cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hairani
Contact Email
ijecsa@universitasbumigora.ac.id
Phone
+6287839793970
Journal Mail Official
ijecsa@universitasbumigora.ac.id
Editorial Address
Universitas Bumigora Jl. Ismail Marzuki-Cilinaya-Cakranegara-Mataram 83127
Location
Kota mataram,
Nusa tenggara barat
INDONESIA
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
 1   2   3   4   5 
Potential of Cari Rumah Software to Brings House Product Consumer and Digital Marketing Tool Agus Pribadi
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 1 No 2 (2022): September 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (583.643 KB) | DOI: 10.30812/ijecsa.v1i2.2400

Abstract

Growth of residential areas in the city of Mataram and its surroundings has a tendency to continue to increase. The growth of residential areas is in line with population growth which reaches 1,842%. Emergence of new residential areas in the city of Mataram and its surroundings involve a lot of amount of housing developer companies. Wide distribution of new residential/housing areas has in potential consumers having to make special efforts to obtain information on the purchase of new house. Cari Rumah software provides online search services for new residential products to the public using smartphone devices. The software makes it easier for prospective consumers to get information on new residential products without having to go to the marketing office of housing developer companies one by one. On the other hand, Cari Rumah software can also function as a means of marketing new residential house products by housing developer companies. Cari Rumah software offers convenience in finding new residential house product provided by housing developer companies. The content owned by the Cari Rumah software has the possibility as a means of digital marketing in addition to the function of helping potential consumers. This search study aims to find out the potential of the Cari Rumah software to meet the information needs of potential consumers and possibilities be used as a means of digital marketing. Method used is an impression test of the Cari Rumah software. The main stages of this method are gathering information, impression test, and potential conclusion. Results obtained indicate that the Cari Rumah software is not sufficient to meet the information needs for users / potential consumers of residential house product. However, 83.94% of users of the Cari Rumah software recommend to developer criteria features. Potential use of the Cari Rumah software as a digital marketing tool only reaches 20.15%.
Designing a Honey Quality Tool Based on Gas Sensor and Color Sensor Al Bima; Sirojul Hadi; M. Najmul Fadli; Nurul Hidayah; Lalu Danu Prima Arzani
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 1 No 2 (2022): September 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (578.267 KB) | DOI: 10.30812/ijecsa.v1i2.2407

Abstract

Honey has many benefits. Thus, honey counterfeiting often occurs with the ever-increasing demand. However, many do not know how to distinguish between real and fake honey, even honey breeders and hunters find it difficult to tell the difference. The honey used to test the quality of honey is honey produced by Apis dorsata bees or wild bees, the nectar consumed by these bees is the kesambi tree, the kesambi tree that grows a lot on the slopes of Mount Tambora, Bima district. Honey contains a lot of antioxidants such as vitamin C, pinocembrin, chrysin, pinobaksin, catalase, and many other ingredients that are very beneficial for the health of the body. Testing the authenticity of honey using two sensors, namely a gas sensor and a color sensor, the tool has been connected to a database and web application to display test data. The web application can be accessed from any location as long as it is connected to the internet network. The results of the research are the ideal distance for measurements is carried out as far as 2 cm. The success rate in testing pure honey with a mixture of honey has different values, such as the value of pure honey has a gas sensor voltage of 3.3 volts while the value of mixed honey with 50% pure honey and 50% sugar, the value of the gas sensor voltage is 2.54 volts. Mixed honey has a voltage below 3 volts. The results of the color sensor test, namely the sensor output have different values, the RGB value for pure honey is red 206, green 246, and blue 182 while the RGB value for mixed honey is for 20% pure honey and 80% sugar mixture produces an RGB value of Red 156, Green 210, Blue 171. The color sensor can distinguish between real honey and mixed honey for trigona honey.
COVID-19 Suspects Monitoring System Based on Symptom recognition using Deep Neural Network Erika Devi Udayanti; Etika Kartikadharma; Fahri Firdausillah; Nur Ikhsan
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.
The Application of the Fletcher-Reeves Algorithm to Predict Spinach Vegetable Production in Sumatra Mhd. Zoel Ardha; Verdi Yasin; Solikhun Solikhun
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.v2i1.2417

