Journal of Systems Engineering and Information Technology
International Journal of Systems Engineering and Information Technology (JOSEIT) is an international journal published by Ikatan Ahli Informatika Indonesia (IAII / Association of Indonesian Informatics Experts). The research article submitted to this online journal will be peer-reviewed. The accepted research articles will be available online (free download) following the journal peer-reviewing process. The language used in this journal is English. JOSEIT is a peer-reviewed, blinded journal dedicated to publishing quality research results in Computers Engineering and Information Technology but is not limited implicitly. All journal articles can be read online for free without a subscription because all journals are open-access.
Articles
40 Documents
<![CDATA[Analysis Of the Behavior of Cyberattacks on Online Services Using the Cyber Threat Classification ]]>
<![CDATA[Isaev Sergey Vladislavovich]]>;
<![CDATA[Kononov Dmitry Dmitrievich]]>
<![CDATA[ Journal of Systems Engineering and Information Technology (JOSEIT) Vol 1 No 2 (2022): September 2022 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia ]]>
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DOI: 10.29207/joseit.v1i2.4944
<![CDATA[The paper contains a study of the dynamics of attacks on online services using the categorization of cyber threats by type in the corporate network of the Krasnoyarsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences. The study was conducted using online service logs and allows solving pressing issues related to ensuring the built-in security of web services, such as: identifying both current and future cybersecurity risks. A summary of the most important logging and analysis techniques is provided. The authors describe the nature and content of the data sources and the software used. The extensive observation period of the study is one of its outstanding features. The structure of the processing system is provided and software tools for attack analysis and categorization are created. The paper shows that using categorized sampling allows for the detection of periodicity and the identification of patterns in specific types of attacks. A correlation matrix was created based on the type of attack. Except for Command Injection, Directory Browsing, and Java Code Injection attacks, which can be aggregated, the research found that most attack types had poor correlation. Based on the classification of cyber threats, the authors proposed a heuristic technique of risk comparison.]]>
<![CDATA[Analysis of the Effect Emotional Intelligence on Understanding Level in Programming Algorithm Learning ]]>
<![CDATA[Sri Handani Widiastuti]]>;
<![CDATA[Nur Imansyah]]>;
<![CDATA[Abdul Zain]]>
<![CDATA[ Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 1 (2023): March 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia ]]>
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DOI: 10.29207/joseit.v2i1.4996
<![CDATA[The course Algorithms and Programming in the Informatics Engineering study program is given in the first and second semesters, as this course is a scientific course that serves as a basis for advanced programming courses in higher semesters. It is often the case that intelligent graduates obtain good academic grades during their studies and become outstanding students, but when working in their field of expertise, their performance is not as good as graduates who had lower academic grades. Success is not only determined by intellectual intelligence (IQ), but also by other types of intelligence. Intellectual intelligence or cognitive intelligence is one type of intelligence, while non-intellectual intelligence includes other types of intelligence outside of cognitive intelligence. One of this non-intellectual intelligence is emotional intelligence. Intellectual intelligence and non-intellectual intelligence play equally important roles in supporting individual success. This research discusses the analysis of the effect of emotional intelligence on the level of understanding of algorithms and programming using the linear regression method implemented with an application. The sample taken is students of the Informatics Engineering study program in the third and fourth semesters. With the results of the influence of emotional intelligence on the understanding of Algorithms and Programming, it can provide a solution to improve students' understanding of the Algorithms and Programming course and other computer courses in the scientific field of the Informatics Engineering study program. The application is created using the Python programming language.]]>
<![CDATA[Classification Analysis of Back propagation-Optimized CNN Performance in Image Processing ]]>
<![CDATA[Putrama Alkhairi]]>;
<![CDATA[Agus Perdana Windarto]]>
<![CDATA[ Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 1 (2023): March 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia ]]>
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DOI: 10.29207/joseit.v2i1.5015
<![CDATA[This study aims to optimize the performance of the Convolutional Neural Network (CNN) in the image classification task by applying data augmentation and fine-tuning techniques to a case study of mammal classification. In this study, we took a fairly complex image classification dataset and used the CNN model as a basis for training and evaluating the performance of the model compared to Back propagation. From this study, the CNN VGG16 architecture optimized with ADAM optimization has been compared with the Back propagation optimization of SGD. We also conducted a literature review on several related studies and basic concepts in CNN, such as convolution, pooling, and fully connected layers. The research methodology involves creating datasets using data augmentation techniques, model training using fine-tuning techniques, and testing model performance using a number of evaluation metrics, including accuracy, precision, and recall. The results of this study indicate that the techniques used have succeeded in improving the performance of the CNN model in complex image classification tasks with accuracy in identifying and monitoring animal species more accurately, with an accuracy of 91.18% for the best model. Model accuracy increased by 2% after applying data augmentation and fine-tuning techniques to the CNN model. These results indicate that the techniques applied in this study can be a good alternative in improving the performance of the CNN model in the image classification task.]]>
