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Engineering, Mathematics and Computer Science Journal (EMACS)
Published by Universitas Bina Nusantara
ISSN : -     EISSN : 26862573     DOI : https://doi.org/10.21512/emacs
Core Subject : Science, Education, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics
Engineering, MAthematics and Computer Science (EMACS) Journal invites academicians and professionals to write their ideas, concepts, new theories, or science development in the field of Information Systems, Architecture, Civil Engineering, Computer Engineering, Industrial Engineering, Food Technology, Computer Science, Mathematics, and Statistics through this scientific journal.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 174 Documents
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Web Based Application Development for Creating Collaborative Project Using NodeJs Danaparamita, Muhammad; Purwoyudo, Yordanka Andree Giovanni; Darmawan, Dion
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.11651

Abstract

In an era marked by rapid technological advancements, the ease of accessing information has unlocked unprecedented opportunities for individuals to realize their aspirations. However, the mere acquisition of knowledge or technical skills does not always lead to success or recognition, particularly when striving to create something truly remarkable. The success story of The Beatles serves as a prime example of how collaboration can amplify individual talents and lead to extraordinary achievements. The band’s collective effort demonstrates that co-creation among individuals can produce results far greater than the sum of its parts. With the rise of digital connectivity, collaborative efforts have become more accessible than ever before. Advances in technology have bridged physical distances, allowing for global teamwork that transcends geographic barriers. Despite these advancements, successful collaboration hinges on building trust, which is often nurtured through transparency. Transparent communication fosters a culture of honesty, openness, and mutual respect, which, in turn, strengthens trust among collaborators. To address the need for enhanced collaboration in creative and technical projects, this paper proposes the development of a web-based application platform. The goal of this platform is to streamline the collaborative process and facilitate the collaborative process and improve outcomes. The results indicate that the platform effectively supports users in initiating projects with multiple collaborators by connecting them with others who share similar goals. Additionally, the platform fosters trust between project creators and potential members through its transparent display of project details.
Building Customer and Product Networks with Cosine Similarity in Graph Analytics for Deep Customer Insight Albone, Aan
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.11693

Abstract

Creating connections that allow users to share information, experiences, and product recommendations is the main goal of social networks. These networks are essential for assisting companies in comprehending user preferences, behavior, and buying trends. Graph theory is a crucial tool for analyzing and interpreting the intricate relationships found in such systems. It enables a structured depiction of users and their interactions through nodes and edges, offering important insights into the information and influence flow within the network. This idea is used in our customer network model to enhance recommendation and product engagement tactics. We can find users with similar interests and recommend pertinent products by examining the relationships between customers. Two customers are said to have closely aligned preferences and behaviors when their cosine similarity is greater than 70%. This makes it possible for the system to suggest goods that a customer has bought or given a high rating to another customer in the same similarity cluster. Additionally, we can track price sensitivity and market trends by mapping products within a product network. The network analysis enables us to see how a product's price impact on demand in comparison to similar items is affected if it is more expensive than comparable alternatives. All things considered, social network analysis and graph theory together provide a potent method for comprehending customer behavior, improving personalization, and refining marketing tactics for improved business results.
Effective Approaches for User Engagement Improvement in Mobile Health Applications: A Comprehensive Literature Analysis Philip, Samuel; Hidayaturrahman, Hidayaturrahman
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.11837

Abstract

Mobile health (mHealth) applications have become an integral part of our existence, offering multiple functions and a new level of user engagement. However, the competitive market presents difficulties for development teams attempting to attract and retain customers. User engagement is crucial to the success of mHealth applications, as it promotes interaction, adherence, and behavior modification. This paper presents a systematic literature review in order to investigate methods for enhancing user engagement in mHealth applications. The review identifies successful strategies from existing research and seeks to provide developers with guidance for creating engaging mobile applications. The selected studies are subjected to systematic searching, screening, data extraction, and quality evaluation, followed by narrative synthesis and thematic analysis. The findings emphasize the importance of gamification, design, personalization, social media integration, and push notifications in boosting user engagement. The review also emphasizes the need for experimental research to evaluate the efficacy of different user engagement strategies to achieve more accurate and reliable results. By addressing gaps and employing effective engagement strategies, mHealth applications can increase user satisfaction, encourage continued use, and improve health outcomes. The study lays the groundwork for future research and makes suggestions for designing strategies to increase user engagement in mHealth applications
Overcoming Overfitting in CNN Models for Potato Disease Classification Using Data Augmentation Prasetyo, Simeon Yuda
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.11840

