2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)最新文献_第3页

Techno-Economics Analysis Of RAN-Spectrum Sharing Scheme Use Sensitivity Analysis Method 基于灵敏度分析法的无线局域网频谱共享方案技术经济分析

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127697

Wisudantyo Wahyu Priambodo, H. Wijanto, N. Adriansyah

{"title":"Techno-Economics Analysis Of RAN-Spectrum Sharing Scheme Use Sensitivity Analysis Method","authors":"Wisudantyo Wahyu Priambodo, H. Wijanto, N. Adriansyah","doi":"10.1109/ICCoSITE57641.2023.10127697","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127697","url":null,"abstract":"The main problem faced by cellular operators in Indonesia is the cost of infrastructure investment which is very expensiv. Thus, a sharing infrastructure scheme is needed between cellular operators to reduce the Capex and Opex. In this thesis, the author will examine the feasibility of a 5G infrastructure core sharing (CN sharing) scheme for cellular operators in Indonesia using two aspects, namely technology and economy. From the technological aspect, the writer will analyze the capacity and coverage approach. The economic aspect is carried out to test the business feasibility of this RAN-frequency sharing scheme from the cellular operator's point of view. The research will be conducted in 2 types of areas, urban area (Banjarmasin City) and suburban area (Banjarbaru City) for 7 years ahead (2022-2028). Based on capacity and coverage planning, Banjarmasin city need 84 gNodeB and Banjarbaru city need 71 gNodeB. The results of the study show that the implementation of the RAN-spectrum sharing scheme can reduce Capex costs 50-67%. The most feasible scenario to be implemented from economical point of view is sharing 3 operators for economic parameter Net Present Value, Internal Rate of Return and Payback Period. Sensitivity Analysis show that the most sensitive parameter is Opex and the least sensitive parameter is interest rate.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115911872","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

Development of Real-Time Face Recognition for Smart Door Lock Security System using Haar Cascade and OpenCV LBPH Face Recognizer 基于Haar级联和OpenCV LBPH人脸识别器的智能门锁安全系统实时人脸识别开发

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127753

Daniel Anando Wangean, Sinjiru Setyawan, F. I. Maulana, Gusti Pangestu, C. Huda

{"title":"Development of Real-Time Face Recognition for Smart Door Lock Security System using Haar Cascade and OpenCV LBPH Face Recognizer","authors":"Daniel Anando Wangean, Sinjiru Setyawan, F. I. Maulana, Gusti Pangestu, C. Huda","doi":"10.1109/ICCoSITE57641.2023.10127753","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127753","url":null,"abstract":"Face recognition is a technology that is widely used in security systems. In a door security system, facial recognition can be used to open the door simply by recognizing the face of the door owner. This study aims to develop a real-time facial recognition system for smart locking doors using Haar Cascade and OpenCV LBPH Face Recognizer. The purpose of this project is creating security system to limit people who can access a room. The Haar Cascade method is used to detect faces in images, while the OpenCV LBPH Face Recognizer is used to recognize detected faces. This system was developed using the Python programming language and the OpenCV library. The test results show that this system can detect and recognize faces with an accuracy of 62.7% with our dataset and can be improved by adding more datasets and using deep learning algorithms to train the recognizer model. Thus, the developed real-time facial recognition system can be used as a smart locking door security solution with high accuracy.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124174101","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

Price Prediction of Non-Fungible Tokens (NFTs) using Data Mining Prediction Algorithm 基于数据挖掘预测算法的不可替代代币(nft)价格预测

