2021 4th International Conference of Computer and Informatics Engineering (IC2IE)最新文献_第8页

Best Ratio Size of Image in Steganography using Portable Document Format with Evaluation RMSE, PSNR, and SSIM 使用评估RMSE, PSNR和SSIM的便携式文档格式的隐写术中图像的最佳比例大小

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649198

Irzal Ahmad Sabilla, Maulida Meirisdiana, Dwi Sunaryono, M. Husni

{"title":"Best Ratio Size of Image in Steganography using Portable Document Format with Evaluation RMSE, PSNR, and SSIM","authors":"Irzal Ahmad Sabilla, Maulida Meirisdiana, Dwi Sunaryono, M. Husni","doi":"10.1109/ic2ie53219.2021.9649198","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649198","url":null,"abstract":"Steganography is a data encryption method to hide messages into files. Files that are often used are images as a place to hide data. One type of steganography that is often used is Least Significant Bit (LSB). The LSB mechanism inserts data into the last bit of each pixel in the cover file. In this paper we build application system encryption to insert Portable Document Format (PDF) files into images. Every 700 characters stored in a PDF file are inserted into the image using LSB Steganography. The images used are divided into three size classes, namely 200*200 pixels, 400*400 pixels, and 1000*1000 pixels with 100 experimental images in each class. The results of the insertion are compared with the original image using the evaluation of Root Mean Squared Error (RMSE), Structural Similarity Index (SSIM), and Peak to Signal Ratio (PSNR). The experimental results show that an image with a size of 1000*1000 shows the best results with RMSE measurements showing an average result of 7.68e-06 dB, SSIM showing an average result of 0.999999985 dB, and PSNR showing an average result of 102.2891185 dB. From the measurement results of the evaluation method, the best ratio for inserting a PDF file into an image is that every two bits of an image can accommodate one bit of a PDF file.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128985933","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: 4

Analysis Reliability of Distribution Network Interconnected with Distributed Generation 分布式发电互联配电网可靠性分析

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649236

Y. Siregar, Z. Pane, Rizky Kurniawan

引用次数: 0

The Design of Information Security Risk Management: A Case Study Human Resources Information System at XYZ University 信息安全风险管理的设计:以XYZ大学人力资源信息系统为例

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649035

Agus Anang, Arfive Gandhi, Y. G. Sucahyo

{"title":"The Design of Information Security Risk Management: A Case Study Human Resources Information System at XYZ University","authors":"Agus Anang, Arfive Gandhi, Y. G. Sucahyo","doi":"10.1109/ic2ie53219.2021.9649035","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649035","url":null,"abstract":"Human Resource Information System (HRIS) as one of the vital information systems at XYZ University, is expected to provide support in accelerating the achievement of XYZ University's strategic goals as a healthy university based on Good University Governance (GUG) through accelerating the provision of relevant, timely, and quality information. However, the management of HRIS is still not in compliance with information security standards, as can be seen from several incidents and the management of these incidents, which are still accidental. The purpose of this study was to obtain a design for HRIS information security risk management. This study uses a qualitative method where data collection is done by interview, observation, and literature review. ISO/IEC 27005:2018 is used as an information security risk assessment, while risk control recommendation uses SNI ISO/IEC 27001:2013. This study resulted in 40 of these risks: 12 High risks, 19 Moderate risks, six Low risks, and three Very Low risks. The results of this study are the design of HRIS's information security risk management at XYZ University.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133244316","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

Enhancing Nearest Centroid with Coverage Principle for Classification Problem 基于覆盖原理的最近质心增强分类问题

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649329

A. H. Ismail, N. Hartono, S. Zeybek, H. T. N. Ignatius

引用次数: 0

Analysis of KNN in Classification of Firearms Sounds using Fast Fourier Transform 基于快速傅里叶变换的KNN在枪械声音分类中的分析

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649269

Muhammad Ari Afriansyah, Opim Salim Sitompul, S. Suwilo

引用次数: 0

Questions Classification Based on Revised Bloom's Taxonomy Cognitive Level using Naive Bayes and Support Vector Machine 基于朴素贝叶斯和支持向量机的修正Bloom分类法认知水平的问题分类

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649187

Annisa Syafarani Callista, Oktariani Nurul Pratiwi, E. Sutoyo

{"title":"Questions Classification Based on Revised Bloom's Taxonomy Cognitive Level using Naive Bayes and Support Vector Machine","authors":"Annisa Syafarani Callista, Oktariani Nurul Pratiwi, E. Sutoyo","doi":"10.1109/ic2ie53219.2021.9649187","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649187","url":null,"abstract":"Education is an essential aspect in building the social value and norm to produce individuals who can think in high order thinking through learning and teaching activities. As technology keeps growing, an online learning platform has emerged. This platform is called e-Learning. e-Learning allows teachers to save many questions into the e-Learning question bank. However, these questions need to be reviewed so the questions can be matched with the achievement of competence. One educational identification standard that is often to improve the quality of the questions is Bloom's Taxonomy. Bloom's Taxonomy was created in 1956 and revised in 2001. This study compares the performance of the Support Vector Machine and Naïve Bayes algorithms to classify quiz questions based on the cognitive level of Revised Bloom's Taxonomy. In this study, the dataset received two treatments in handling the imbalanced class. One dataset is using SMOTE method, and one another is not using any oversampling methods. The result shows that classification with oversampling datasets had better results than those without oversampling. The Support Vector Machine algorithm with SMOTE has the highest accuracy of 98%, rather than the Naïve Bayes algorithm with SMOTE has an accuracy of 91%.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131290953","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

