cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Christian Harito
Contact Email
christian.harito@binus.edu
Phone
+6221-5350660
Journal Mail Official
aagung@binus.edu
Editorial Address
Universitas Bina Nusantara Jl. Kebon Jeruk Raya No.27 Kebon Jeruk, Jakarta Barat 11530
Location
Kota adm. jakarta barat,
Dki jakarta
INDONESIA
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
 8   9   10   11   12 
Bayesian Accelerated Failure Time Model for Risk Pregnancy Detection Dennis Alexander; Sarini Abdullah; Adam Fahsyah Nurzaman
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 5 No. 3 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i3.10540

Abstract

Preeclampsia (PE) also known as a hypertension during third trisemester of pregnancy. PE, is one of the most feared complications of pregnancy because it can potentially become serious complications in the future, including mother and fetus’s death. The goal of this study is other than to have a bettter undestanding about risk factor in pregnancy by modelling the relationship between several factors and the time until deliveries under the PE condition. Data on 924 patients at obstetric and gynecology department in Jakarta were used in the analysis. Accelerated Failure Time (AFT) model was proposed to indentify some risk factors that influenced the condition. Model parameters were estimated using Bayesian method. Due to imbalance data, undersampling method will be used as a pre-procesing stage. Ratio between PE and non-PE data will be 60:40. Flat prior and posterior sample will be used using MCMC simulation with 12,000 iterations (including 2,000 iterations as a burnin stage) to get a convergen result. The iteration was repeated for 100 times so that the chosen data from undersampling was not error and biased. A consistent result for credible interval of the mean result was considered as the factors that affect PE condition consistently. From this study, there are two factors that have consistent Credible Interval result, Body Mass Index (BMI) and Mean Arterial Pressure (MAP).
Predictive Modeling of Jakarta's Social Cohesion: GBDT Leads Comparative Analysis Muhammad Rizki Nur Majiid; Karli Eka Setiawan
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 5 No. 3 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i3.10602

Abstract

In this study, we address the challenge of predicting the Social Cohesion Index in Jakarta through a comprehensive analysis of machine learning models. Finding the most accurate and effective predictive model for this crucial urban evaluation task is the primary goal of our research. We use a variety of machine learning algorithms, comparing their performance using metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and computational cost. These algorithms include Gradient Boosted Decision Trees (GBDT), Polynomial Regression, Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). It should be noted that GBDT stands out as a top performer, regularly displaying outstanding accuracy with a competitive MAE of 0.692, RMSE of 0.887, and MAPE of 25.59%. The computational efficiency of GBDT is also impressive, with predictions taking only 0.05 seconds. These results underscore the potential of GBDT as a practical and precise tool for real-time assessments of social cohesion in large urban environments like Jakarta. The findings offer a data-driven way to guide policy decisions and community development activities, with important implications for urban planning and governance. Overall, this research emphasizes the promise of GBDT in boosting social cohesion evaluation approaches and increases our understanding of the application of machine learning in addressing complex urban difficulties.
Deep Transfer Learning for Sign Language Image Classification: A Bisindo Dataset Study Ika Dyah Agustia Rachmawati; Rezki Yunanda; Muhammad Fadlan Hidayat; Pandu Wicaksono
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 5 No. 3 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i3.10621

Abstract

This study aims to identify and categorize the BISINDO sign language dataset, primarily consisting of image data. Deep learning techniques are used, with three pre-trained models: ResNet50 for training, MobileNetV4 for validation, and InceptionV3 for testing. The primary objective is to evaluate and compare the performance of each model based on the loss function derived during training. The training success rate provides a rough idea of the ResNet50 model's understanding of the BISINDO dataset, while MobileNetV4 measures validation loss to understand the model's generalization abilities. The InceptionV3-evaluated test loss serves as the ultimate litmus test for the model's performance, evaluating its ability to classify unobserved sign language images. The results of these exhaustive experiments will determine the most effective model and achieve the highest performance in sign language recognition using the BISINDO dataset.
Analysis and Design of Ticketing Application System (Case Study: Bebek Kaleyo) Heri Ngarianto; Ahmad Fikri Mulaputra; Fanny Septiany; Tiara Delima
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 5 No. 3 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i3.10699

