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Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Published by Universitas Bumigora
ISSN : -     EISSN : 28285611     DOI : https://doi.org/10.30812/ijecsa.v1i2
Core Subject : Science,
Computer Science & IT
Description of Journal : The International Journal of Engineering and Computer Science Applications (IJECSA) is a scientific journal that was born as a forum to facilitate scientists, especially in the field of computer science, to publish their research papers. The 12th of the 12th month of 2021 is the historic day of the establishment of the IJECSA International Journal. The initial idea of ​​forming the IJECSA Journal was based on the thoughts and suggestions of Experts and Lecturers of Computer Science at Bumigora University Mataram-Lombok. This journal covers all areas of computer science research, and studies literature including hardware, software, computer systems organization, computational theory, information systems, computational mathematics, data mining and data science, computational methodology, computer applications, machine learning, and learning technologies. computer. The initial publication of the IJECSA journal is 2 editions in one year, and this will continue to be reviewed based on the number of submitted papers and will increase the number of editions based on the number of submitted papers. Incoming papers will be reviewed by experts in the field of computer science from various countries. We, on behalf of the Editors, ask researchers from all fields of computer science to contribute to the publication of the IJECSA Journal. Topics covered include Computational Mathematics Data Science Computer Applications Information Systems Learning Science And Technology Network Architectures And Protocols Computer Network Education Computer Distance Learning Cloud Computing Cluster Computing Distributed Computing E-Commerce Protocols Automata Theory Game Theory. E-Health Biometric Security And Artificial Intelligence Cryptography And Security Protocols Authentication And Identification Modulation/Coding/Signal Processing Network Measurement And Management Bayesian Networks, Fuzzy And Rough Set Biometric Security And Artificial Intelligence Cryptography And Security Protocols Image Processing And Computer Vision Authentication And Identification Bayesian Networks Fuzzy And Rough Set Mobile System Security Ubiquitous Computing Security Sensor And Mobile Ad Hoc Network Security Security In Social Networks Security For Web Services Security In Wireless Network Security For Grid Computing Security For Web Services Security For Personal Data And Databases Management Of Computing Security Intelligent Multimedia Security Service Computer Applications In Engineering And Technology Computer Control System Design Cad/Cam, Cae, Cim And Robotics Computer Applications In Knowledge-Based And Expert Systems Computer Applications In Information Technology And Communication Computer-Integrated Material Processing (Cimp) Computer-Aided Learning (Cal) Computer Modelling And Simulation Man-Machine Interface Software Engineering And Management Management Techniques And Methods Human Computer InteractionTopics covered include Computational Mathematics Data Science Computer Applications Information Systems Learning Science And Technology Network Architectures And Protocols Computer Network Education Computer Distance Learning Cloud Computing Cluster Computing Distributed Computing E-Commerce Protocols Automata Theory Game Theory. E-Health Biometric Security And Artificial Intelligence Cryptography And Security Protocols Authentication And Identification Modulation/Coding/Signal Processing Network Measurement And Management Bayesian Networks, Fuzzy And Rough Set Biometric Security And Artificial Intelligence Cryptography And Security Protocols Image Processing And Computer Vision Authentication And Identification Bayesian Networks Fuzzy And Rough Set Mobile System Security Ubiquitous Computing Security Sensor And Mobile Ad Hoc Network Security Security In Social Networks Security For Web Services Security In Wireless Network Security For Grid Computing Security For Web Services Security For Personal Data And Databases Management Of Computing Security Intelligent Multimedia Security Service Computer Applications In Engineering And Technology Computer Control System Design Cad/Cam, Cae, Cim And Robotics Computer Applications In Knowledge-Based And Expert Systems Computer Applications In Information Technology And Communication Computer-Integrated Material Processing (Cimp) Computer-Aided Learning (Cal) Computer Modelling And Simulation Man-Machine Interface Software Engineering And Management Management Techniques And Methods Human Computer Interaction
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 78 Documents
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Clustering Analysis of Umrah Pilgrim Data Based on the K-Medoid Method Huda, Dias Nabila; Anggrawan, Anthony; Hairani, Hairani
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 3 No 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4601

