabNet provided competitive results with an F1-Score of 0.7237, offering additional benefits of feature interpretability through its attention mechanism. These findings suggest that while traditional models, particularly AdaBoost, perform adequately, deep learning models, especially DNN and TabNet, are better equipped to capture the complex, non-linear relationships in acoustic and emotional data. The results provide actionable insights for optimizing sales strategies through data-driven decision-making. It offers a foundation for future advancements in predictive analytics within automotive industries using voicebased datasets. © 2025 IEEE.

关键词： Contrastive Learning

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Detection of Watermarks in Digital Images Using Filter Techniques and Convolutional Neural Networks

Detection of Watermarks in Digital Images Using Filter Techn...

引用

International IoT, Electronics and Mechatronics Conference, IEMTRONICS 2024

作者： Angelica, Vanessa Irmawati Information Systems Study Program Universitas Multimedia Nusantara Banten Tangerang Indonesia

ISBN: (纸本)9789819747832

The forgery of watermark images poses a significant threat to digital works’ visual integrity and authenticity. This research focuses on developing a method for detecting forged watermark images by comparing the performance of two conventional approaches: the Gabor and the Prewitt filters. In addition, a Convolutional Neural Network (CNN) is employed as a classifier to model complex features that may be challenging to identify using traditional detection methods. The Gabor and Prewitt filters extract texture and edge features from the images. Experiments were conducted on a dataset containing various forged watermark images with different levels of complexity. Performance evaluation results were measured by assessing accuracy during validation and testing, True Positive Rate (TPR), False Negative Rate (FNR), and the ROC–AUC curve. The research findings indicate that the Prewitt filter outperforms the Gabor filter, achieving a testing accuracy of 88.32%, a TPR of 82.94%, an FNR of 17.05%, and an ROC–AUC value of 0.884 for validation accuracy. These results demonstrate the potential of the Prewitt filter and CNN as a practical approach to addressing the challenges of detecting increasingly complex watermark image forgeries. This study contributes to developing watermark image authenticity detection methods by combining conventional approaches with deep learning technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Efficiency Comparison of Employee Management Automation Using Selenium and Playwright: A Case study of Technology Consulting Company

Efficiency Comparison of Employee Management Automation Usin...

引用

2024 International Conference on information Technology systems and Innovation, ICITSI 2024

作者： Apriansah, Muhammad Rio Desanti, Ririn Ikana Universitas Multimedia Nusantara Information Systems Study Program Tangerang Indonesia

ISBN: (纸本)9798331511470

The research employed a case study methodology to investigate a technology consulting company. The company implements an employee management application to facilitate the approval procedure for granting employee benefits. Before approval, the procedure of gathering and analyzing allowance data is conducted. The current procedure requires manual processing of each task individually, with each permission list taking approximately 3 to 5 minutes. This approach is not only time-consuming but also highly prone to errors, leading to inefficiencies and inconsistent outputs that fail to meet the company's standards. An effective solution to this problem is to develop an automated program using Selenium and Playwright, alongside the Python programming language. The program will be created following the agile software development methodology, and its testing will involve both black box testing and User Acceptance Testing (UAT). After successfully developing both automation programs, a detailed comparison was conducted using data from 10 allowance lists. Testing showed that the Selenium automation program completed all operations in 55.03 and 49.25 seconds, while the Playwright program required only 47.14 and 47.71 seconds. These results indicate that the Playwright automation program demonstrates a higher level of efficiency compared to Selenium. © 2024 IEEE.

关键词： Acceptance tests

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IT Governance Evaluation Using COBIT 2019 Framework in A Manufacturing Company 7

IT Governance Evaluation Using COBIT 2019 Framework in A Man...

引用

7th International Conference on New Media Studies, CONMEDIA 2023

作者： Kwak, Patrick Desanti, Ririn Ikana Universitas Multimedia Nusantara Information Systems Study Program Tangerang Indonesia

ISBN: (纸本)9798350307504

At this research, a case study approach was applied at a manufacturing company. The company is having problems with information technology governance and management. Many problems are found in the company's operational activities, and the systems that exist to support operational activities have not been used optimally. To address the company's issues, an evaluation and assessment of the capability level in information technology governance and management will be conducted using the COBIT 2019 framework. Data will be gathered through interviews and literature reviews, resulting in qualitative data. The data collected can be used to identify appropriate domain procedures to address the company's problems. APO07, APO12, APO13, BAI08, and DSS05 are the domain processes that are used. The investigation on information technology governance and management discovered that procedures APO07, APO12, APO13, BAI08, and DSS05 stopped at level 2 with the 'Largely Achieved' category, indicating the presence of gaps in each of these processes. Six recommendations for procedure APO07, three recommendations for APO12, three recommendations for APO13, two recommendations for BAI08, and four recommendations for DSS05 have been offered. The recommendations will be delivered to the company to assist in addressing the gaps in these procedures. © 2023 IEEE.

