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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 11 Documents
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Search results for , issue "Vol. 5 No. 3 (2023): EMACS" : 11 Documents clear
User Experience Analysis of Duolingo Using User Experience Questionnaire Anderies Anderies; Cindy Agustina; Tania Lipiena; Ayunda Raaziqi; Alexander Agung Santoso Gunawan
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.v5i3.9227

Abstract

The internet is one of the vital means for everyone to get various information easily and exact like they’re looking for. The use of internet-based learning that is applied in modern times is very influential in the field of education compared to the past, because it can develop language skills in a country, besides that increasingly sophisticated technology can help students learn in a structured manner. One of the impacts we can see or feel is on the learning process. With the internet, it is so much easier either for the students or the teachers. One of the well-known applications in the world is Duolingo. Duolingo is one of many applications that give so much influence to language learning applications. More than 300 million people already use Duolingo for their learning. The purpose of this experiment is to analyze the User Experience of the Duolingo application. The experimental method was applied using surveys distributed via social media. There are 103 Duolingo users who were willing to take the surveys and answer all of the questions given. The result of the survey showed Novelty’s scale has the lowest mean, and Perspicuity’s scale has the highest. That means some of Duolingo’s users found that the application is less interesting. Hence, that could affect the effectiveness of the application.
Relay Driver Based on Arduino UNO to Bridge the Gap of The Digital Output Voltage of The Node MCU ESP32 Yulianto Yulianto
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.v5i3.9697

Abstract

The IoT could control the devices that need a high current voltage to operate. The voltage control here means that the IoT could give the command to turn on and turn off the electric current by using a relay module. One of the devices that are most frequently used in many research projects is Node MCU ESP8266 and Node MCU ESP32. Those microcontrollers work with the maximum supply is 3.3-volt direct current (DC). On the other hand, the relay module commonly needs a voltage supply of 5-volt DC and the relay needs to be controlled by a single transistor to make a trig on. The relay will be active when the transistor’s basis pin is grounded into the ground, so the relay will get the current flow. However, the relay module which is controlled using Node MCU could not work properly, caused Node MCU only provides the digital out is 3-volt maximum from its digital Input Output pins (I/O). Meanwhile, the driver relay based on a single transistor needs a bias input amount of 5 volts to make the relay module active well. If the bias voltage doesn't reach 5 volts or just 3.3 volts will make the relay can't switch on properly which can result in bad contact. To overcome that problem this research proposed the driver relay based on Arduino UNO. The novel of this research is adding the Arduino UNO module between Node MCU and the relay module which has task to bridge the voltage difference between the output digital output ESP that only maximum 3.3 volt converted by Arduino to be digital output which can reach the voltage of 5 volt. The Arduino JSON library was also involved to wrap the commands that produced by Node MCU then deserialized on Arduino to parse and convert to be digital output to control the relay module.
Structural Time Series Model using Hamiltonian Monte Carlo for Rice Price Rifdatun Ni'mah
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.v5i3.9903

Abstract

Although forecasts of future events are simply uncertain, predicting is one of the most important aspects of future planning. Accurate rice price predictions tend to be helpful for wholesalers, producers, and farmers to develop plans and strategies to reduce the risks that can be faced. Structural time series models are the most plausible alternative for long-term forecasting. This paper proposes an alternate method for modeling average rice prices using structural time series along with Bayesian parameter inference via Hamiltonian Monte Carlo (HMC). The model has been built using the monthly average wholesale rice price from January 2010 to December 2019. For working out both structural time series and HMC, the TensorFlow Probability Library was used. Linear trend, seasonal, and autoregressive components were combined as an additive model to the structural time model. The proposed Hamiltonian parameter produces an optimal acceptance rate. Their trace plot was used to diagnose the convergence of their chain. One of the predictive accuracy of models was assessed using the mean absolute percent error (MAPE). Through both single and multiple chain iterations, the prediction accuracy of a year-ahead is highly accurate, with MAPE less than 2%. Long-term iteration draws during Hamiltonian Monte Carlo should be considered when attempting to achieve more convergence.
Analysis and Design of Asset and Non-Asset Lending Information Systems in the Informatics Engineering Service Unit at Astra Polytechnic Haikal Andrean; Indah Cyithia Devi; Fadhilah Nur’aini; Michael Edigia Wizard
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.v5i3.9907

