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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 75 Documents
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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%.
Electric Vehicle Sales-Prediction Application Using Backpropagation Algorithm Based on Web Ramadhanti Ramadhanti; Hairani Hairani; Muhammad Innuddin
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.v2i2.3388

Abstract

The accuracy of predicting future product sales is needed to minimize losses and gain profits. Inventory of goods carried out manually or improper product inventory planning causes the number of goods to accumulate due to the small number of requests, so the goods are damaged. Therefore, a sales prediction system with high accuracy is needed to assist in stocking electric vehicles. This research aimed to predict electric vehicle sales using the web-based backpropagation method. This study uses the backpropagation method to predict electric vehicle sales data from 2015 to 2022. The data is divided into 84 instances as training data and 12 instances as testing data. The result of this study was that the backpropagation method obtained a MAPE error rate of 6.25%. Thus, the backpropagation method can be used for predicting electric vehicle sales because it has a very accurate performance level.
Sentiment Analysis and Topic Modeling of Kitabisa Applications using Support Vector Machine (SVM) and Smote-Tomek Links Methods I Nyoman Switrayana; Diki Ashadi; Hairani Hairani; Afrig Aminuddin
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.v2i2.3406

Abstract

Kitabisa is an Indonesian application that functions to raise funds online. Users can easily support various types of campaigns and donate funds to various social causes through the app. User reviews of the application are very diverse, and it is not sure whether user reviews of the application tend to be positive, neutral, or negative. This research aimed to analyze the sentiment of the Kitabisa application by modeling topics using Latent Dirichlet Allocation (LDA) and classifying user reviews using a Support Vector Machine (SVM). The scrapped dataset showed imbalanced dataset problems, so the SMOTE-Tomek Links oversampling technique was proposed. The results of this study show that using LDA produces five topics often discussed in 750 reviews. Then, the performance of SVM without using SMOTE-Tomek Links was 72% accuracy, 76% precision, 72% recall, and 64% f1 score. Meanwhile, using SMOTE-Tomek Links could significantly improve the performance, namely 98% accuracy, 98% precision, 98% recall, and 98% f1 score. Based on this research, the application of SVM achieved high performance for user sentiment classification, especially when the dataset was in a balanced state. Therefore, the SMOTE-Tomek Links oversampling technique is recommended for dealing with unbalanced sentiment datasets.
Determining and Managing Stock of Goods Based on Purchasing Patterns Using the Frequent Pattern Growth Algorithm Muhammad Aldi Zarkhasy; Christofer Satria
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 1 (2024): March 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i1.3416

Abstract

Stock inventory is one of the important work activities, because stock inventory is the main element in the field of commerce. Mistakes regarding stock inventory will result in fatal problems, especially when stock inventory management is still done manually or improper stock inventory planning can cause the amount of stock to pile up due to the small amount of demand from consumers, resulting in the stock being damaged, especially those contains elements of expiration because it is not sold. On the other hand, if the stock of goods is low while consumer demand is high, consumers will turn to other supermarkets to look for the goods they want. This can make supermarkets lose money because they cannot meet consumer needs. Therefore, a system is needed to look for product item combination patterns using association techniques in determining and managing stock. This research aims to look for product item combination patterns in previous period transaction data based on purchasing patterns in determining and managing inventory at supermarkets using the FP-Growth method. Where with Min. Support 30% and Min. Confidence 70% produces 12 rules then with Min. Support 45% and Min.Confidence 60% produces 6 rules. Based on the comparison and analysis results obtained using the FP-Growth method, in this study the Min limit was chosen. Support 30% and Min. Confidence 70% due to generating 12 association rules. This information can be used as a reference for supermarkets in making decisions in determining and managing stock of goods based on purchasing patterns.
Design of Fuel Monitoring Application for Reservoir Tanks in Army Fuel Supply Point on Military Logistics Corps Based on Internet of Things Rizky Safrizal Akbar; Fajar Kholid; Kasiyanto Kasiyanto; Dekki Widiatmoko; Afif Achmad
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 1 (2024): March 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i1.3737

Abstract

The Bekang Corps, a vital component of the Army responsible for logistics and transportation, plays a crucial role in maintaining the military's operational readiness and mobility. In the intricate landscape of military operations, the seamless integration of strategy, logistics, and tactics becomes imperative for success. Integrated logistical support, encompassing maintenance, supply, personnel, education, and training, and base facility support, serves as the backbone of effective military operations. However, the manual monitoring of underground fuel tanks at Storage and Supply Points (SPBT) presents challenges in terms of potential errors, time consumption, and significant efforts. The aim of this research is to address these issues by focusing on leveraging Internet of Things (IoT) technology to design and implement a monitoring application specifically tailored for the SPBT environment within the Army's Bekang Unit. This research method is aimed at providing a real-time solution for efficiently monitoring and managing fuel levels. By integrating the Float Level Switch sensor and NodeMCU ESP 8266 microcontroller, this research establishes a foundation in IoT. The Android application, developed using Android Studio, serves as the user interface, while Firebase functions as the real-time database, facilitating seamless communication and data exchange. The results of this research are the successful implementation of this IoT-based solution, which not only enhances the accuracy and responsiveness of fuel level monitoring but also contributes significantly to military operational efficiency. The anticipated significant contribution of the application includes the enhancement of military operational efficiency, the reduction of human error risks, and an increased sense of responsibility regarding fuel availability for operational needs.
Handling Imbalance Data using Hybrid Sampling SMOTE-ENN in Lung Cancer Classification Muhammad Abdul Latief; Luthfi Rakan Nabila; Wildan Miftakhurrahman; Saihun Ma'rufatullah; Henri Tantyoko
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 1 (2024): March 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i1.3758