Abstract

The Umrah pilgrimage is becoming increasingly popular among Indonesians, with millions of participants yearly. This trend creates a need for service providers to understand the characteristics of pilgrims to improve service quality, marketing strategies, and competitiveness. Analyzing data on pilgrims helps service providers develop more effective strategies and tailor packages to match their needs, ensuring competitiveness in a growing market. This study aims to clusters Umrah pilgrims based on age, gender, district, and chosen package using the K-Medoid clustering method. This research uses the K-Medoid method for the reason that it is more resistant to noise and outliers compared to other clustering methods. The most centrally located point in the data set is called a ”medoid,” which is an object in a cluster that has the lowest difference to all other objects in the cluster. The results of this study are that the K-Medoid method successfully grouped pilgrims into three clusters: Cluster 1 with 63 members, Cluster 2 with 25 members, and Cluster 3 with 25 members. The findings indicate that the Milad Mastour package is preferred by older pilgrims, primarily from Mataram and West Lombok. The Arbain package is favored by younger pilgrims from the same regions, while adult pilgrims mostly choose the Regular package. The implication of this research is that it can provide insights for service providers to design more specific programs that align with the profiles of pilgrims based on age and district.
Design of a Quick Response Code-Based Infrastructure Management Information System Sukron, Moh.; Ramadhan, M. Raihan; Sihabillah, Ahmad
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 3 No 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4653

Abstract

The management of infrastructure and facilities at MTs Mambaul Hasan Sumberrejo Paiton Probolinggo is currently conducted manually, resulting in significant issues such as data inaccuracies, misplacement of items, and difficulties in tracking asset movements. These challenges reduce efficiency and hinder effective inventory management. The aim of this research is to design and develop a Quick Response (QR) Code-based management information system to enhance the efficiency and effectiveness of infrastructure and facilities management at MTs Mambaul Hasan. This research method is based on Research and Development (R&D) with a quantitative approach and a case study framework. The process includes system requirements analysis through direct observation and interviews with school staff, followed by system design using the Object-Oriented Analysis and Design (OOAD) approach. A prototype is then developed and tested to gather user feedback, and system evaluation is conducted to refine the system before full implementation. The results of this research are a QR Code-based infrastructure and facilities management information system that simplifies asset registration, enhances tracking accuracy, and reduces manual workload. Usability testing with school staff revealed an 82,67% satisfaction rate, indicating a significant improvement in efficiency and traceability of assets. The implementation of this system provides a practical and effective solution for managing infrastructure and facilities at MTs Mambaul Hasan. This study concludes that the QR Code-based system improves efficiency, accuracy, and traceability in inventory management. The implications of these findings suggest that other educational institutions can adopt similar technological solutions to modernize their management processes, with potential future integration of mobile and cloud technologies for enhanced usability and scalability.
Plume Detection System Based Internet of Things Utama, I Nyoman Susila Astraning; Hadi, Sirojul; Hariyadi, I Putu
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i1.1698

Abstract

Security is one of the important aspects in a system or environment. Residential, office, tourist and industrial areas are places that are prone to fires because they contain flammable objects. Slow handling when a gas leak occurs can trigger a fire. The solution that can be used to minimize the occurrence of fires is to build tools that work to monitor the condition of the room or environment that is prone to leakage of gas or other flammable liquids. The design and manufacture of a system to detect LPG and alcohol gas leaks can be useful for providing information in the event of a gas or alcohol leak so that it can be handled quickly and minimize fire damage. This system combines an plume detection system with an internet of things system so that it can provide information when a gas or flammable liquid leak occurs. The gas leak information is sent as a notification to the telegram from the operator. The design and manufacture of this system uses the Waterfall methodology with the following stages: analyzing (covering the need for system creation), system design (including designing electronic circuits and web monitoring interfaces), implementing system design and testing the system as a whole. The result of this research is that an electronic detection system has been successfully built that can distinguish gases and can provide information via telegram and web if gas is detected in the sensor environment. In the LPG gas leak test, the results show that the characteristics of LPG gas, namely the sensor output voltage, have an average of 4.17 volts with an average Part Per Million (PPM) of 8340 and the characteristics of alcohol gas, namely the sensor output voltage, have an average of 0, 13 volts with an average Part Per Million (PPM) of 254.
Prediction of Electricity Usage with Back-propagation Neural Network Anggrawan, Anthony; Hairani, Hairani; Candra, M. Ade
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i1.1722