关键词： Industrial research

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The Implementation of Genetic Algorithm to Solve Final Project Defense Scheduling Problem 8

The Implementation of Genetic Algorithm to Solve Final Proje...

引用

8th International Conference on Business and Industrial Research, ICBIR 2023

作者： Ilham, Gema Desanti, Ririn Ikana Suryasari Universitas Multimedia Nusantara Information Systems Study Program Tangerang Indonesia

ISBN: (纸本)9798350399646

Scheduling of final project session is one of the problems that need to be handled in the information systems study program at universitas multimedia nusantara (UMN). The problem arises because there are some differences in the available schedules of the prospective examiners. Besides that, another factor that is taken into consideration when the study program determines prospective examiners is the expertise of the examiners. Therefore, the topic of this research was raised to be able to solve the problem. The system development method used in this study is prototyping. The algorithm that will be used to create the scheduling system is a genetic algorithm to determine the final schedule for students' final project defense based on the availability and expertise of the examiners. The proposed system will handle two main processes, namely determining the appropriate examiner for each student based on the student's final project topic and determining a final defense schedule. The proposed system will be built on a web-based platform and have 3 access levels such as the head of the study program, lecturers, and students. The proposed system will provide the best recommendation scheduling results from all the possibilities. The recommended schedule will be used as the final schedule of the student's final project defense. © 2023 IEEE.

关键词： Genetic algorithms

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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...

引用

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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Generation Z: How Acceptable Health Application for Us? 13

Generation Z: How Acceptable Health Application for Us?

引用

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

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

引用

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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In-Depth Examination of Student Attrition in Higher Education Through Decision Tree, Random Forest, and XGBoost Algorithms 11

In-Depth Examination of Student Attrition in Higher Educatio...

引用

11th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2024

作者： Aurelio, Clement Setiawan, Johan Universitas Multimedia Nusantara Faculty of Engineering and Informatics Information Systems Study Tangerang Indonesia

ISBN: (纸本)9798350355314

Customer Customer churn, a critical metric for businesses reliant on customer loyalty, involves the loss of customers from a company. This study examines churn among college and university students, leading to financial losses, decreased accreditation, and diminished reputations for educational institutions, ultimately reducing their appeal to prospective students. To identify factors contributing to student churn and classify students as likely to churn or not, this research utilizes Decision Tree, Random Forest, and XGBoost algorithms. These classification algorithms, with XGBoost and Random Forest enhancing Decision Trees through boosting and bagging techniques, respectively, are well-suited for the task. The data was sourced from the Academic information Bureau and processed before model development. The model with the best performance will predict student churn on new, unseen data. Findings indicate varying performance metrics for each model, including accuracy, precision, recall, and F1-score. The XGBoost model excelled, achieving an accuracy of 98%, precision of 99%, recall of 96%, and an F1-score of 97%, significantly improving over the base Decision Tree model across all metrics. Notably, all models showed better performance in predicting the majority class (not churn) compared to the minority class (churn). © 2024 IEEE.

关键词： Sales

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An Integrated Approach for Sentiment Analysis and Topic Modeling of a Digital Bank in Indonesia using Naïve Bayes and Latent Dirichlet Allocation Algorithms on Social Media Data 4

An Integrated Approach for Sentiment Analysis and Topic Mode...

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4th International Conference on Big Data Analytics and Practices, IBDAP 2023

作者： Setiawan, Johan Milenia, Anastasia Faza, Ahmad Universitas Multimedia Nusantara Information Systems Study Tangerang Indonesia

ISBN: (纸本)9798350300192

Social media provides a public platform for expressing complaints and opinions. Researchers can use text mining techniques such as sentiment analysis and topic modeling on social media data to compare features and gauge public opinion on competing digital banks in Indonesia. The aim of this study is to classify sentiments and identify topics in social media data related to a specific digital bank. To accomplish this, the Naïve Bayes algorithm is used for sentiment analysis, while Latent Dirichlet Allocation is used for topic modeling. The social media data is sourced from Twitter and Instagram for Line Bank digital bank. The study finds that the Naïve Bayes algorithm performs well in classifying sentiments, achieving a maximum F1 score of 0.863. Positive sentiments are more prevalent in Twitter data, while negative sentiments are more prevalent on Instagram. Topic modeling using Latent Dirichlet Allocation algorithm identifies four optimal topics for positive sentiment and five for negative sentiment. The coherence value obtained is 0.426279 for positive sentiment and 0.397232 for negative sentiment. © 2023 IEEE.

关键词： Sentiment analysis

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