Abstract

Bebek Kaleyo is a restaurant chain with several outlets in the food business. Information technology (IT) equipment is used extensively in Bebek Kaleyo's operating processes, and any problems with the IT hardware are escalated to the IT department for solutions. However, each restaurant branch's reporting of interruptions is still done by hand. Based on these issues and the analysis, it is necessary to speed up the complaint procedure to guarantee quick repairs of IT equipment and successful execution of tasks. The research was conducted by examining the company's daily activities, and the results were used to develop a ticketing system for monitoring process improvements and reporting interruptions. A ticketing system that facilitates effective reporting of IT issues and monitors process improvements was designed and developed as part of this research using the prototype methodology. The installed ticketing system helps users at each branch report problems, keep track of repairs, and improves the efficiency and quality of service provided by the IT department in support of the supply and use of system and technology-related facilities. By using this ticketing system, Bebek Kaleyo hopes to improve overall process efficiency while streamlining the process of reporting IT concerns. This solution gives branch users the ability to discuss IT issues with the IT department in an efficient manner, which improves the organization's operational performance.
An Experiment to Prevent Malicious Actors from Compromising Private Digital Assets Over a Public Network Hartanto, Feliks; Budiman, Budiman; Gwei, Eldwin; Gunawan, Alexander Agung Santoso; Edbert, Ivan Sebastian
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 1 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i1.10389

Abstract

In the current millennium, human society has immensely improved its ability to obtain and distribute information. This change on the other hand, has caused the majority of daily routines to actively involve the usage of computers and mobile devices, which in turn has made people rely heavily on the availability of internet access. This fact was taken advantage of, causing a massive increase in public networks by people or businesses to draw in customers or just as simple public service. This increase gives both ease and risks which this paper will address, specifically on the security measures in network devices that are nearby, and the solution proposed to provide complementary insight on securing the technologies. The authors of this paper supply the main point of the research through experimental efforts i.e., by testing the solution in a real-life scenario. The solution itself involves the configuration of a Raspberry Pi into a VPN server and rerouting all traffic into the Raspberry server so that it will be encrypted and safe from the dangers that will be mentioned in later parts of this paper. The result of the experiment shows that the proposed solution can successfully encrypt the targeted packet so it can’t be read by malicious attackers. Although the solution works it can’t be simply applied to every public network due to internet connection protocols and its inconvenience. Future research will involve the improvement or rework of the solution until the issues mentioned above are solved.
Optimization of Fraud Detection Model with Hybrid Machine Learning and Graph Database Albone, Aan
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 1 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i1.10744

Abstract

Machine learning and the graph database work well together. By concentrating on the relationships between fraudsters or fraud cases, graph databases can provide an additional layer of security, while machine learning uses statistics and data analytical tools to categorize information and identify patterns within data. In doing so, it can transcend rigid rules and scale human insights into algorithms. When combined with a graph, machine learning alone can increase the accuracy of fraud signals to 90% or higher. On its own, it can reach 70–80%. Graphs also improve machine learning's explainability.
Machine Learning for Predicting Personality using Facebook-Based Posts Suhartono, Derwin; Ciputri, Marcella Marella; Susilo, Stefanny
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 1 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i1.10748

Abstract

Social media contributes a lot to human life. People can share their thoughts through text, photos, and voice through social media. Information from social media can be useful, including in personality research. Personality can generally be known through personality tests. In this research, personality prediction is formed to determine personality through Facebook posts without using a personality test. We create a model based on big five personality traits using 5 machine learning algorithms: Support Vector Machine (SVM), Multinomial Naive Bayes, Decision Tree, K-Nearest Neighbor, and Logistic Regression. Data augmentation was also used for balancing the dataset value and trained using stratified 10-fold cross-validation. This research yields the highest f1 score on Openness using Multinomial Naive Bayes algorithm of 82.31% and the highest average is 68.62%. So the five supervised Machine Learning algorithms used in this research produced Multinomial Naive Bayes as the best algorithm to predict personality based on big five personality traits from user postings on Facebook.
Goods Storage Rental Application (YourStorage) Using the React Native Framework Hardjanto, Farhan Rifanto; Nugroho, Aldi; Hidayat, Faridz; Zulfikar, Muhammad Taufiq
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 1 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i1.10759