<![CDATA[Classification of Bullying Comments on YouTube Streamer Comment Sections Using Naïve Bayes Classification ]]>
<![CDATA[Ahlida Nikmatul H]]>;
<![CDATA[Didih Rizki C]]>;
<![CDATA[Christian S.K. Aditya]]>
<![CDATA[ Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 1 (2023): March 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia ]]>
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DOI: 10.29207/joseit.v2i1.5016
<![CDATA[One of the social media crimes that is rampant in the current era is cyberbullying. Cyberbullying is a form of intimidation by someone to harass other people using technological devices. this research uses a design for information decision making that aims to get the expected results. the data collection process is carried out manually with a time frame of 1 week by watching the live broadcast of the online game YouTube streamer then sorting out some bullying and non-bullying comments in the comment’s column. Data labeling is done manually. The data obtained amounted to 1000 with 500 negative comments and 500 positive comments. The above test can be concluded that from the distribution of test data there are 90% - 10% have results that are superior to the results of other tests with an increase of 4% in the Naïve Bayes weighting Gain Ratio method. Based on the test data, the results of precision, recall, F1-score and accuracy of the Naïve Bayes classification method are obtained. The test analysis above can be concluded that from the distribution of test data, 90% - 10% have results that are superior to other test results with a 4% increase in the Naïve Bayes weighting Gain Ratio method. The existence of increased accuracy results is due to a randomized data processing process.]]>
<![CDATA[Research Growth of Engineering Faculty Universitas Negeri Padang: A Bibliometric Study of Journal ]]>
<![CDATA[Yose Indarta]]>;
<![CDATA[Ronal Watrianthos]]>;
<![CDATA[Agariadne Dwinggo Samala]]>
<![CDATA[ Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 1 (2023): March 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia ]]>
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DOI: 10.29207/joseit.v2i1.5017
<![CDATA[This research aimed to conduct a comprehensive literature review of all papers from the beginning of time published by the Faculty of Engineering at Universitas Negeri Padang and indexed in the SINTA database using bibliometrics analysis. It started with a query to the DOI (Digital Object Identifier) database. The papers from this scholarly publication may be accessed here. A total of 1094 papers from 7 journals were collected. Each paper published by the Engineering Faculty at Universitas Negeri Padang receives an average of 0.42 citations. Given the insignificance of this average, it seems likely that the publication's contribution has yet to be fully recognized and has only had a little effect on the global state of knowledge. According to the findings, Universitas Negeri Andalas researchers collaborate the most with their peers around the country. Contrast this with the declining frequency with which multinational teams work together. With 38 articles published and ten citations obtained, Ahmaddul Hadi of the Informatics Department stood out as the most productive author. Research conducted by this faculty has been widely disseminated due to its publication in the reputable academic journal Inovasi Vokasi Dan Teknologi (INVOTEK).]]>
<![CDATA[Predicting ICO Prices Using Artificial Neural Network and Ridge Regression Algorithm ]]>
<![CDATA[Trầhn Kim Toại]]>;
<![CDATA[Võ Thị Xuân Hạn]]>;
<![CDATA[Võ Min Huân]]>
<![CDATA[ Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 1 (2023): March 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia ]]>
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DOI: 10.29207/joseit.v2i1.5022
<![CDATA[An Initial Coin Offering (ICO) is a method of raising funds for digital currency projects. Investors purchase these coins at a very low initial price before they are released. These coins are then listed on the trading platform, and their prices may increase rapidly if the currency performs well. After six months of release, ICO evaluation is the expected time for investors to profit. A dataset consisting of 109 ICOs was constructed from reputable websites after data preprocessing. Correlation analysis of 12 inputs revealed issues of multicollinearity, leading to biased regression model results. Overfitting occurred when using the regression model. To address these limitations, the Ridge regression method resolved the issues with the ICO data. An artificial neural network model addressed the complex nonlinear relationships between inputs and ICO prices. By adjusting parameters to achieve the best performance according to the Root Mean Square Error, R-squares, and Mean Absolute Error metrics, the results showed that the Ridge regression algorithm with a test set of three ICOs achieved accuracy ranging from 63% to 92% of ICO prices, while the artificial neural network model predicted with 98% accuracy depending on the metric used.]]>
<![CDATA[Monitoring System for Temperature and Humidity Sensors in the Production Room Using Node-Red as the Backend and Grafana as the Frontend ]]>
<![CDATA[Khoirul Anam]]>;
<![CDATA[Difa Nur Rofi]]>;
<![CDATA[Ruci Meiyanti]]>
<![CDATA[ Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia ]]>
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DOI: 10.29207/joseit.v2i2.5222
