2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)最新文献_第2页

An Implementation of Support Vector Machine Classification for Developer Academy Acceptance Prediction Model 支持向量机分类在开发者学院接受度预测模型中的实现

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590146

Trianggoro Wiradinata, Rinabi Tanamal, Theresia Ratih Dewi Saputri, Y. Soekamto

{"title":"An Implementation of Support Vector Machine Classification for Developer Academy Acceptance Prediction Model","authors":"Trianggoro Wiradinata, Rinabi Tanamal, Theresia Ratih Dewi Saputri, Y. Soekamto","doi":"10.1109/ICITech50181.2021.9590146","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590146","url":null,"abstract":"In order to prepare graduates with work readiness in the IT industry, specifically in mobile apps development, one of its ways is to create a Developer Academy where final year students are prepared in an intensive program for two consecutive semesters to learn the stages of mobile apps development. To ensure the quality of participants in the Developer Academy, a set of selection procedures needs to be prepared, consisting of Aptitude Test, Portfolio Showcase, and Individual Interview. The problem arises when applicants are far more than the class capacity. Hence selection procedures take a longer time. The Developer Academy registration team record showed a ratio of 1: 12, which overburdens the team when it comes to selecting the applicants. More effective procedures are needed with the help of machine learning tools to help with decision making. This study aims to produce a prediction model for developer academy applicants. Several classification algorithms such as k-nearest neighbors, support vector machine, decision tree, and random forest were analyzed. Data was collected from 527 valid applicant's data which submit complete documents based on due date, other applicants who did not submit complete documents were not included in the analysis. Preliminary findings from the study show that the Support Vector Machine algorithm performs best with an accuracy of 86% and this score was then increased by applying oversampling and kernel tricks to get an accuracy rate of 98%. Hence it can be concluded that the prediction model has excellent performance. Keywords-developer academy, artificial intelligence, machine learning, support vector machine, data science, classification","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121574681","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: 2

Encryption with Covertext and Reordering using Permutated Table and Random Function 使用置换表和随机函数的复文本加密和重排序

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590171

E. Ardhianto, Widiyanto Tri Handoko, Hari Murti, Rara Redjeki

引用次数: 0

Cancer Stage Prediction From Gene Expression Data Using Weighted Graph Convolution Network 利用加权图卷积网络从基因表达数据预测癌症分期

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590177

A. Elmahy, S. Aly, F. Elkhwsky

{"title":"Cancer Stage Prediction From Gene Expression Data Using Weighted Graph Convolution Network","authors":"A. Elmahy, S. Aly, F. Elkhwsky","doi":"10.1109/ICITech50181.2021.9590177","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590177","url":null,"abstract":"The early detection of cancer stage is a crucial step for effective treatment. In contrast to traditional approaches, RNA -Seq is the current state of the art technique for gene expression estimation. RNA -Seq data have been used in research and in production as input data for several classification and prediction models in many disease including cancer staging. We present a novel cancer stage prediction approach based on gene expression data. Our approach is based on Weighted Graph Convolution Networks (GCN). GCN is the application of deep learning back-propagation on graph structures. In this work, we used correlation between genes to generate a gene network graph. A neural network model with weighted graph convolution layer was trained to predict the cancer stage for cancer patients. We employed the Kidney Renal Clear Cell Carcinoma dataset (TCGA-KIRC) from the Human Cancer Genome Atlas (TCGA) to predict the cancer stage for each patient. The TCGA-KIRC dataset includes 4 cancer stages, I, II, III, and IV. We generated a binary classification problem where stages I and II are classified as “early cancer stage” and stages III and IV are classified as “late cancer stage”. We compared our approach to the state of the art approaches such as random forest and support vector machine. Our approach achieved an accuracy of 82% which outperformed existing approaches with more than 3% increase.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128212820","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

Enhanced K-Means Clustering Approach for Diagnosis Types of Acne 改进的k均值聚类方法诊断痤疮类型

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590124

C. Hayat

引用次数: 4

Combination of Isolation Forest and LSTM Autoencoder for Anomaly Detection 结合隔离森林和LSTM自编码器进行异常检测

