Journal of Systems Engineering and Information Technology (JOSEIT)最新文献

The Analysis Of The Effect Of Emotional Intelligence On Understanding Level In Programming Algorithm Learning 情绪智力对编程算法学习理解水平的影响分析

Journal of Systems Engineering and Information Technology (JOSEIT) Pub Date : 2023-03-31 DOI: 10.29207/joseit.v2i1.4996

Sri Handani Widiastuti, Nur Imansyah, Abdul Zain

{"title":"The Analysis Of The Effect Of Emotional Intelligence On Understanding Level In Programming Algorithm Learning","authors":"Sri Handani Widiastuti, Nur Imansyah, Abdul Zain","doi":"10.29207/joseit.v2i1.4996","DOIUrl":"https://doi.org/10.29207/joseit.v2i1.4996","url":null,"abstract":"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. \u0000This 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","PeriodicalId":367560,"journal":{"name":"Journal of Systems Engineering and Information Technology (JOSEIT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122478988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of Bullying Comments on YouTube Streamer Comment Sections Using Naïve Bayes Classification YouTube流媒体评论部分使用Naïve贝叶斯分类的欺凌评论分类

Journal of Systems Engineering and Information Technology (JOSEIT) Pub Date : 2023-03-31 DOI: 10.29207/joseit.v2i1.5016

Ahlida Nikmatul H, Didih Rizki C, Christian S.K. Aditya

{"title":"Classification of Bullying Comments on YouTube Streamer Comment Sections Using Naïve Bayes Classification","authors":"Ahlida Nikmatul H, Didih Rizki C, Christian S.K. Aditya","doi":"10.29207/joseit.v2i1.5016","DOIUrl":"https://doi.org/10.29207/joseit.v2i1.5016","url":null,"abstract":"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.","PeriodicalId":367560,"journal":{"name":"Journal of Systems Engineering and Information Technology (JOSEIT)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115004417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification Analysis of Back propagation-Optimized CNN Performance in Image Processing 反向传播优化CNN图像处理性能的分类分析

Journal of Systems Engineering and Information Technology (JOSEIT) Pub Date : 2023-03-31 DOI: 10.29207/joseit.v2i1.5015

Putrama Alkhairi, A. Windarto

{"title":"Classification Analysis of Back propagation-Optimized CNN Performance in Image Processing","authors":"Putrama Alkhairi, A. Windarto","doi":"10.29207/joseit.v2i1.5015","DOIUrl":"https://doi.org/10.29207/joseit.v2i1.5015","url":null,"abstract":"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.","PeriodicalId":367560,"journal":{"name":"Journal of Systems Engineering and Information Technology (JOSEIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117043791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Research Growth of Engineering Faculty Universitas Negeri Padang: A Bibliometric Study of Journal 巴东大学工程学院研究成长:期刊文献计量学研究

Journal of Systems Engineering and Information Technology (JOSEIT) Pub Date : 2023-03-31 DOI: 10.29207/joseit.v2i1.5017

Yose Indarta, Ronal Watrianthos, Agariadne Dwinggo Samala

{"title":"Research Growth of Engineering Faculty Universitas Negeri Padang: A Bibliometric Study of Journal","authors":"Yose Indarta, Ronal Watrianthos, Agariadne Dwinggo Samala","doi":"10.29207/joseit.v2i1.5017","DOIUrl":"https://doi.org/10.29207/joseit.v2i1.5017","url":null,"abstract":"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).","PeriodicalId":367560,"journal":{"name":"Journal of Systems Engineering and Information Technology (JOSEIT)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122091481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting ICO Prices Using Artificial Neural Network and Ridge Regression Algorithm 利用神经网络和岭回归算法预测ICO价格

Journal of Systems Engineering and Information Technology (JOSEIT) Pub Date : 2023-03-31 DOI: 10.29207/joseit.v2i1.5022

Trầhn Kim Toại, Võ Thị Xuân Hạn, Võ Min Huân

{"title":"Predicting ICO Prices Using Artificial Neural Network and Ridge Regression Algorithm","authors":"Trầhn Kim Toại, Võ Thị Xuân Hạn, Võ Min Huân","doi":"10.29207/joseit.v2i1.5022","DOIUrl":"https://doi.org/10.29207/joseit.v2i1.5022","url":null,"abstract":"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.","PeriodicalId":367560,"journal":{"name":"Journal of Systems Engineering and Information Technology (JOSEIT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124230625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Analysis Of the Behavior of Cyberattacks on Online Services Using the Cyber Threat Classification 基于网络威胁分类的在线服务网络攻击行为分析

Journal of Systems Engineering and Information Technology (JOSEIT) Pub Date : 2022-09-25 DOI: 10.29207/joseit.v1i2.4944

