Engineering, Mathematics and Computer Science Journal (EMACS)
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.
Articles
12 Documents
Search results for
, issue
"Vol. 6 No. 3 (2024): EMACS"
:
12 Documents
clear
<![CDATA[American Sign Language Translation to Display the Text (Subtitles) using a Convolutional Neural Network ]]>
<![CDATA[Ramadhan, Muhammad Fajar]]>;
<![CDATA[Samsuryadi, Samsuryadi]]>;
<![CDATA[Primanita, Anggina]]>
<![CDATA[ Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS ]]>
Publisher : <![CDATA[Bina Nusantara University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.21512/emacsjournal.v6i3.11904
<![CDATA[Sign language is a harmonious combination of hand gestures, postures, and facial expressions. One of the most used and also the most researched Sign Language is American Sign Language (ASL) because it is easier to implement and also more common to apply on a daily basic. More and more research related to American Sign Language aims to make it easier for the speech impaired to communicate with other normal people. Now, American Sign Language research is starting to refer to the vision of computers so that everyone in the world can easily understand American Sign Language through machine learning. Technology continues to develop sign language translation, especially American Sign Language using the Convolutional Neural Network. This study uses the Densenet201 and DenseNet201 PyTorch architectures to translate American Sign Language, then display the translation into written form on a monitor screen. There are 4 comparisons of data splits, namely 90:10, 80:20, 70:30, and 60:30. The results showed the best results on DenseNet201 PyTorch in the train-test dataset comparison of 70:30 with an accuracy of 0.99732, precision of 0.99737, recall (sensitivity) of 0.99732, specificity of 0.99990, F1-score of 0.99731, and error of 0.00268. The results of the translation of American Sign Language into written form were successfully carried out by performance evaluation using ROUGE-1 and ROUGE-L resulting in a precision of 0.14286, Recall (sensitivity) 0.14286, and F1-score.]]>
<![CDATA[Integration of QFD, HOQ, Taguchi, and Kansei Engineering for Smart Desk Lamp Design ]]>
<![CDATA[Barus, Maranatha]]>;
<![CDATA[Harito, Christian]]>
<![CDATA[ Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS ]]>
Publisher : <![CDATA[Bina Nusantara University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.21512/emacsjournal.v6i3.12136
<![CDATA[This research focuses on the analysis of smart desk lamps. To the authors’ knowledge, there is no previous study about Voice of Customer regarding smart desk lamps in Indonesia, to know the requirements for a smart desk lamp from Indonesian customer using the QFD and House of Quality (HOQ) The overall value of the interaction matrix with HOQ, the relative weight results for each customer and functional requirements are obtained. The highest technical requirement from HOQ is automatic on/off. The Taguchi method is to find out the best design for each Kansei Engineering that has been created. The Kansei Engineering that have been created are easy to use, adjustable lamp, affordable price, modern design, hi-tech. Each Kansei Engineering produces the required design according to the S/N Ratio (signal to noise ratio). Based on the QFD questionnaire and design using Kansei Engineering, one best design was obtained, namely hi-tech. The design consists of 3 parts, namely the top, middle and bottom. At the top, the slim rectangular shape is preferable with lights position in the middle. Pole position is round, upright position. At the bottom, it has a circular shape.]]>
<![CDATA[Efficient Computation of Number Fractions from the Square Root of Two Using the A-B Goen Number Function Via the Ivan Newton (in) Series ]]>
<![CDATA[Goenawan, Stephanus Ivan]]>
<![CDATA[ Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS ]]>
Publisher : <![CDATA[Bina Nusantara University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.21512/emacsjournal.v6i3.11575
<![CDATA[The square root number of two is an irrational number. If it is an irrational number, the result cannot be written as a fraction of the numerator and denominator. Fractions that approach the square root value of two have a correlation with Goen's A-B numbers. The regularity of the A-B Goen number sequence can be formulated into the A-B Goen function which is built from the Ivan Newton series. In this research, it can be proven that the A-B Goen function from the Ivan Newton (IN) series is computationally more effective and efficient when compared to the A-B Goen generating function in producing A-B Goen numbers which in infinite sequence will approach the square root value of two.]]>
<![CDATA[Web Based Application Development for Creating Collaborative Project Using NodeJs ]]>
<![CDATA[Danaparamita, Muhammad]]>;
<![CDATA[Purwoyudo, Yordanka Andree Giovanni]]>;
<![CDATA[Darmawan, Dion]]>
<![CDATA[ Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS ]]>
Publisher : <![CDATA[Bina Nusantara University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.21512/emacsjournal.v6i3.11651
<![CDATA[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.]]>
<![CDATA[Building Customer and Product Networks with Cosine Similarity in Graph Analytics for Deep Customer Insight ]]>
<![CDATA[Albone, Aan]]>
<![CDATA[ Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS ]]>
Publisher : <![CDATA[Bina Nusantara University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.21512/emacsjournal.v6i3.11693
<![CDATA[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.]]>
<![CDATA[Effective Approaches for User Engagement Improvement in Mobile Health Applications: A Comprehensive Literature Analysis ]]>
<![CDATA[Philip, Samuel]]>;
<![CDATA[Hidayaturrahman, Hidayaturrahman]]>
<![CDATA[ Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS ]]>
Publisher : <![CDATA[Bina Nusantara University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.21512/emacsjournal.v6i3.11837
<![CDATA[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]]>
<![CDATA[Overcoming Overfitting in CNN Models for Potato Disease Classification Using Data Augmentation ]]>
<![CDATA[Prasetyo, Simeon Yuda]]>
<![CDATA[ Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS ]]>
Publisher : <![CDATA[Bina Nusantara University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.21512/emacsjournal.v6i3.11840
<![CDATA[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.]]>
<![CDATA[Machine Learning-Based Malicious Website Detection Using Logistic Regression Algorithm ]]>
<![CDATA[Pastika, Puan Bening]]>;
<![CDATA[Alamsyah, Alamsyah]]>
<![CDATA[ Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS ]]>
Publisher : <![CDATA[Bina Nusantara University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.21512/emacsjournal.v6i3.11844
<![CDATA[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.]]>
<![CDATA[Forecasting Poverty Ratios in Indonesia: A Time Series Modeling Approach ]]>
<![CDATA[Hidayat, Muhammad Fadlan]]>;
<![CDATA[Henryka, Diva Nabila]]>;
<![CDATA[Citra, Lovina Anabelle]]>;
<![CDATA[Permai, Syarifah Diana]]>
<![CDATA[ Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS ]]>
Publisher : <![CDATA[Bina Nusantara University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.21512/emacsjournal.v6i3.11968
<![CDATA[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.]]>
<![CDATA[An Implementation of Ordinal Probit Regression Model on Factor Affecting East Java Human Development Index ]]>
<![CDATA[Purnama, Mohammad Dian]]>
<![CDATA[ Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS ]]>
Publisher : <![CDATA[Bina Nusantara University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.21512/emacsjournal.v6i3.12094
<![CDATA[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%.]]>