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127679

Indri Tri Julianto, D. Kurniadi, Fakhrun Mahda Khoiriyyah

{"title":"Price Prediction of Non-Fungible Tokens (NFTs) using Data Mining Prediction Algorithm","authors":"Indri Tri Julianto, D. Kurniadi, Fakhrun Mahda Khoiriyyah","doi":"10.1109/ICCoSITE57641.2023.10127679","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127679","url":null,"abstract":"Non-Fungible Tokens (NFTs) experienced a peak of popularity in Indonesia through content created and sold by an account at OpenSea called Ghozali Everyday in early 2022. Ghozali reportedly earned ± Rp. 1.3 billion from the content he has created. This sparked the curiosity of the Indonesian people to imitate what Ghozali Everyday did in the hope of getting similar benefits. The market price of NFTs is the same as stock prices, which will fluctuate depending on the price of the cryptocurrency because these NFTs can generally be purchased with the cryptocurrency, namely Ethereum. This research was conducted to predict the price of NFTs using the Data Mining Prediction Algorithm. Five algorithms are compared to find the best algorithm: Deep Learning, Linear Regression, Neural Networks, Support Vector Machines, and Generalized Linear Model. The methodology used is Knowledge Discovery in Databases. The NFTs price dataset is taken from the page coinmarketcap.com from 16 November 2021 to 16 November 2022. The results show that the best Data Mining Prediction Algorithm is a Neural Network with a value of The lowest Root Mean Square Error (RMSE) compared to other algorithms, namely 83.617 +/- 18.853 (micro average: 85.590 +/- 0.000). After the Neural Network is used in the Dataset, the graph results show no significant difference between the Closing Price and the Predicted Price.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"5 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116789870","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

Dynamic Patient Categorization Based on Medical Records Using Fuzzy C-Means Clustering Technique 基于病历的模糊c均值聚类技术动态患者分类

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127798

Devi Fajar Wati, F. Renaldi, I. Santikarama

{"title":"Dynamic Patient Categorization Based on Medical Records Using Fuzzy C-Means Clustering Technique","authors":"Devi Fajar Wati, F. Renaldi, I. Santikarama","doi":"10.1109/ICCoSITE57641.2023.10127798","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127798","url":null,"abstract":"Health agencies have actively implemented electronic medical records (EMR) on paper-based medical records. Currently, EMR is not very informative for extracting useful information or for tracking the patient disease. Hidden patterns that can be removed through data mining can help practitioners understand discreet relationships, such as inpatient categorization. Categorizing patients in determining groups in hospitals is very helpful for medical personnel in understanding work and actions to provide the right decisions and fast in taking action. However, there are several challenges in categorizing patients using data mining, one of which is selecting methods for the right cluster results according to the data used. Although there have been many studies that discuss the categorization of patients, no one has addressed the categorization of dynamic patients, especially in medical records. We consider this an important issue because, in medical records, there are similarities between one data and another, which causes one patient data to fall into two categories and affects health practitioner decision making. These challenges can be overcome using dynamic patient categorization. After implementation, we do an accuracy test. The test is done using Silhouette, Sum Squared Error, and Duns Fuzziness Coefficients. The result is that the accuracy is close to 85.2%. Identifying the types of diseases that become cluster labels is a good future work to do.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129811311","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

A modification of the Distance Formula on the K-Nearest Neighbor Method is Examined in Order to Categorize Spices from Photo Using the Histogram of Oriented Gradient * 研究了对距离公式在k近邻方法上的改进，以便利用有向梯度直方图*对照片中的香料进行分类

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127780

Melisah Melisah, Muhathir Muhathir

{"title":"A modification of the Distance Formula on the K-Nearest Neighbor Method is Examined in Order to Categorize Spices from Photo Using the Histogram of Oriented Gradient *","authors":"Melisah Melisah, Muhathir Muhathir","doi":"10.1109/ICCoSITE57641.2023.10127780","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127780","url":null,"abstract":"Spices are biological resources that have long played a very important role in everyday life. Spices have characteristics, shapes, and colors that are almost similar and it is difficult to distinguish one spice from another. To assist in recognizing the characteristics of existing spices, the author tries to do research with the title. \"Spices Classification Using the K-Nearest Neighbor (K-NN) Method and Using Histogram Oriented Gradient Feature Extraction. The method used in this study is the K-Nearest Neighbor and uses the Histogram Of Oriented Gradient feature extraction. In this study, the dataset used was 2250 image samples and divided into two categories, namely training data and testing data with a ratio of 80%: 20%. The results of this study indicate that the most optimal testing distance formula, namely the Manhattan distance formula, obtained an average accuracy of 87%, 87% precision, 87% recall, 87% f1 score, 87% Fbeta score, and 77% Jaccard score. These results indicate that feature extraction greatly influences the number of types in extracting information, the Histogram of Oriented Gradient works optimally when the number of types extracted is small and not optimal when used in a large number of classification types.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"17 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127220272","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: 3