Abstract

The use of electricity has become a need that is increasing day by day. So it is not surprising that the problem of using electricity has attracted the attention of many researchers to research it. Electricity users make various efforts and ways to save on the use of electrical energy. One of them is saving electricity usage by electricity users using electrical energy-efficient equipment. That is why the previous research confirms the need for interventions to reduce the use of electrical energy. Therefore, this study aims to predict electricity use and measure the performance of the anticipated results of electricity use. This study uses the back-propagation method in predicting the use of electricity. This study concluded that the backpropagation architectural model with better performance is the six hidden layer architecture, 0.4 learning rate, and the Root Means Square Error (RMSE) value of 0.203424. Meanwhile, the training data test results get the best architectural model on hidden layer 8 with a learning rate of 0.3 with an RMSE performance value of 0.035811. The prediction results show that the prediction of electricity consumption is close to the actual data of actual electricity consumption.
Home Security Voice Notification System With Arduino Sensor, PIR based SMS Gateway Mujitomo, Muh Thariq Ali; Santoso, Heroe; Widyawati, Lilik; Priyanto, Dadang
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i1.1793

Abstract

The level of crime that afflicts households such as theft and robbery is still common. Many thefts occur when the homeowner is away and the house is staying for a long time. Although some residential neighborhoods already have security officers, human limitations can be a gap for perpetrators of theft. To overcome this, we need a guard who is always there to monitor the house to avoid theft. In this case the risk of guards on duty to guard the house is very large, such as acts of theft accompanied by violence. The design and manufacture of this system uses the Network Development Life Cycle (NDLC) method. NDLC is a method that relies on previous development processes such as business strategy planning, application development lifecycle, and data distribution analysis. There are 6 stages in the Network Development Life Cycle (NDLC) namely Analysis, Design, Simulation Prototyping, Implementation, Monitoring, Management. The purpose of this research is Designing a home security system that can guard the house in real time and can be accessed remotely. The conclusion of this research is that the entire system is proven to be able to work in detecting, giving warnings, and making calls to the user.Network Development Life Cycle (NDLC) namely Analysis, Design, Simulation Prototyping, Implementation, Monitoring, Management. The purpose of this research is Designing a home security system that can guard the house in real time and can be accessed remotely. The conclusion of this research is that the entire system is proven to be able to work in detecting, giving warnings, and making calls to the user.
Optic Disk Segmentation Using Histogram Analysis Triwijoyo, Bambang Krismono
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i1.1799

Abstract

In the field of disease diagnosis with ophthalmic aids, automatic segmentation of the retinal optic disc is required. The main challenge in OD segmentation is to determine the exact location of the OD and remove noise in the retinal image. This paper proposes a method for automatic optical disc segmentation on color retinal fundus images using histogram analysis. Based on the properties of the optical disk, where the optical disk tends to occupy a high intensity. This method has been applied to the Digital Retinal Database for Vessel Extraction (DRIVE)and MESSIDOR database. The experimental results show that the proposed automatic optical segmentation method has an accuracy of 55% for DRIVE dataset and 89% for MESSIDOR database
Ensemble Implementation for Predicting Student Graduation with Classification Algorithm Rismayati, Ria; Ismarmiaty, Ismarmiaty; Hidayat, Syahroni
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i1.1805

Abstract

Graduating on time at the higher education level is one of the main targets of every student and university institution. Many factors can affect a student's length of study, the different character of each student is also an internal factor that affects their study period. These characters are used in this study to classify data groups of students who graduated on time or not. Classification was chosen because it is able to find a model or pattern that can describe and distinguish classes in a dataset. This research method uses the esemble learning method which aims to see student graduation predictions using a dataset from Kaggle, the data used is a IPK dataset collected from a university in Indonesia which consists of 1687 records and 5 attributes where this dataset is not balanced. The intended target is whether the student is predicted to graduate on time or not. The method proposed in this study is Ensemble Learning Different Contribution Sampling (DCS) and the algorithms used include Logistic Regression, Decision Tree Classifier, Gaussian, Random Forest Classifier, Ada Bost Classifier, Support Vector Coefficient, KNeighbors Classifier and MLP Classifier. From each classification algorithm used, the test value and accuracy are calculated which are then compared between the algorithms. Based on the results of research that has been carried out, it is concluded that the best accuracy results are owned by the MLPClassifier algorithm with the ability to predict student graduation on time of 91.87%. The classification model provided by the DCS-LCA used does not give better results than the basic classifier of its constituent, namely the MLPClassifier algorithm of 91.87%, SVC of 91.64%, Logistic Regression of 91.46%, AdaBost Classifier of 90.87%, Random Forest Classifier of 90.45% , and KNN of 89.80%.
Implementation Of The Simple Additive Weighting Method At Universitas Terbuka Mataram For New Employee Recruitment Husain, Husain; Santoso, Heroe; Wardhana, Helna; Ardiasyah, Muhammad Irwan; Fitriani, Nurul
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i1.1814

