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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 10 Documents
 1 
Search results for , issue "Vol. 5 No. 2 (2023): EMACS" : 10 Documents clear
Training CNN-based Model on Low Resource Hardware and Small Dataset for Early Prediction of Melanoma from Skin Lesion Images Ivan Halim Parmonangan; Marsella Marsella; Doharfen Frans Rino Pardede; Katarina Prisca Rijanto; Stephanie Stephanie; Kreshna Adhitya Chandra Kesuma; Valentina Tiara Cahyaningtyas; Maria Susan Anggreainy
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9904

Abstract

Melanoma is a kind of rare skin cancer that can spread quickly to the other skin layers and the organs beneath. Melanoma is known to be curable only if it is diagnosed at an early stage. This poses a challenge for accurate prediction to cut the number of deaths caused by melanoma. Deep learning methods have recently shown promising performance in classifying images accurately. However, it requires a lot of samples to generalize well, while the number of melanoma sample images is limited. To solve this issue, transfer learning has widely adapted to transfer the knowledge of the pretrained model to another domain or new dataset which has lesser samples or different tasks. This study is aimed to find which method is better to achieve this for early melanoma prediction from skin lesion images. We investigated three pretrained and one non-pretrained image classification models. Specifically, we choose the pretrained models which are efficient to train on small training sample and low hardware resource. The result shows that using limited sample images and low hardware resource, pretrained image models yield better overall accuracy and recall compared to the non-pretrained model. This suggests that pretrained models are more suitable in this task with constrained data and hardware resource.
The SDLC Analysis for Implementation Document Management System at IPR Center of Universitas Jenderal Soedirman Muhammad Syaiful Aliim; Retno Supriyanti; Hari Siswantoro
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9910

Abstract

The Document Management System (DMS), the Center for the Development of Intellectual Property Rights, is a web-based document management information system developed by the Center for Intellectual Property Development at Universitas Jenderal Soedirman. This system is used to archive documents digitally. However, because it did not work as intended by the user, the DMS Intellectual Property Rights Center did redesign both appearance and database relations. After the redesign process is complete, the SIMD Intellectual Property Rights Centre’s development results from the redesign need to be implemented in a production environment. For the implementation process to run well, it is necessary to analyze the factors that affect the deployment process with the implementation process in the widely used System Development Life Cycle (SDLC) model. By mapping these factors with the characteristics, advantages, and disadvantages of each SDLC implementation process, the V-shaped model's deployment process is more effective and efficient in its execution in a production environment. The results show that the results of this redesign procedure can solve the problems that have occurred so far.
In-Car Air Quality Notification Using Internet of Things Platform Regi Fernando Najoan; Suharjito Suharjito
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9950

Abstract

In the development of modern society, transportation is essential to support daily activities. With the existence of vehicles, the activities carried out by the community will be much easier. The car is a means of transportation that is often used by the community to carry out activities related to their respective goals. In-car air quality is very crucial for society because most people spend their time in cars. Often, the air in the car contains not only good air but also bad air for humans. The impact of poor air quality can make people sleepy, as well as cause respiratory problems and several other diseases that can even affect a driver's ability to make decisions. Therefore, there must be a real-time monitoring and notification system for air quality in the car. The purpose of this research is to be able to develop an air quality monitoring system for cars and provide notifications if the air quality worsens in real-time. In this study, researchers developed an air quality monitoring and notification system for cars using the NodeMCU ESP8266 microcontroller, sensors MQ-7, MQ-135, PMS5003, and the IoT platform, namely Blynk and ThingSpeak. The result of this research is a system that can detect, measure, and monitor air quality levels of carbon dioxide (CO), carbon monoxide (CO2), particulate matter (PM10), and particulate matter (PM2.5) via the internet in real-time. and displays air quality data on the dashboard, then provides notifications using the Blynk application if the air quality is low and getting worse.
Phishing Site Detection Classification Model Using Machine Learning Approach Yohan Muliono; Muhammad Amar Ma’ruf; Zakiyyah Mutiara Azzahra
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9951

Abstract

Phishing has been a cybercrime that has existed for a long time, and there are still many people who are victims of this attack. This research attempts to prevent phishing by extracting the attributes found on phishing websites. This study uses a hybrid method by combining allowlist and denylist as part of a classification system. This research utilizes 18 features to identify a phishing site in terms of address bar, abnormal request, and source code (HTML and JavaScript). Where in each feature the author determines the benchmark. This study validates the status code and detects 52 URL shortening service domains and then evaluates these abnormalities with a binary classification system. Algorithms that have good results are Decision Tree and K Nearest Neighbor (KNN). After evaluating the performance of the algorithm in terms of Precision, Recall, and F-Measure. As a result, the Decision Tree algorithm has the highest accuracy of 97.62% and the fastest computation time of 0.00894 seconds. So that the Decision Tree is superior in terms of accuracy and computation time in detecting phishing URLs.
Implementation of Augmented Reality Shopping in E-Commerce to Increase Customer's Purchase Intention　 Diana Diana; Grasiela Angelina; Inigo Hersanta Sutandyo; Jeremy Michael Yuwono; Ivan Sebastian Edbert; Alvina Aulia
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9954