Abstract

Determination of spinach plant predictions is one of the most critical decision-making processes. In predicting spinach plants in each period, it depends on each period, both the previous and subsequent periods. The production of spinach plants that change every period causes uncertainty in predicting. The method used to indicate the data is the Fletcher-Reeves algorithm, it is an appropriate development technique compared to the backpropagation strategy because this strategy can speed up the preparation time to arrive at the minimum convergence value. This paper does not discuss the prediction results. Still, it discusses the ability of the Fletcher-Reeves algorithm to make predictions based on the spinach production dataset obtained from the Central Statistics Agency. The purpose of this research is to see the accuracy and performance measurement of the algorithm in the search for the best results to solve the prediction of spinach plants in Sumatra. The research data used are spinach vegetable production data in North Sumatra. Based on this data, a network architecture model will be formed and determined, including 2-20-1, 2-30-1, 2-35-1, 2-45-1, and 2-50-1. After training and testing, these five models show that the best architectural model is 2-20-1 with an MSE value of 0.00608399, the lowest among the other four models. So the model can be used to predict spinach plants in Sumatra.A well-prepared abstract enables the reader to identify the basic content of a document quickly and accurately, to determine its relevance to their interests, and thus to decide whether to read the document in its entirety.
The Implementation Of The Fletcher-Reeves Algorithm In Predicting The Growth Of Forest Plant Cultures Dwi Ramahdhani; Solikhun Solikhun
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.v2i1.2418

Abstract

Forest protection and development are essential because forests are the world's lungs. In addition, the HTI organization (modern manor backwoods) began to hide again. However, due to the great interest in wood to be used as raw material for material and property production lines, large organizations started to develop hamlet wood which was then marketed abroad, such as pressed wood, rattan, sawn timber, and done jobs for individuals in the area around the hamlet. By making a prediction, knowledge about the growth of forest plants can be known so that they can anticipate or minimize the risks that may arise. They can assist in determining policies and making decisions. This study aims to predict the growth of forest plants in the following year using an Artificial Neural Network Algorithm. The information used in this study is from the Central Bureau of Statistics from 2011 to 2022. The method of implementing this research uses the Fletcher-Reeves Algorithm, one of the Artificial Neural Network methods using 5 models, including 7-10-1, 7-15- 1, 7-20-1, 7-25-1, and 7-30-1. Of the five models, the structural model is 7-20-1 with an MSE value of 0.00037397. It can be said that this model can be used because it produces a fast combination and a short period of time.
The Performance Machine Learning Powel-Beale for Predicting Rubber Plant Production in Sumatera Siska Rama Dani; Solikhun Solikhun; Dadang Priyanto
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.v2i1.2420

Abstract

This study aims to predict rubber plants in Sumatra; rubber plants have a relatively high economic value; rubber sap must be cultivated because it is a product of the rubber plant, which is the raw material for the rubber industry, so in large quantities. Therefore, rubber sap, the selling value will increase so that it can increase farmers' income. Rubber production in Sumatra experiences ups and downs; therefore, this study aims to predict rubber plants using the Powell-Beale algorithm method, one of the Artificial Neural Network methods often used for data prediction, implemented using Matlab software. That supports it. This study does not discuss the prediction results. Still, it discusses the ability of the Powell-Beale algorithm to make predictions based on datasets of rubber plant production in recent years obtained from the Central Statistics Agency. Based on this data, a network architecture model will be formed and determined, including 6-10-1, 6-15-1, 6-30-1, 6-45-1 and 6-50-1. The best architecture is 6-15-1, with the lowest Performance/MSE test score of 0.00791984.
The Utilization Of The Conjugate Gradient Algorithm For Predicting School Year Expectations By Province Astri Rismauli Simbolon; Solikhun Solikhun
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.v2i1.2426

Abstract

Expected Length of School (HLS) is the length of school (in years) that is expected to be felt by children at a certain age in the future. It is assumed that the probability that the child will remain in school at the following ages is the same as the probability of the population attending school per total population for the current age. Length of School is also a benchmark for evaluating government programs in improving Human Resources that excel in the competition of technological advances. The purpose of this study is to apply the Conjugate Gradient Algorithm with the Best Performance for Predicting School Life Expectancy in Indonesia. Research data on the Expectation of Schooling in Indonesia consists of 10 Provinces obtained from the Central Statistics Agency from 2016 to 2021. This study uses 5 architectural models, namely 2-10-1, 2-15-1, 2-20-1, 2-25-1 and 2-30-1. Of the five architectural models used, the best architectural model is 2-3-1 with an MSE of 0.000000002 in two seconds. Based on this best architectural model, it will be used to predict the Expectation of Old Schools in Indonesia for the next five years, namely from 2022 to 2026.
Analysis of Vulnerability Assessment Technique Implementation on Network Using OpenVas Muhammad Muharrom; Afif Saktiansyah
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 2 No 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3297