Abstract

An agency will thrive if it is supported by qualified employees. So that employees are certainly one of the most important assets in an institution, both private and public institutions, so that agencies are required to recruit prospective contract employees who have competence and talent to support the implementation of work at the Open University of Mataram City. Skilled workers who can bring institutions forward and compete with other agencies so that they can keep up with the times, and the recruitment aspect is starting to get a special view, because the recruitment process is not in accordance with the needs at the Mataram Open University so that it can hinder the rate of development of the agency itself. Therefore, a decision support system is needed for the contract employee recruitment process at the Universitas Terbuka Mataram. Therefore, a decision support system is needed for the contract employee recruitment process at universitas Terbuka Mataram campus. This decision support system uses the Simple Additive Weighting SAW method. In this case, prospective employees are compared with one another so as to provide an output value of priority intensity which results in a system that provides an assessment of each employee. This decision support system helps evaluate each employee, make changes to the criteria, and changes the weight values. This is useful to facilitate decision making related to employee selection issues, so that the most appropriate employees will be received in the company. The purpose of this study was to select the best candidate for employees at the Universitas Terbuka Mataram. The results of this study indicate that the SAW method is appropriate for selecting the best prospective employees because it can obtain qualified employees in accordance with the expectations of the company and the leadership.
Augmented Reality in Indonesia's Primary School: Systematic Mapping Study Chani Saputri, Dian Syafitri; Susilowati, Dyah
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i1.1817

Abstract

Augmented reality is an increase trending technology in education. Research in the field of AR for Education is increasing every year. A large number of research reviews have focused on the use of AR in education worldwide and at all levels of education, but there have not been many reviews of the use of AR at the elementary school level in Indonesia. This study aims to determine the trends of using AR in elementary school in Indonesia. This research used a Systematic Mapping Study approach, which consists of the stages of definition research question, conduct research, screening papers, keywording using abstract, and data extraction and mapping process. Subjects of elementary education, grade, type of AR application, and research method and facets, have been used in classifying AR. Inclusion and exclusion criteria were used in determining the relevant papers. This study examining 42 articles containing “augmented reality”, “elementary school” and “Indonesia” in their titles, abstracts, and keywords. Articles were published between 2017 and 2021. The results have shown that science learning is the most subject to be the object of AR research, followed by mathematics, language, and culture. Most of the AR implementations are implemented in class IV, V and VI. The most widely used type of AR application is marker-based AR. The most widely used research method is R&D which focuses on developing AR products.
COVID-19 Suspects Monitoring System Based on Symptom recognition using Deep Neural Network Udayanti, Erika Devi; Kartikadharma, Etika; Firdausillah, Fahri; Ikhsan, Nur
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 2 No. 1 (2023): March 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i1.2073

Abstract

The outbreak of the Corona virus or COVID-19 was still a global concern even though it has been declared an endemic in several countries in the world, including Indonesia. However, with the emergence of new variants of this virus, preventive efforts continue to be made to prevent its spread. To prevent the spread of this virus, early detection was important, especially in knowing prospective clients who are positive and reactive to this virus, thus enabling early isolation measures for prospective patients who are taking action. This identification can be carried out in public areas that are the center of community activities. In this study, an intelligent system will be developed that can detect people suspected of COVID-19 through fever and breathing problem symptoms that can provide solutions to prevent the spread of this virus. Identify these symptoms through thermography-based image processing sourced from thermal camera sensors and then look for the possibility of suspected and reactive COVID19. Furthermore, the AI model was used by the early detection system of people suspected of being positive and reactive for COVID-19 using the Deep Neural Network method. This study aims to identify symptoms of fever and respiratory infection through image processing sourced from thermal camera sensors and further diagnose prospective patients who are suspected of being positive and reactive for COVID19 using the CNN method as an intelligent system for early detection of suspected positive and reactive COVID19 patientsIn the process of testing the classification training model, the performance results in the CNN classification process have an accuracy value of more than 88%. Furthermore, a comparison was made between the CNN classification and other classifications, such as SVM, Naive Bayes and Multi-Layer Perceptron (MLP). The results obtained from this comparison have an average percentage of accuracy above 80%. MLP has the lowest accuracy among its classification methods of 83.56%. CNN has the highest accuracy value compared to other methods of 88.68%. Therefore, CNN can be chosen to be the right one for use in the COVID-19 suspect detection system through the recognition of symptoms and respiratory disorders. Based on these performance measurements, the process of detecting COVID19 suspects indicated by health symptoms can be applied to real data.

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