Abstract

The increase in online business today with quite high transactions means that business actors who sell their merchandise through marketplaces need temporary storage space that does not have a place to store goods. Based on this, the aim of this research is to build a goods storage rental application to make it easier for business actors to rent storage space online with flexible time periods. This type of research is quantitative research where the system development method uses SCRUM and the software architecture used is service-oriented (Microservice). The results of this research are in the form of an Android-based mobile application and as many as 75% of business respondents feel that the “YourStorage” application can help, 68.75% (22) of respondents stated that the application could help in controlling where goods are stored and 71.87% (23) of respondents stated that the application could make it easier to store goods according to required size
Two-Layer Shallow Water Equations with Momentum Conservative Scheme for Wave Propagation Simulation Ginting, Maria Artanta; Suandi, Dani; Dani, Yasi
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 1 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i1.10786

Abstract

In this paper, we discuss the implementation of momentum conservative scheme to shallow water equations (SWE). In shallow water model, the hydrodynamic pressure of the water is neglected. Here, the numerical calculation of mass and momentum conservation was applied on a staggered grid domain. The vertical interval was divided into two parts which made the computation quite efficient and accurate. Our focus is on the performance of the numerical scheme in simulating wave propagation and run-up phenomena, where the main challenge is to calculate the wave speed accurately and to count the non-linear term of the model. Here we also considered the wet and dry conditions of the topography. Three benchmark tests were picked out to validate the numerical scheme. A simulation of standing wave was carried out; the results were compared to the linear analytical solution and show a good fit. In addition, a simulation of harmonic wave propagation on a sloping beach was conducted, and the results closely align with the expected values from exact solution. Finally, we carried out a simulation of solitary wave with a sloping topography; and the results were compared to laboratory data. A good agreement was observed between the simulation results and experimental measurements.
Digital Game as A Media to Increase Cognitive Intelligence of 13-18 Years Old Teenagers　 Edbert, Ivan Sebastian; Tsaniya, Devita Azka; Constantino, Bernico; Riandy, Geary; Aulia, Alvina; Nadia, Nadia
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 1 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i1.10848

Abstract

Nowadays, Cognitive Intelligence plays an essential role especially on making decisions. The growth of digital media makes public thinks that video games are addictive. They think that video games are addictive and damaging. Games are design to refresh, challenge and help people to train their problem solving. In this research, the researcher explored the cognitive development of teenagers aged 13-18 with a puzzle-based digital game. Participants were 15 students studying in junior and senior high school. Participants were given three tests: pre-test and post-test by IQ test and a Game Engagement Questionnaire (GEQ) to explore the game's engagement from the participants' perspective. The average of Pre-Test is 113.2, while the Post-Test is 118.33. This Show that after playing the games it increases the IQ of the students. The researcher also discovered that many factors could influence the outcome of participant IQ. The GEQ shows that the participants agreed that some of the puzzle-based game might be a good or bad influence on them. Keywords: Cognitive Intelligence; Digital Games, Formal Operational Game-based Learning, Jean Piaget's Theory

Page 10 of 18 | Total Record : 174

 8   9   10   11   12 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol. 8 No. 1 (2026): EMACS (In Press) Vol. 7 No. 3 (2025): EMACS Vol. 7 No. 2 (2025): EMACS Vol. 7 No. 1 (2025): EMACS Vol. 6 No. 3 (2024): EMACS Vol. 6 No. 2 (2024): EMACS Vol. 6 No. 1 (2024): EMACS Vol. 5 No. 3 (2023): EMACS Vol. 5 No. 2 (2023): EMACS Vol. 5 No. 1 (2023): EMACS Vol. 4 No. 3 (2022): EMACS Vol. 4 No. 2 (2022): EMACS Vol. 4 No. 1 (2022): EMACS Vol. 3 No. 3 (2021): EMACS Vol. 3 No. 2 (2021): EMACS Vol. 3 No. 1 (2021): EMACS Vol. 2 No. 3 (2020): EMACS Vol. 2 No. 2 (2020): EMACS Vol. 2 No. 1 (2020): EMACS Vol. 1 No. 1 (2019): EMACS More Issue