Abstract

The development of information technology is developing rapidly and has a positive impact on organizational, one of which is Astra Polytechnic, especially at UPT Informatics, which supports the operational activities of inventory lending in the form of assets and non-assets which are considered inefficient. The process of recording, submitting, and approving inventory loans is still done manually, so it takes a lot of time and is prone to errors. The process of recording inventory data is still using excel, loan applications are still made using hardcopy forms and there are no asset and non-asset reports. The aim of research is to analyze needs and design of a system for lending assets and non-assets at UPT Informatics based on Web Applications in order to overcome these problems. The design uses System Development Life Cycle (SDLC) based on Prototyping methodology until design stage. The process of analysis and design is explained using Unified Modeling Language (UML) approach. Analysis of system requirements is carried out on ongoing business processes, which are then outlined in design of asset and non-asset lending information systems, especially in documentation process to produce reports according to company needs.
Government Recommendations for the Effective Implementation of QuickCommerce Policy to Transform the E-Commerce Sector Muhammad Younus; Tunjung Sulaksono; Suranto Suranto
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.v5i3.9969

Abstract

This paper aims to determine the guideline regarding the Initiative of the Government to transform the E-commerce industry of the country and take it to a new level of QuickCommerce. The Researchers who are working on the research in this field of work are using ‘Qualitative Research.’ This research material was taken from social media, Online Media, Websites, and Articles, and then it is displayed in descriptive format. This paper indicates that making this guideline will ensure the improvement needed in the E-commerce Market and make it ready for the successful implementation of QuickCommerce in the country. Several steps need to be followed to create and successfully implement QuickCommerce—first, the analysis and determining the significant challenges are present currently, which is pushing back the smooth implementation. Second, the selection of the way forward for the implementation of it and deciding the critical indicators required for it. Finally, then proposing the solution based on the analysis. This study is limited to the extent to which the problem is clearly defined, then suggests the solution with its working flow, and lastly, what advantage it will provide to solve the problem that the current E-commerce market is facing.
Minimizing Defects in Radiator Grille Upper Garnish Parts using Six Sigma (DMAIC) at PT. AAS Yosica Mariana; Wandi Andrean; Nina Tania Lestari
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.v5i3.10481

Abstract

To achieve quality improvement, companies must align their production processes with customer needs, control production costs effectively, and maintain product quality. This approach enhances customer satisfaction, increases market share, and boosts profitability. PT. AAS, an automotive company specializing in injection molding and painting, relies on resin as its primary raw material to produce car parts for major clients like Hyundai, Suzuki, and Astra. To dominate the market, PT. AAS must prioritize delivering quality products on time to earn customer trust and secure continuous orders. Initially, the QC data for the injection molding area showed a high defect rate of approximately 17.5% for the Garnish Radiator Grille Upper part, from December 2022 to May 2023. Prior to implementing the Six Sigma method, PT. AAS had a DPMO (Defects Per Million Opportunities) value of 58,177, equivalent to a 3.10 Sigma level. After applying the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) process, the DPMO decreased to 17,991, resulting in a 3.60 Sigma level. This suggests that PT. AAS currently operates at a 4 Sigma level, with a strong potential to reach 5 or even 6 Sigma by addressing the root causes of rejection. The fishbone analysis highlights the need for action across all departments, including Management. Key areas to focus on include Material, ensuring the correct resin delivery to the Injection Molding station; Machine, verifying proper machine settings and utilizing the 5S methodology; Environment, optimizing room temperature and ventilation; and Man, providing training to enhance operators' knowledge and sense of responsibility.
Influential Factors Affecting the Adoption Intention of Electric Vehicles in Indonesia: An Extension of the Theory of Planned Behavior Daffa Refor Multi Ray; Christian Harito
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.v5i3.10525

Abstract

The primary goal of the thesis was to examine the factors that affect the willingness of people in Indonesia to adopt Electric Vehicles (EVs). Given the pressing need in Indonesia to address energy shortages and reduce greenhouse gas emissions, this research aimed to investigate the elements that influence people's inclination to use EVs. In this study, questionnaires were used as a means of measurement. Respondents were provided with a brief explanation before completing the survey. Using an extended TPB (Theory of Planned Behavior) model, the research analyzed the adoption intentions of 310 respondents from Indonesia, following a minimum sample guideline of 200. The collected data was analyzed using smartPLS4 to extract insights. The empirical analysis of the research focused on five key factors: attitude, subjective norms, perceived behavioral control, environmental concern, and moral norms. Notably, the empirical results showed that while attitude had an insignificant impact on the adoption intention of EVs in Indonesia, the other factors subjective norms, perceived behavioral control, environmental concern, and moral norms had a significant and positive influence on the intention to embrace electric vehicles in the country. Based on these findings, it can be concluded that the extended TPB model is suitable for predicting the adoption intention of electric vehicles. Considering these results, the study explores the implications for EV adoption in Indonesia, offering valuable insights and recommendations for future research and for the Indonesian government's decision-making process regarding the factors that influence EV adoption.
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.

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