<![CDATA[The client from the TRIAS project is facing issues with a decrease in production quality or defective products. They require a monitoring system for temperature and humidity in the production area, providing real-time notifications in case of any anomalies in temperature and humidity. However, the project has a limited budget, which poses a challenge for the contractor in developing a monitoring system that tracks temperature and humidity changes using temperature and humidity sensors as the data source. It should also provide alarms if the temperature and humidity values exceed the standard values for the room. Additionally, the pricing offer should be adjusted using information technology. The research methodology used in this study includes qualitative methods such as observation, literature review, and interviews to gather data on the mentioned issues. The SWOT method is used to analyze business process problems, while the Waterfall method is employed for system development. Based on the research findings, the researcher concludes that this project requires cost reduction in material usage and also needs a data visualization application for the mentioned sensors. The visualization application system utilizes Grafana as the frontend, chosen for its high flexibility in processing. The temperature and humidity data obtained from the sensors will be recorded by Node-Red as the backend and synchronized on the server. The data stored on the server will be saved in a MySQL database. The data from the database will be synchronized with Grafana for processing and visualization, presenting the data in easily understandable graphical forms.]]>
<![CDATA[Acceleration and Clustering of Liver Disorder Using K-Means Clustering Method with Mahout’s Library ]]>
<![CDATA[Tariq bin Samer]]>;
<![CDATA[Cahyo Darujati]]>
<![CDATA[ Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia ]]>
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DOI: 10.29207/joseit.v2i2.5334
<![CDATA[Evaluation of liver disorders was performed to observed and clustered in Big Data environment applications. However, since liver disorder is a common illness, global awareness of such cases can be life threatening, therefore the urge to avoid and study must be essential. The idea of parallel computing is established on the basis of the K-means method. The MapReduce framework is used to complete multi-node data processing, and a solution to the MapReduce K-Means method is given. The ultimate goal is to establish clusters that allow each entity to be examined and assigned to a certain cluster. These algorithms are designed to accelerate computations, reduce the volume of enormous data that must be computed, and improve the efficiency of arithmetic operations. The combination of theoretical analysis and experimental evaluation is very significant.]]>
<![CDATA[Performance Analysis of Mobile Ad-Hoc Networks Based on TCP and UDP Traffic on AODV Protocol for Warship Communication ]]>
<![CDATA[Alon Jala Tirta Segara]]>;
<![CDATA[Afifah Dwi Ramadhani]]>
<![CDATA[ Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia ]]>
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DOI: 10.29207/joseit.v2i2.5343
<![CDATA[This research focuses on evaluating two key parameters in Mobile Ad-Hoc Networks (MANETs) that use the AODV protocol for warship communication, namely the packet delivery ratio (PDR) and end-to-end delay. PDR describes the percentage of data packets that successfully reach their destination without loss or damage during transmission. The study will analyze and compare PDR in MANETs with TCP and UDP traffic to understand the reliability and efficiency of the AODV protocol in data delivery. Furthermore, the research will also assess end-to-end delay, which measures the time it takes data packets to reach their final destination. Evaluating this delay will provide insights into the network's responsiveness in transmitting data between source and destination. The results of this research will offer valuable information about the performance of MANETs using the AODV protocol with TCP and UDP traffic. These findings can be used to optimize warship communication systems by selecting the most suitable protocol and traffic to achieve high PDR and minimal end-to-end delay; this study has the potential to serve as a critical foundation for developing reliable and efficient mobile ad hoc networks for military communication in dynamic and challenging environments.]]>
<![CDATA[Overview and Exploratory Analyses of CICIDS 2017 Intrusion Detection Dataset ]]>
<![CDATA[Akinyemi Oyelakin]]>;
<![CDATA[Ameen A.O]]>;
<![CDATA[Ogundele T.S]]>;
<![CDATA[Salau-Ibrahim T]]>;
<![CDATA[Abdulrauf U.T]]>;
<![CDATA[Olufadi H.I]]>;
<![CDATA[Ajiboye I.K]]>;
<![CDATA[Muhammad-Thani S]]>;
<![CDATA[Adeniji I. A]]>
<![CDATA[ Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia ]]>
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DOI: 10.29207/joseit.v2i2.5411
<![CDATA[Intrusion detection systems are used to detect attacks on a network. Machine learning (ML) approaches have been widely used to build such intrusion detection systems (IDSs) because they are more accurate when built from a very large and representative dataset. Recently, one of the benchmark datasets that are used to build ML-based intrusion detection models is the CICIDS2017 dataset. The data set is contained in eight groups and was collected from the Data Set & Repository of the Canadian Institute of Cyber Security. The data set is available in both PCAP and net flow formats. This study used the net flow records in the CIDIDS2017 dataset, as they were found to contain newer attacks, very large, and useful for traffic analysis. Exploratory data analysis (EDA) techniques were used to reveal various characteristics of the dataset. The general objective is to provide more insight into the nature, structure, and issues of the data set so as to identify the best ways to use it to achieve improved ML-based IDS models. Furthermore, some of the open problems that can arise from the use of the dataset in any machine learning-based intrusion detection systems are highlighted and possible solutions are briefly discussed. The EDA techniques used revealed important relationships between the input variables and the target class. The study concluded that the EDA can better influence the decision about future IDS research using the dataset.]]>