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590143

Celvin Yota Priyanto, Hendry, H. Purnomo

引用次数: 4

Biomedical Image Quality Improvement with Spatial Domains 空间域生物医学图像质量改进

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590175

Erick Fernando, Pandapotan Siagian

{"title":"Biomedical Image Quality Improvement with Spatial Domains","authors":"Erick Fernando, Pandapotan Siagian","doi":"10.1109/ICITech50181.2021.9590175","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590175","url":null,"abstract":"Digital image processing aims to improve the quality of an original image so that it can display an image that is relatively better than the original image, so as to obtain the detailed information needed for an analysis need. Quality degradation Biomedical images in computerization usually experience changes in brightness, blur, and contrast stretching. Because of the deterioration in the image quality, doctors and patients cannot obtain the information needed for analysis, therefore the need for biomedical image processing techniques with spatial domain methods to improve the quality and details of information in the image so that it helps doctors in diagnosing. Data analysis is biomedical images like X-ray images, CT-Scan (Computer Tomographic Scan) images, and USG (Ultrasound Graphic) images. A focused study on improving image quality can be done with digital spatial domain image processing techniques. Digital domain image processing is digital image processing based on the manipulation of pixel values in the image directly, in the form of point processing and mask processing. Process testing for each image for Blur level is CT-scan images with a value of 0.98436, ultrasound images with a value of 0.9875, X-ray images with a value of 0.9836. This value is blur level value get near to 1 which means the image becomes clearer. The analysis results prove that the spatial domain method can clarify the object image in this study. The biomedical image quality processing model is proven to be able to improve the quality of the image that is declining due to the digitization process and assist the user in analyzing the biomedical image.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129627959","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

Measurement of Detection Rate Accuracy in Forecasting Crude Palm Oil Production using Fuzzy Time Series 模糊时间序列预测粗棕榈油产量的检出率准确度测量

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590172

Arif Ridho Lubis, S. Prayudani, Y. Fatmi, Al-Khowarizmi, Julham, Y. Y. Lase

{"title":"Measurement of Detection Rate Accuracy in Forecasting Crude Palm Oil Production using Fuzzy Time Series","authors":"Arif Ridho Lubis, S. Prayudani, Y. Fatmi, Al-Khowarizmi, Julham, Y. Y. Lase","doi":"10.1109/ICITech50181.2021.9590172","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590172","url":null,"abstract":"Time Series is a superior method of predicting the future based on past data. Time series are also used in various businesses to make forecasts for profit. Time series data provide data visualization with statistical explanations necessary for business decisions. One of the businesses that operates for the needs of all elements is the Crude Palm Oil (CPO) commodity industry. Where the CPO price can be forecast using time series because it uses a series at the time available in fact. In this paper, 599 data of CPO price data were crawled from September 10, 2019 to April 30, 2021, then divided into 560 training data and 39 testing data. In this case, testing was carried out in measuring accuracy using MAPE in forecasting CPO prices. with time series getting 0.01781302% while accuracy is also measured by MAPE combined with detection rate gaining a percentage of 0.501031843%. This indicates that when forecasting with time series on CPO price data, the best accuracy is calculated using MAPE without any combination with other techniques.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130667888","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: 2

Alphabetical Author Index 按字母顺序排列的作者索引

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/icitech50181.2021.9590106

引用次数: 0

Comparison of Capacitated Vehicle Routing Problem Using Initial Route and Without Initial Route for Pharmaceuticals Distribution 药品配送有初始路径与无初始路径的有能力车辆路径问题比较

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590116

Jessi Febria, Christine Dewi, Evangs Mailoa

引用次数: 1

Design and Validity Test of The Disaster Mitigation Information System Using EUCS Method 基于EUCS方法的减灾信息系统设计与有效性检验

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590163

K. Hartomo, Arnoldi Dea Tesa Hernanda

{"title":"Design and Validity Test of The Disaster Mitigation Information System Using EUCS Method","authors":"K. Hartomo, Arnoldi Dea Tesa Hernanda","doi":"10.1109/ICITech50181.2021.9590163","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590163","url":null,"abstract":"Natural disasters always cause loss of lives and properties in all regions of Indonesia. So far, coordination and disaster mitigation have not been integrated properly between disaster agencies and stakeholders so that negative impacts cannot be minimized. This research proposes an automated disaster mitigation system to increase the effectiveness of emergency response and aid after a natural disaster strikes, which the validity of the system's performance will be tested using the End-User Computing Satisfaction (EUCS) method. The disaster mitigation information system receives input of disaster event data which disaster information is then distributed them to agencies and stakeholders, and finally stakeholders can provide a response of aid through the system so that the synchronization and integration of the aid is well organized. The result of the validity test using the End-User Computing Satisfaction method shows a test score of 3.4 out of 4 for a total satisfaction score, thus this result indicates that the built disaster mitigation information system is in the category of satisfied and acceptable system by users.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116786946","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