2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)最新文献_第3页

Comparison of Renewable Energy Output Power Transmission to Loads Via HVAC and HVDC HVAC和HVDC两种方式下可再生能源输出电力向负荷传输的比较

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702761

L. Gumilar, Mokhammad Sholeh, D. E. Cahyani, A. Kusumawardana, M. A. Habibi, Syamsul Falah Akhmadi

{"title":"Comparison of Renewable Energy Output Power Transmission to Loads Via HVAC and HVDC","authors":"L. Gumilar, Mokhammad Sholeh, D. E. Cahyani, A. Kusumawardana, M. A. Habibi, Syamsul Falah Akhmadi","doi":"10.1109/ISRITI54043.2021.9702761","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702761","url":null,"abstract":"Transmission of electrical power from the generator to the load is a very important part of the electric power system. In the transmission system, it is necessary to increase the voltage to minimize power losses. The transmission system commonly used is High Voltage Alternating Current (HVAC). However, in reality the HVAC system can cause large losses. There are other transmission systems besides HVAC, namely High Voltage Direct Current (HVDC). The purpose of this paper is to compare the performance of HVAC and HVDC transmission systems in distributing electricity to variations in transmission distance or conductor line length and transmission voltage variations. The performance of the HVDC and HVAC systems measured is the power losses in the line conductors. In addition, power losses in transformer 1, transformer 2, and active power in HVAC. In addition, power losses at station 1, station 2, and active power at HVDC. The type of power plant used in this paper is renewable energy. The renewable energy consists of photovoltaic and wind power plants. The results for very long transmission distances, HVDC has smaller power losses than HVAC. In addition, HVDC stations have higher power losses than HVAC transformers. Based on the total power losses, overall the total power losses in HVDC is smaller than the total power losses in HVAC.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129742262","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

Performance Evaluation of Visible Light Communication System Deployment using Multipower Multiple LED Scenario 多功率多LED场景下可见光通信系统部署的性能评估

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702878

Berlian Nurfadhilah, M. I. Nashiruddin, Brian Pamukti, M. Nugraha

引用次数: 0

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702826

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

{"title":"Similarity Normalized Euclidean Distance on KNN Method to Classify Image of Skin Cancer","authors":"Arif Ridho Lubis, S. Prayudani, Al-Khowarizmi, Y. Y. Lase, Y. Fatmi","doi":"10.1109/ISRITI54043.2021.9702826","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702826","url":null,"abstract":"Research on skin cancer patients in terms of optimal classification measurements is still very lacking, so it is necessary to do research that aims to get optimal values in distance measurements with normalized Euclidean distance on the KNN method to classify images of skin cancer patients. The method which is used to classify various types of data such as numbers, images, text is the K-Nearest Neighbor (KNN) method. Basically KNN, however, accepts numeric data so that data other than numeric extract them into numeric. As in this paper, the classifying images of Skin Cancer sufferers consisting of malignant and benign images is performed by extracting data with a Gray-Level Co-occurrence matrix (GLCM) to obtain numerical data from skin cancer images. The GLCM process in this paper makes the matrix be divided into contrast, dissimilarity, homogeneity, energy, correlation and ASM. Then the process is classified where the process with KNN performs the same which usually uses the Euclidean distance compared to the normalized Euclidean distance. The classification process also produces validation applying the accuracy technique calculated by MAPE. The results in this paper testing with Euclidean distance achieved MAPE of 0.71043036% by testing with Normalized Euclidean distance achieving MAPE of 0.3151053%. This showed the similarity in image classification using KNN is more optimal by using the normalized Euclidean distance approach.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130907739","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

Stock Price Prediction During the Pandemic Period with the SVM, BPNN, and LSTM Algorithm 基于SVM、BPNN和LSTM算法的大流行期间股票价格预测

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702865

Icha Mailinda, Y. Ruldeviyani, Fadly Tanjung, Rifqy Mikoriza T, Reihan Putra, Tinna Fauziah A

{"title":"Stock Price Prediction During the Pandemic Period with the SVM, BPNN, and LSTM Algorithm","authors":"Icha Mailinda, Y. Ruldeviyani, Fadly Tanjung, Rifqy Mikoriza T, Reihan Putra, Tinna Fauziah A","doi":"10.1109/ISRITI54043.2021.9702865","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702865","url":null,"abstract":"The stock market volatility during the pandemic was a challenge that affected investors' decisions in making their investments. Machine learning was one of the options to cope with the issue, for it helped develop a predicted algorithm that analyzes time series data as part of the investor's investment consideration. Thus, the algorithm in machine learning can be the answer to the issue. The three comparable algorithms included SVM, BPNN, and LSTM within the BBRI stock report case study from November 14, 2019, to November 13, 2020. The study compared those three algorithms to figure out which is the best one. This research emphasizes CRISP-DM methodology, business understanding, data comprehension, data preparation, algorithm development, evaluation, and deployment. This research concluded that SVM has the best prediction accuracy with 0,003 MSE and 0,058 RMSE, followed by LSTM with 0,008 MSE and 0,087 RMSE, and lastly BPNN with 0,017 MSE and 0,132 RMSE. Reviewing this trend, SVM had the closest forecast to the exact result. BPPN had the highest RMSE, nevertheless, it showed a closer forecast to the exact result, compared to LSTM. This research benefits investors in delivering more accurate predictions to execute accurate decisions regarding stock forecast and investment.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129842982","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