Abstract

E-commerce has grown immensely in the past few years, especially during the Covid-19 pandemic. This condition brings severe conditions. Most traditional business owners must shift and implement e-commerce into their business models. Although e-commerce has many advantages, it also has limitations. The limitation of e-commerce is that it is difficult for customers to visualize the products they will buy in the real world. Most E-commerce applications only allow customers to see images and reviews of the products they want to buy through the screen of their devices. This situation can decrease customer satisfaction if the products they believe do not match the images. This research aims to help solve the limitations of e-commerce applications by implementing Augmented Reality to help customers visualize the products they buy. The researcher developed an Android-based mobile AR application using Unity and the Vuforia engine. After creating our AR application, we surveyed 46 participants to test our AR application. The researcher used Likert Scale with a scale of 4. The result is that almost all participants agreed that AR makes it easier to visualize the product purchased from e-commerce. An average score of 3.6 shows that most participants strongly agree that AR positively influences their decision-making to buy products.
CRISPR: On How it'll Change the Future Alvina Aulia; Rael Russel Hutapea; Prawira Setya Abdima; Akhmad Ali Emawan; Ivan Sebastian Edbert
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9975

Abstract

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) explain genetic illness and how people can treat it using. CRISPR, a gene editing technology, has altered what is now possible in animal modification and the development of human treatments. Technological advancements enable new enhanced plants, breakthrough concepts for human medicine, and appealing yet feasible techniques for reducing vector-borne illnesses. CRISPR is used as a diagnostic method for several critical diseases like cancer. CRISPR can detect and identify the DNA and RNA to identify the cause of the pathogen, like viruses or bacteria with high sensitivity. In this research, the researcher will explain how CRISPR will change the future, specifically for medical purposes. The researcher will do the Systematic Literature Review (SLR) to describe CRISPR. The goal considering CRISPR in the future is routinely used to edit the genetics of plant, bacterial, and even animal models for good purposes. It also nourishes and protects the human body from diseases by examining target genes in genome modification, investigating, and treating genetic disorders, infectious diseases, and immunological diseases. In CRISPR applications for hereditary illnesses, the CRISPR/Cas technology has been used for gene therapy to protect humans against sickness. Although the limitation of the technology is that still in the initial stages, CRISPR could be one of the groundbreaking methods in the future.
Comparison of the TF-IDF Method with the Count Vectorizer to Classify Hate Speech Kristien Margi Suryaningrum
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9978

Abstract

Hate speech is a form of expression used to spread hatred and commit acts of violence and discrimination against a person or group of people for various reasons. Cases of hate speech are very common in social media, one of which is Twitter. The goal to be achieved is to create a system that can classify a tweet on Twitter into hate speech (HS) or non-hate speech (NONHS) classes. The method used is Support Vector Machine by comparing the features of TF-IDF and Count Vectorizer. And the parameters compared are seen from accuracy, precision, recall, and f1-score. Results obtained, overall, by using the TF-IDF feature, the Support Vector Machine algorithm gets high results compared to the Count Vectorizer feature, with an accuracy value of 88.77%, 87.45% precision, 88.77% recall, and f1-score of 87.81%.
Fruits Recognition using Deep Convolutional Neural Network for Low Computing Device Irene Anindaputri Iswanto
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9986

Abstract

Artificial intelligence is one of the most developed fields in Computer Science where a lot of researches had been done to make the computer smarter to perform human-like task. One of the most common human-life task research that had been done is object recognition. Convolutional Neural Network is one of the most popular deep learning model to perform a good object recognition. While improving CNN model can be done by simply increasing the depth of its architecture, some researchers prove that as the CNN architecture go deeper, the accuracy will get worse. ResNet, with their residual layer, successfully lift the limitation, but ResNet by itself is too heavy for a mobile or low computing device. This paper proposes a new model which could reach the accuracy of ResNet while having faster prediction time. The proposed model and other state-of-the-art models had been trained on our own fruits and vegetables dataset. The result shows that the proposed model can reach the same accuracy as Resnet110 and overcome the accuracy of DenseNet121 while being faster than those models.
Enhancing Flood Disaster Preparedness Through Virtual Reality: A VR-based Flood Simulator Game Francisco Maruli Panggabean
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9988

Abstract

This research focuses on the use of virtual reality (VR) technology to increase user preparedness for flood disasters. This study aims to develop a VR-based flood simulation that provides users with immersive flood disaster simulations in line with flood preparedness guidelines. Game application needs are collected through questionnaires and similar application analysis. Evaluation is carried out to assess user needs, user interface (UI), and game effectiveness in increasing flood disaster preparedness. The evaluation was carried out by involving users who live in flood-prone areas, asking them to play a VR flood simulator game and filling out a questionnaire. The results show positive user perceptions and an average score increase of 25.38% in the second play session, indicating increased readiness. The VR flood simulator game shows its potential to increase preparedness and knowledge in dealing with flood disasters.
Phishing Detection Applications for Website and Domain at Browser using Virustototal API Nadia Nadia; Wellson Leewando; Javier Paulus; Valentino Nooril
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9998

Abstract

The purpose of this research is to create a browser extension-based application that can detect malicious sites to minimize phishing attacks. The research method used is to conduct a literature study and collect data from the questionnaire results. Research testing methods are blackbox testing, performance testing using 100 URL with precision and recall method, and comparison between two other simillar applications. The results of this study indicate that this application has good functionality and can reduce phishing attacks on users. The conclusion that can be drawn from this research is that the malicious site detection feature in browser extensions can enhance user protection from phishing attacks

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