Clustering Ornamental Plants Turnover Data using K-Means Algorithm 基于K-Means算法的观赏植物周转数据聚类

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649276

Faldza Fahrezy Arwy, Yaddarabullah, H. Permana

{"title":"Clustering Ornamental Plants Turnover Data using K-Means Algorithm","authors":"Faldza Fahrezy Arwy, Yaddarabullah, H. Permana","doi":"10.1109/ic2ie53219.2021.9649276","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649276","url":null,"abstract":"Ornamental plants are a commodity with high production in Indonesia, with a 17.61 million stalk increase recorded in 2018. (9.55%). Ornamental plants have capability enterprise possibilities in Indonesia as properly. The increase and decrease in ornamental plant turnover can be attributed to a variety of factors such as beauty awareness, the development of the tourism industry, ornamental plant trends, and the construction of housing and hotel complexes. A few of the factors mentioned can have an indirect impact on the sustainability of the ornamental plant business. To resolve these concerns, the grouping method was used with K-Means Clustering to determine the equation of ornamental plant turnover data based on plant commodities and monthly turnover values. Clustering with K-Means Algorithm is used in this study to group turnover data based on crop commodities and turnover value. The WEKA application's grouping results utilizing the K-Means Clustering Algorithm resulted in two clusters with values of 11% (8 data) and 89% (66 data) from a total of 74 data, where the two cluster values appeared after three time iterations.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116412994","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

Classification of Questions Based on Difficulty Levels using Support Vector Machine and Naïve Bayes Algorithms for Imbalanced Class 基于难易度的问题分类:支持向量机和Naïve贝叶斯算法在不平衡类中的应用

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649149

Danny Pratama, Oktariani Nurul Pratiwi, E. Sutoyo

{"title":"Classification of Questions Based on Difficulty Levels using Support Vector Machine and Naïve Bayes Algorithms for Imbalanced Class","authors":"Danny Pratama, Oktariani Nurul Pratiwi, E. Sutoyo","doi":"10.1109/ic2ie53219.2021.9649149","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649149","url":null,"abstract":"Quiz questions are crucial evaluations in measuring student learning development because they are one of the lecturers' benchmarks for providing learning materials. The accuracy of the results of measuring student competency achievement is important because it will be used as a benchmark for assessment by lecturers, therefore a question instrument that functions well is needed in distinguishing between students who have high abilities and students who have low abilities based on defined criteria. A good question, that is, when a question has a balanced level of difficulty (proportional), it is said that the question is good. However, a question should be neither too difficult nor too easy. On that basis, grouping the level of difficulty of the questions should be done to make a package of questions that fit the portion. The case study taken by the researcher is a Data Warehouse S1 Information System at Telkom University. The case study was taken because the Data Warehouse course is a compulsory subject in the Information Studies Program at Telkom University. In doing the classification, the writer compares the Naive Bayes algorithm and the Support Vector Machine. The comparison results obtained the highest accuracy with the algorithm method SVM Classification. The accuracy results were obtained from the comparison of the average scores on the algorithm Naïve Bayes (Before SMOTE) of 85.73% and the SVM algorithm (Before SMOTE) of 85.11%. then for the comparison of the average score on the algorithm Naïve Bayes (After SMOTE) of 88.9% and on the SVM algorithm (After SMOTE) of 97.82%.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116779041","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

Prediction of the Telkom University's Undergraduates Waiting Period for Getting a Job using the CART Algorithm 基于CART算法的电信大学大学生求职等待期预测

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649290

Rani Gusti Angesti, A. Kurniawati, Hilman Dwi Anggana

{"title":"Prediction of the Telkom University's Undergraduates Waiting Period for Getting a Job using the CART Algorithm","authors":"Rani Gusti Angesti, A. Kurniawati, Hilman Dwi Anggana","doi":"10.1109/ic2ie53219.2021.9649290","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649290","url":null,"abstract":"Competition is getting tougher, universities must prepare graduates who can compete in the world of work. The standard of graduates’ profiles that can be used as an assessment is the waiting period. The ideal target of a waiting period is less than or equal to three months. The competence of graduates who can compete in the world of work becomes an assessment of the quality of a university. Several factors that affect the waiting period are the Grade Point Average (GPA), study period, and students’ organization activity. This research was conducted to create a waiting period prediction model using a decision tree based on the factors that affect it. To analyze the waiting period prediction results, the accuracy of the model and the decision tree model is good or not based on the accuracy, sensitivity, and specificity. The decision tree is one of the data mining techniques that can be used for decision-making. In this research, we will use the CART (Classification and Regression Tree) algorithm. In the data mining classification process, data pre-processing will be carried out first, after that the splitting data (training and testing data) will be carried out. Based on the results of the classification tree, the tree size is 10 and has 10 rules. The accuracy of the classification tree's model is 66.67%, 72.97% of sensitivity, and 61.36% of specificity.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121000605","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

Generating Features of Windows Portable Executable Files for Static Analysis using Portable Executable Reader Module (PEFile) 使用便携式可执行文件阅读器模块(PEFile)生成用于静态分析的Windows便携式可执行文件的特征

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649225

Rico S. Santos, E. Festijo

引用次数: 3