Abstract

Classification of diseases in potato plants is crucial for agriculture to ensure quality and yield. Potatoes, being staple foods worldwide, are vulnerable to diseases that cause significant production losses. Early and accurate disease identification is essential. This study evaluates the impact of data augmentation on reducing overfitting in deep learning models for potato disease classification. Various CNN architectures, including VGG16, VGG19, Xception, and InceptionV3, were compared in transfer learning and fine-tuning phases. The "Potato Disease Dataset", consisting of 451 images across seven classes, was used. The dataset was split into training, validation, and test sets, and augmentation increased the training set from 360 to 2160 images. The results indicate that models trained with augmented data exhibited improved performance in terms of accuracy, precision, recall, and F1-scores compared to those trained without augmentation. The learning curves show that data augmentation helps in reducing overfitting and enhancing model stability. Data augmentation is crucial for developing robust deep learning models for potato disease classification. Future work will explore advanced augmentation techniques and other architectures to enhance model performance.
Machine Learning-Based Malicious Website Detection Using Logistic Regression Algorithm Pastika, Puan Bening; Alamsyah, Alamsyah
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.11844

Abstract

Cybercrime is an increasing threat that occurs while exploring the internet. Cybercrime is committed by cybercriminals who exploit the web's vulnerability by inserting malicious software to access systems that belong to web service users. It is detrimental to users, therefore detecting malicious websites is necessary to minimize cybercrime. This research aims to improve the effectiveness of detecting malicious websites by applying the Logistic Regression algorithm. The selection of Logistic Regression is based on its ability to perform binary classification, which is important for distinguishing between benign and potentially malicious websites. This research emphasizes a preprocessing stage that has been deeply optimized. Data cleaning, dataset balancing, and feature mapping are enhanced to improve detection accuracy. Hybrid sampling addresses data imbalance, ensuring the model is trained with representative data from both classes. Experimental results show that the Logistic Regression implementation achieves an excellent level of accuracy. The developed model recorded an accuracy of 92.60% without cross-validation, which increased to 92.71% with 5-fold cross-validation. The novelty of this research lies in the significant increase in accuracy compared to previous methods, demonstrating the potential to improve protection against malicious website threats in an increasingly complex and risky digital environment. This research makes an important contribution to the development of digital security detection technologies to address the ever-growing challenges of cybercrime.
Forecasting Poverty Ratios in Indonesia: A Time Series Modeling Approach Hidayat, Muhammad Fadlan; Henryka, Diva Nabila; Citra, Lovina Anabelle; Permai, Syarifah Diana
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.11968

Abstract

Poverty is one of the main problems still faced by Indonesia today. To help find the right solution, an annual prediction of the poverty rate in Indonesia is needed. This study uses data on the 'Ratio of the Number of Poor People in Indonesia per year from 1998 to 2023' obtained from data.worldbank.org. The prediction methods used in this study include the Naïve Model, Double Moving Average, Double Exponential Smoothing, ARIMA, Time Series Regression, and Neural Network, with a total of 26 models. Of the 26 models, only 19 models passed the model comparison stage. Based on the evaluation results using the RMSE, MAE, MAPE, and MDAE metrics, it was concluded that the NNETAR Neural Network model showed the best performance among the six methods used to predict the poverty ratio in Indonesia.
An Implementation of Ordinal Probit Regression Model on Factor Affecting East Java Human Development Index Purnama, Mohammad Dian
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.12094