Performance Evaluation Brushless DC Motor System With Variable Loads 变负载无刷直流电机系统性能评价

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127818

Febriyani Baharu, Faizal Arya Samman, Y. Yusran

引用次数: 0

Implementation of the K-Means Algorithm for Clustering the Characteristics of Students Receiving Kartu Indonesia Pintar Kuliah (KIP-K) KIP-K学生特征聚类的K-Means算法实现

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127852

Fitri Nuraeni, D. Kurniadi, Gisna Fauzian Dermawan

{"title":"Implementation of the K-Means Algorithm for Clustering the Characteristics of Students Receiving Kartu Indonesia Pintar Kuliah (KIP-K)","authors":"Fitri Nuraeni, D. Kurniadi, Gisna Fauzian Dermawan","doi":"10.1109/ICCoSITE57641.2023.10127852","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127852","url":null,"abstract":"The limited quota of recipients of the Kartu Indonesia Pintar Kuliah (KIP-K) causes the host universities to select applicants to get students who are eligible to receive KIP-K based on academic achievement, non-academic achievements, and family economic conditions. However, after the lectures started, some students who received KIP-K lacked discipline in undergoing lecture procedures and experienced a decrease in their achievement index (IP). Therefore, it is necessary to explore knowledge about the characteristics of KIP-K recipient students by conducting clustering modeling. So, in this study, clustering modeling was carried out on student data receiving KIP-K at a university by applying the Cross-Industry Standard Process for Data Mining (CRIPS-DM) method and the k-means clustering algorithm. This study chooses a clustering model with a value of k=2, which has the smallest Davies Bouldine index (DBI) value of 0.35. This clustering resulted in 2 clusters where student characteristics showed significant differences in the attributes of the distance from home to the campus location and relatively minor fluctuations in IP from the first semester to the fourth semester. From mapping the characteristics of KIP-K recipient students, knowledge can be used as material for higher education decisions in selecting KIP-K registrants to minimize the future academic problems of KIP-K recipient students.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132893003","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

AI-Based Approaches for Handover Optimization in 5G New Radio and 6G Wireless Networks 基于人工智能的5G新无线电和6G无线网络切换优化方法

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127687

Ahmed F. Ashour, M. Fouda

引用次数: 0

Feature Selection using Grey Wolf Optimization Algorithm on Light Gradient Boosting Machine 基于灰狼优化算法的光梯度增强机特征选择

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/iccosite57641.2023.10127801

Felix Indra Kumiadi, Ajeng Wulandari, S. Arifin

引用次数: 0

Analysis of Different Naïve Bayes Methods for Categorizing Spices Through Photo using the Speeded-up Robust Feature 基于加速鲁棒特征的Naïve不同贝叶斯照片香料分类方法分析

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127787

Ira Safira, Muhathir Muhathir

{"title":"Analysis of Different Naïve Bayes Methods for Categorizing Spices Through Photo using the Speeded-up Robust Feature","authors":"Ira Safira, Muhathir Muhathir","doi":"10.1109/ICCoSITE57641.2023.10127787","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127787","url":null,"abstract":"Spices are biological natural resources that have long been used in human life. Spices are highly valued in the European market due to their flavor, aroma, and delicacy. Spices come in a variety of shapes and sizes, each with its own set of characteristics. Because there are so many different types of spices, many people are unfamiliar with their names and forms. As a result, this study discusses how to classify spices using the Nave Bayes method and the Speeded-up Robust Features feature extraction method. According to the results of the tests conducted in this study, experiments with 5 types of spices produced better results with an accuracy of 77.3%, precision of 77.5%, recall of 77.5%, f1 score of 76.4%, f beta score of 76.8%, and Jaccard score of 63.3%, whereas experiments with 10 types of spices and 15 types of spices produced less than the maximum. The findings revealed that the number of spice species used in extracting information is greatly influenced by feature extraction. Speeded-up Robust features that have been accelerated Feature Extraction works best when the number of spices extracted is small, and it performs poorly when used in a large number of classification types.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120976551","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: 3