Quasi Linear Utility Function Based-Wireless Internet Incentive-Pricing Models 基于准线性效用函数的无线互联网激励定价模型

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702851

F. Puspita, Depianna Br. Haloho, S. Yahdin, E. Yuliza, Lenni Nurhayati, Y. Hartono

{"title":"Quasi Linear Utility Function Based-Wireless Internet Incentive-Pricing Models","authors":"F. Puspita, Depianna Br. Haloho, S. Yahdin, E. Yuliza, Lenni Nurhayati, Y. Hartono","doi":"10.1109/ISRITI54043.2021.9702851","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702851","url":null,"abstract":"In the internet pricing scheme, internet incentive pricing is used to optimize the price of online services. The desire to pay is one of the prerequisites for customers to subscribe to the service in this study, hence diverse consumers are used. With the growing demand for internet services, service providers are competing to deliver the finest service possible in order to improve service quality and attract customers. Then, using a combination of bundling, a quasi linear utility function, high and low demand users, reverse charging, and a two-part tariff pricing system, this study aims to find the best model and solution for an enhanced internet incentive-pricing model. The computation used to test the model using real data reveals that in this scenario $beta$ as a parameter and $gamma$ as a parameter with two-part tariff-pricing scheme when $FG_{xy}$ increases and $a$ increases, as numerical example from local data shows an incentive value of IDR 797.55 / kbps. This result shows that an enhanced incentive-pricing strategy has benefited ISPs.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128843088","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

Recommendation System for Elective Courses using Content-based Filtering and Weighted Cosine Similarity 基于内容过滤和加权余弦相似度的选修课推荐系统

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702788

Yusfi Adilaksa, Aina Musdholifah

{"title":"Recommendation System for Elective Courses using Content-based Filtering and Weighted Cosine Similarity","authors":"Yusfi Adilaksa, Aina Musdholifah","doi":"10.1109/ISRITI54043.2021.9702788","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702788","url":null,"abstract":"Each study program requires students to take several elective courses. The appropriateness of the elective courses taken with the student's abilities can be one of the factors for the success of student studies. This research focuses on building a content-based filtering recommendation system that provides several elective courses recommendation according to the student's academic history. The proposed recommendation systems' results are based on preprocessed word items from courses taken by the user. The weighted cosine similarity between the elective courses syllabus and the user profiles is calculated. Moreover, the experiment employed a dataset of the CSUGM course syllabus. The proposed recommendation system is evaluated in two ways, i.e., questionnaire method and validation method. The questionnaire method obtains an assessment of system performance, hence the validation method to get the average accuracy. The questionnaire was conducted by involving thirty students of the CSUGM undergraduate program. The experimental results show that the proposed recommendation system has a good performance proven by the percentage of recommendation diversity 81.67%. Furthermore, the accuracy of the proposed recommendation system has an average of 64%.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122488262","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: 6

Modeling of Multiple Cantilevers System for Broadband Vibration Energy Harvester 宽带振动能量采集器多悬臂梁系统建模

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702763

L. Thong, Swee Leong Kok, R. Ramlan

{"title":"Modeling of Multiple Cantilevers System for Broadband Vibration Energy Harvester","authors":"L. Thong, Swee Leong Kok, R. Ramlan","doi":"10.1109/ISRITI54043.2021.9702763","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702763","url":null,"abstract":"Piezoelectric energy harvester has the capability in powering small scale semiconductor devices particularly in the low power sensors applications in Internet of Things (IoT) environment. It is known that the bandwidth and power of these energy harvesters can be improved by increasing the number of cantilevers in the system. This research presents the electromechanical model of multimode piezoelectric energy harvesters with different polarity connections between the cantilever beams to improve the broadband performance of frequency response in the system. The theoretical model and experimental results of the proposed multi-mode system exhibited a significant escalation of output voltage at the gap between two resonance frequencies when the polarity configuration in the cantilever connection is reversed accordingly. The outcomes designate that by interchanging the polarity of the electrical connection between the cantilever beams, the output voltage between the resonance frequency of the multi-mode system can be increase significantly in comparison with the conventional series interconnection.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123795327","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 Color and Texture Features for Samarinda Sarong Classification Samarinda Sarong分类的颜色和纹理特征分析