Abstract

The classification problem is one instance of a problem that is typically handled or resolved using machine learning. When there is an imbalance in the classes within the data, machine learning models have a tendency to overclassify a greater number of classes. The model will have low accuracy in a few classes and high accuracy in many classes as a result of the issue. The majority of the data has the same number of classes, but if the difference is too great, it will differ. The issue of data imbalance is also evident in the data on lung cancer, where there are 283 positive classes and negative classes 38. Therefore, this research aims to use a hybrid sampling technique, combining Synthetic Minority Over-sampling Technique (SMOTE) with Edited Nearest Neighbors (ENN) and Random Forest, to balance the data of lung cancer patients who experience class imbalance. This research method involves the SMOTE-ENN preprocessing method to balance the data and the Random Forest method is used as a classification method to predict lung cancer by dividing training data and testing 10-fold cross validation. The results of this study show that using SMOTE-ENN with Random Forest has the best performance compared to SMOTE and without oversampling on all metrics used. The conclusion is using the SMOTE-ENN hybrid sampling technique with the Random Forest model significantly improves the model's ability to identify and classify data.
Exploring Customer Purchasing Patterns: A Study Utilizing FP-Growth Algorithm on Supermarket Transaction Data Hairani Hairani; Juvinal Ximenes Guterres
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 1 (2024): March 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i1.3874

Abstract

The need to analyze consumer purchasing patterns using association techniques also lies in the increasingly fierce competition in the retail market. Supermarkets face the challenge of understanding their customers' buying patterns. By utilizing association techniques, supermarkets can identify customer buying trends and quickly and appropriately adjust their strategies. Thus, analyzing consumer purchasing patterns using association techniques is no longer an option but an urgent need for supermarkets that want to survive and thrive in a changing market. Therefore, this study aimed to analyze purchasing patterns in supermarkets using the FP-Growth method to understand purchasing behavior and identify relevant patterns from transaction data. The method used in this research was the FP-Growth association method to create association rules from customer transaction data. The findings of this research were the use of the FP-Growth method in analyzing supermarket customer purchasing patterns, which obtained 10 association rules for 2 itemsets and 11 association rules for 3 itemsets based on a minimum Support value of 30% and a minimum Confidence of 70%. The association rules generated by the FP-Growth method on 2 itemsets and 3 itemsets simultaneously bring up items often purchased by customers with the same pattern, namely Cooking Oil, Eggs, Flour, and Candy. This research concludes that the association rules formed can be used as a benchmark by supermarkets in preparing stock items and making strategies to increase sales for more profit.
Clustering Biplot on Tourist Visits in Indonesia Isma Muthahharah; Zakiyah Mar'ah
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 1 (2024): March 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i1.3890

Abstract

This research aims to find out whether or not many tourists visited Indonesia after Covid-19 by clustering. This will generate foreign exchange earnings and contribute directly to the country's economic growth. The analytical method used in this research is K-Medoids. K-Medoids is a partition clustering technique that groups a collection of n objects into k clusters by utilizing the objects in the collection of objects to represent a cluster called a medoid. The data in this research used secondary data related to foreign tourist visits to Indonesia from several publication sources in 2017-2021. The results of this research show that there were 3 clusters obtained: Cluster 1 shows the number of tourist visits visiting Indonesia in 2017, 2018 and early 2019 because the Covid-19 pandemic has not yet occurred, Cluster 2 shows that there were no tourist visits in 2020 due to the start of the Covid-19 pandemic, and Cluster 3 indicates low tourist arrivals in 2021 due to the Covid 19 pandemic which temporarily prohibited foreign tourists from visiting Indonesia.
Thesis Topic Modeling Study: Latent Dirichlet Allocation (LDA) and Machine Learning Approach Hairani Hairani; Mengas Janhasmadja; Abu Tholib; Juvinal Ximenes Guterres; Yuri Ariyanto
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.v3i2.4375

Abstract

The thesis reports housed in the campus repository have yet to be analyzed to reveal valuable knowledge patterns. Analyzing trends in thesis research topics can facilitate the selection of research topics, aid in mapping research areas, and identify underexplored topics.Therefore, this research aims to model and classify thesis topics using Latent Dirichlet Allocation (LDA) and the Naïve Bayes and Support Vector Machine (SVM) methods. This study employs the LDA method for thesis topic modeling, while SVM and Naïve Bayes are used for classifying these topics. The research results show that LDA successfully modeled five of the most popular thesis topics, namely two related to computer networks, two on software engineering, and one on multimedia. For thesis topic classification, the SVM method demonstrated higher accuracy than Naïve Bayes, reaching 92.80% after the data was balanced using Synthetic Minority Oversampling Technique (SMOTE). The implication of this study is that the topic modeling approach using LDA is able to identify dominant thesis topics. In addition, the SVM classification results obtained better accuracy than Naïve Bayes in the thesis topic classification task.

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