Isaev Sergey Vladislavovich, Kononov Dmitry Dmitrievich

{"title":"Analysis Of the Behavior of Cyberattacks on Online Services Using the Cyber Threat Classification","authors":"Isaev Sergey Vladislavovich, Kononov Dmitry Dmitrievich","doi":"10.29207/joseit.v1i2.4944","DOIUrl":"https://doi.org/10.29207/joseit.v1i2.4944","url":null,"abstract":"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.","PeriodicalId":367560,"journal":{"name":"Journal of Systems Engineering and Information Technology (JOSEIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130516573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault Detection of Mechanical Equipment Failure Detection Using Intelligent Data Analysis 基于智能数据分析的机械设备故障检测

Journal of Systems Engineering and Information Technology (JOSEIT) Pub Date : 2022-09-25 DOI: 10.29207/joseit.v1i2.4943

Maksim Andreevich Kovito

引用次数: 1

Optimization of Flood Prediction using SVM Algorithm to determine Flood Prone Areas 基于SVM算法确定洪水易发区域的洪水预测优化

Journal of Systems Engineering and Information Technology (JOSEIT) Pub Date : 2022-09-25 DOI: 10.29207/joseit.v1i2.1995

Saruni Dwiasnati, Yudo Devianto

{"title":"Optimization of Flood Prediction using SVM Algorithm to determine Flood Prone Areas","authors":"Saruni Dwiasnati, Yudo Devianto","doi":"10.29207/joseit.v1i2.1995","DOIUrl":"https://doi.org/10.29207/joseit.v1i2.1995","url":null,"abstract":"Flooding is one thing that can slow down the economic pace in the affected area. Bandung is called the city of flowers and the city of fashion because the nickname makes Bandung a city with a variety of fashions growing in multiple places as a starting point for the buying and selling process. Not only did Bandung spawn fashions that became hits every year, but it also had many Meccas of traditional food preparation that were extraordinarily unique and interesting. Creating a flood-prone area model can make it easier to provide information for communities in Bandung Prefecture that belong to flood-prone and non-flood-prone areas. The SVM algorithm is a technique that can be used in the case of classification and regression, which is very popular lately. SVM is in a class with Artificial Neural Networks (ANN) in terms of features and conditions of problems that can be solved, and to be able to increase its accuracy it uses what can be optimized with PSO (Particle Swarm Optimization), where the test data is used BNPB official website data, BPS Bandung District and BMKG processed. The accuracy rate generated by using the SVM algorithm is 85.71% and the generated AUC is 0.841, while the accuracy rate generated by using the PSM-based SVM algorithm is 97.62%. and AUC produced at 1,000.","PeriodicalId":367560,"journal":{"name":"Journal of Systems Engineering and Information Technology (JOSEIT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116290263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Educational Data Mining (EDM) Prediction of Student Study Period with Naïve Bayes Classifier and C4.5 Algorithm Comparison 利用Naïve贝叶斯分类器与C4.5算法比较教育数据挖掘(EDM)预测学生学习时间

Journal of Systems Engineering and Information Technology (JOSEIT) Pub Date : 2022-09-25 DOI: 10.29207/joseit.v1i2.4942

Galih

引用次数: 0

Application of Data Mining for Visit Prediction at Amikom Creative Economy Park 数据挖掘在Amikom创意经济园区游客预测中的应用

Journal of Systems Engineering and Information Technology (JOSEIT) Pub Date : 2022-09-25 DOI: 10.29207/joseit.v1i2.4941

Rumini, Norhikmah

{"title":"Application of Data Mining for Visit Prediction at Amikom Creative Economy Park","authors":"Rumini, Norhikmah","doi":"10.29207/joseit.v1i2.4941","DOIUrl":"https://doi.org/10.29207/joseit.v1i2.4941","url":null,"abstract":"A creative economy park is a place designed with strategic goals for technology skills collaboration, information and knowledge transfer, creation of innovative high-tech enterprises and entrepreneurs, introduction of new technology industries in creative economy enterprises to promote economic development. Yogyakarta Amikom University has been declared a Creative Economy Park and is known as Amikom Creative Economy Park (ACEP). ACEP includes multiple multimedia environments for targeting businesses such as software development, film, television, games, radio, animation, advertising, investment consulting, and project design. Every year, the number of institutions visiting Amikom Yogyakarta University carries the slogan Amikom Creative Economy Park with a fairly busy program of visits. The agenda for accepting this visit was carried out by Amikom's Public Relations Department (DKUI, Directorate of Public Relations and International Affairs). The evolution of visitor numbers from year to year, forecasts must be made to support the planning and preparation process when receiving visits. This research will discuss the trend of visitors having a comparative study in Amikom Creative Economy Park in the future. The data used in this study is visitor data from January 2019 to December 2019. This predictive data analysis uses the Autoregressive Integrated Moving Average (ARIMA) method and Exponential Smoothing as a comparison for the accuracy of the prediction. With the forecast of this visit, the planning and preparation for the Directorate of Public Relations and International Affairs and for the University AMIKOM Yogyakarta is to be done.","PeriodicalId":367560,"journal":{"name":"Journal of Systems Engineering and Information Technology (JOSEIT)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115349377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0