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702797

Anindita Septiarini, Rizqi Saputra, Andi Tejawati, M. Wati, H. Hamdani, N. Puspitasari

{"title":"Analysis of Color and Texture Features for Samarinda Sarong Classification","authors":"Anindita Septiarini, Rizqi Saputra, Andi Tejawati, M. Wati, H. Hamdani, N. Puspitasari","doi":"10.1109/ISRITI54043.2021.9702797","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702797","url":null,"abstract":"Samarinda sarong or Tajong Samarinda is a traditional woven fabric originating from Samarinda, East Borneo, Indonesia. It is made through a weaving process using a loom called a Gedokan (a traditional machine). Unfortunately, many Samarinda people still lack knowledge regarding the type of Samarinda sarong; hence they cannot recognize it. Therefore, an automatic method of image processing-based needed to recognizing and classifying the motif of Samarinda sarong. This method requires appropriate and discriminatory features to obtain the optimal classification results. This work aims to analyze color and texture features to produce discriminative features. The color features used are color moments applied on RGB and HSV color spaces, while texture features were extracted using Gray Level Co-occurrence Matrix (GLCM). Subsequently, those features were reduced using correlation-based feature selection (CFS) followed by applying the Support Vector Machine (SVM) classifier. The dataset used consists of 150 sarong images (50 Belang Hata, 50 Belang Negara, and 50 Kuningsau). The method performance successfully achieved the accuracy of 100% using only 10 color features from a total of 34 features.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133615222","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

Sensor Placement Strategy to Detect Corrosion in Water Distribution Networks 检测配水管网腐蚀的传感器布置策略

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702855

A. M. Shiddiqi, Deddy Aditya Pramana, R. Ijtihadie, T. Ahmad, H. Studiawan, B. J. Santoso, B. Pratomo

{"title":"Sensor Placement Strategy to Detect Corrosion in Water Distribution Networks","authors":"A. M. Shiddiqi, Deddy Aditya Pramana, R. Ijtihadie, T. Ahmad, H. Studiawan, B. J. Santoso, B. Pratomo","doi":"10.1109/ISRITI54043.2021.9702855","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702855","url":null,"abstract":"Water is distributed from sources to meet demands through pipe networks. Pipes are commonly made of metal and buried underground. These pipes can be corroded over time due to the environmental factors. A high level of corrosion on pipes indicates that the pipes in a network should immediately be replaced because they are very susceptible to cause leaks. One way to detect the presence of corrosion in pipes is to observe flows in pipes by installing flow sensors in all pipes. This method enables us to accurately capture corrosion signature at every possible location in a pipeline. However, it is very inefficient to do so due to provision and maintenance costs. We developed a sensor placement strategy to find locations of flow sensors to maximize the sensors functionality to detect pipe corrosion. We used a directed acyclic graph (DAG) to model flow changes due to the presence of corrosion. We apply the procedure to produce DAGs for locations that are susceptible to corrosion. Sensors are placed at locations with the highest detection sensitivity indicated by the intersection of the DAGs. Experimental results indicate that our method can accurately model the corrosion signature and locate strategic sensor locations.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130332930","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

Intelligent Diabetic Retinopathy Detection using Deep Learning 基于深度学习的糖尿病视网膜病变智能检测

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702859

H. A. Nugroho, Eka Legya Frannita

{"title":"Intelligent Diabetic Retinopathy Detection using Deep Learning","authors":"H. A. Nugroho, Eka Legya Frannita","doi":"10.1109/ISRITI54043.2021.9702859","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702859","url":null,"abstract":"Diabetic retinopathy (DR) is the most common illness related to diabetes caused by the increasing of glucose in human blood and has been dramatically increased in the last decade. Practically, DR is examined by conducting manual analysis on retina images resulted from fundus camera modality in which can lead to some problems such as time-consuming, need more thoroughness and properly skill and experience. Due to the insufficient number of ophthalmologists, especially in rural areas, an alternative solution in supporting diagnosis properly is needed. Regarding to those issues, some research communities have proposed intelligent system for detecting DR. Despite some previous intelligent DR detection have been developed, there still remained problem that quality of image was extremely affect the performance. Hence, in this study we proposed an intelligent DR detection completed with image enhancement process for maintaining the model performance. Our proposed solution was performed in 200 retina images consisting of two classes (normal and abnormal or DR). Our proposed solution successfully increased the performance with the highest accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 0.92, 0.95, 0.81, 0.95, 0.81, respectively. This result has increased by around of 40% in most of evaluation metrics of the model's performance without an image enhancement process. It indicates that conducting image enhancement process before training the model was important to increase the model performance and to prevent the miss-detection.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131187424","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