Abstract

An instrument for measuring human development, the Human Development Index (HDI) looks at how well human development has been achieved in relation to a few fundamental aspects of quality of life. In 2023, East Java's HDI showed an increase in the last three years with the latest value of 73.38. Despite the increase, East Java still has the lowest HDI in Java and Bali. This situation suggests the need for an in-depth analysis of the factors that influence HDI. This study aims to identify factors that contribute to HDI to formulate more appropriate policies in the future. The data used is the HDI of East Java in 2023 with ordinal categories. To analyze the ordinal data, the ordinal probit regression method was applied. The results show that the percentage of poor people has a significant influence on HDI. In addition, the classification accuracy of the model is obtained with a value of 50.5%, which indicates that the accuracy of the model in predicting HDI into the right category reaches 50.5%.
Simulation Techniques in Sugarcane Transportation Model Using R Programming Language Yudistira, I Gusti Agung Anom; Pasaribu, Asysta Amalia; Aryusmar, Aryusmar
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.12344

Abstract

The R programming language is generally known for its strength in Monte Carlo simulations, and numerical computing. This study will try to utilize R for discrete event simulations, namely in transportation systems, especially sugarcane transportation. The purpose of this paper is to study the performance of the sugarcane transportation system from the plantation to the factory, by utilizing the R programming language. The things that will be studied are to obtain changes in the system parameters so that more optimal performance is obtained. These parameters include the time required for the sugarcane to be in the transportation system, the length of the sugarcane pile in the plantation before being transported, the amount of resources needed for all transportation activities (loading, transporting and unloading), the number of transport equipment, loading equipment and unloading equipment needed, so that the harvest target is met and the waiting time for the sugarcane to be milled is as minimal as possible. As well as the level of utility of all resources provided in the system. The stages in this study include 1) literature review, 2) describing the sugarcane transportation system, 3) building assumptions and system constraints, 4) designing the transportation system conceptually, 5) developing programming code, 6) model testing/verification, 7) model validation, and 8) conducting experiments on the model. The results of the analysis of the model output indicate that the “open source R” programming language can be effectively applied to model the sugarcane transportation system.
Multimedia Learning Material Impact on a Bootcamp Training Program at Merdeka Campus Heriyanni, Eileen; Qomariyah, Nunung Nurul
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.12392

Abstract

The use of multimedia in teaching and learning activities has been a practice that has been going on for a long time, especially since COVID-19. Teaching and learning activities using multimedia seems more interesting and able to increase student’s learning interest. This research highlights the use of multimedia material in technical bootcamp training program for non-technical background participants who are studying data analytics in certified independent studies program at Merdeka Campus. Data obtained by interview and discussion with Subject Matter Expert (SME) and study literature with Republic Indonesia National Standards for Higher Education, Outcome Based Education (OBE), Instructional System Design (ISD), Bloom Taxonomy, and Kirkpatrick Model to develop the program curriculum which later analyzed and processed to become the basis for multimedia material development. The evaluation of multimedia material implementation is assessed with a questionnaire which measures participants' perception of the multimedia material used in bootcamp activity to improve their knowledge and skill while studying data analytics. The conclusion of this research is to prove the impact of using multimedia material on the teaching and learning process through the results of participants’ average passing score which reached at 78.01 and the engagement of participants' commitment to the program which concluded in graduated percentage, reached at 73.33%.
Optimizer Comparison In Convolutional Neural Network For Real Time Face Recognition Elbert, Elbert; Wulandari, Meirista; Fat, Joni
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 1 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i1.12058

Abstract

Face recognition is one of the computer vision technologies that's used in many industries. Face recognition always used in various sector that require the verification of an individual identity. There are many ways that can be used to develop face recognition, one of them is convolutional neural network. Convolutional neural network (CNN) is a deep learning neural network that is created specifically to process and analyze visual data, such as images and videos. CNN have the ability to learn many features from visual data, making them highly effective for tasks like face recognition. There are many factors that can affect CNN performance including the optimizers that are used in the neural network. Optimizers are the algorithm that adjust weights of the neural network to minimize error between the predicted output and actual target. This study used 10 different subjects for face recognition. In this study, the CNN model uses a training algorithm called backpropagation then will compare 3 different types of optimizers. The optimizers that used in this study are Adaptive Momentum (Adam), Root Mean Square Propagation (RMSProp), and Stochastic Gradient Descent (SGD). The results of the comparison will be shown in the form of performance metrics. The performance metrics include correct classification rate (CCR) as well as the confusion matrix of each model. CNN model with SGD optimizers has the highest CCR of 97.07%.

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