Abstract

Vulnerability Assessment is an important method for identifying and analyzing security vulnerabilities within a network system. This research aims to identify security vulnerabilities within the PT. Dutakom Wibawa Putra network using OpenVAS as a research tool. In the vulnerability analysis phase, OpenVAS is utilized to scan the PT. Dutakom Wibawa Putra network and identify existing vulnerabilities. Subsequently, an evaluation is conducted on the identified vulnerabilities, including risk assessment and necessary mitigation recommendations. The outcomes of this study provide a clear overview of the security vulnerabilities present in the PT. Dutakom Wibawa Putra network. This research using method analysis of vulnerability assessment technique implementation on network using OpenVas, several significant vulnerabilities that could impact the security of the network and systems have been identified. The findings of this analysis report can serve as a foundation for developing improved security strategies and implementing effective mitigation measures. In conclusion, this study successfully applies Vulnerability Assessment techniques to the PT. Dutakom Wibawa Putra network using OpenVAS. The identified vulnerability analysis results offer valuable insights into security weaknesses that need to be addressed. It is hoped that this research can serve as a reference for PT. Dutakom Wibawa Putra and similar organizations in enhancing their network security through the implementation of effective Vulnerability Assessment techniques.
Combination of Smote and Random Forest Methods for Lung Cancer Classification Christopher Michael Lauw; Hairani Hairani; Ilham Saifuddin; Juvinal Ximenes Guterres; Muhammad Maariful Huda; Mayadi Mayadi
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 2 No 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3333

Abstract

Lung cancer is a network of cells that grow abnormally in the lungs. Lung cancer has four severity levels, namely stages 1 to 4. If lung cancer is not treated quickly, it is at risk of causing death. This research aimed to combine Synthetic Minority Over-sampling (Smote) and Random Forest methods for lung cancer classification. The method used was a combination of Smote and Random Forest. Smote was used to balance the data, while Random Forest was used to classify lung cancer data. The results showed that the combination of Smote and Random Forest methods obtained an accuracy of 94.1%, sensitivity of 94.5, and specificity of 93.7%. Meanwhile, without Smote, the accuracy is 89.1%, sensitivity is 55%, and specificity is 94.5%. The use of Smote can improve the performance of the Random Forest classification method based on accuracy and sensitivity. There was an increase of 5% in accuracy and a 39% increase in sensitivity.
Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms Abu Tholib; M Noer Fadli Hidayat; Supri yono; Resty Wulanningrum; Erna Daniati
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 2 No 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3364

Abstract

Student graduation is a very important element for universities because it relates to college accreditation assessment. One of them is at the Faculty of Engineering Nurul Jadid University, which has problems completing the study period within a predetermined time. So that it can be detrimental because accreditation is less than optimal, and the number of active students makes it less ideal in teaching and learning activities. This study aimed to compare the level of accuracy using the C4.5 algorithm and Naïve Bayes method in predicting graduation on time. The C4.5 and Naïve Bayes algorithms are one of the methods in the algorithm for classifying. Tests were carried out using the C4.5 and Naïve Bayes algorithms using Google Colab with Python programming language, then validated using 10-fold cross-validation. The results of this study indicate that the Naïve Bayes method has a higher accuracy value with an accuracy rate of 96.12%, while the C4.5 algorithm method is 93.82%.

Page 2 of 8 | Total Record : 78

 1   2   3   4   5 

Filter by Year

2022 2025


Reset
Filter By Issues
All Issue Vol. 4 No. 2 (2025): September 2025 Vol. 4 No. 1 (2025): March 2025 Vol. 3 No. 2 (2024): September 2024 Vol 3 No 2 (2024): September 2024 Vol. 3 No. 1 (2024): March 2024 Vol 3 No 1 (2024): March 2024 Vol 2 No 2 (2023): September 2023 Vol. 2 No. 2 (2023): September 2023 Vol 2 No 1 (2023): March 2023 Vol. 2 No. 1 (2023): March 2023 Vol. 1 No. 2 (2022): September 2022 Vol 1 No 2 (2022): September 2022 Vol 1 No 1 (2022): March 2022 Vol. 1 No. 1 (2022): March 2022 More Issue