Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315375
R. Prakoso, Y. Ruldeviyani, K. F. Arisya, A. L. Fadhilah
The rise of misuse of online transportation accounts causes a person to be aware to protect their personal information. A person's awareness is an important factor for safeguarding his personal information assets and reducing crime loopholes. This research was conducted to measure the security awareness of online transportation users. This research was conducted by distributing questionnaires to 215 respondents who use online transportation applications in Indonesia. The framework used to measure the level of information security awareness is the Knowledge-Attitude-Behavior (KAB) model and Human Aspects of Information Security Questionnaire (HAIS-Q). Analytic Hierarchy Process (AHP) will be used as a data processing method to assess information security awareness level of online transportation users. The result shows that information security awareness level of online transportation users is at the level of “good”. But some focus areas of users indicate the score at the level of “average”, which the score is not enough to protect personal assets so need certain improvements in the application and user aspects that can increase the level of information security awareness of online transportation users.
{"title":"Measurement of Information Security Awareness Level: A Case Study of Online Transportation Users","authors":"R. Prakoso, Y. Ruldeviyani, K. F. Arisya, A. L. Fadhilah","doi":"10.1109/ISRITI51436.2020.9315375","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315375","url":null,"abstract":"The rise of misuse of online transportation accounts causes a person to be aware to protect their personal information. A person's awareness is an important factor for safeguarding his personal information assets and reducing crime loopholes. This research was conducted to measure the security awareness of online transportation users. This research was conducted by distributing questionnaires to 215 respondents who use online transportation applications in Indonesia. The framework used to measure the level of information security awareness is the Knowledge-Attitude-Behavior (KAB) model and Human Aspects of Information Security Questionnaire (HAIS-Q). Analytic Hierarchy Process (AHP) will be used as a data processing method to assess information security awareness level of online transportation users. The result shows that information security awareness level of online transportation users is at the level of “good”. But some focus areas of users indicate the score at the level of “average”, which the score is not enough to protect personal assets so need certain improvements in the application and user aspects that can increase the level of information security awareness of online transportation users.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115721914","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315370
Nur Muhamad Ziko Iskandar, E. Setijadi, A. Affandi
In this paper, a new combination method of Defected Ground Structure (DGS) and Line Resonator to reduce mutual coupling in linear array $2times 1$ antenna in H-plane structure is proposed. Both DGS and Line Resonator are designed to block surface waves of two antennas that work on a frequency of 2.6 GHz with an edge-to-edge distance element spacing is $0.049 lambdamathrm{o}$. The antenna performances before and after design implementation have been investigated. The simulation results show the coupling isolation between the antennas has been improved by −11.84 dB, the gain of the antenna increase by 0.191 dBi, and drop the side lobe level by 2.5 dB. The purposed design has the advantage of wide bandwidth, compactness and easy to fabricate, so it could be used for massive multiple-input multiple-output (M-MIMO) system for 5G communication in S-Band frequency.
{"title":"A Combination of Defected Ground Structure and Line Resonator for Mutual Coupling Reduction","authors":"Nur Muhamad Ziko Iskandar, E. Setijadi, A. Affandi","doi":"10.1109/ISRITI51436.2020.9315370","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315370","url":null,"abstract":"In this paper, a new combination method of Defected Ground Structure (DGS) and Line Resonator to reduce mutual coupling in linear array $2times 1$ antenna in H-plane structure is proposed. Both DGS and Line Resonator are designed to block surface waves of two antennas that work on a frequency of 2.6 GHz with an edge-to-edge distance element spacing is $0.049 lambdamathrm{o}$. The antenna performances before and after design implementation have been investigated. The simulation results show the coupling isolation between the antennas has been improved by −11.84 dB, the gain of the antenna increase by 0.191 dBi, and drop the side lobe level by 2.5 dB. The purposed design has the advantage of wide bandwidth, compactness and easy to fabricate, so it could be used for massive multiple-input multiple-output (M-MIMO) system for 5G communication in S-Band frequency.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123078857","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315444
A. Sasmito, Y. Ruldeviyani
Indonesian civil servants already have social security; however, the benefits' value has not sufficed life necessities in retirement. Indonesian social insurance company provides additional insurance products for civil servants, yet only 7 percent of civil servants are interested. Improved marketing by identifying civil servants through data mining will help boost product sales. Data mining uses the CRISP-DM approach, starting from understanding business processes, civil servant data, data preparation, and modeling to evaluation. Data mining techniques use classification with three algorithms: Decision Tree, Naive Bayes, and Neural Network. Data mining results show six influential attributes of civil servants, including sex, the number of children, age, remaining working period, marital status, and years of service. The neural network algorithm has better performance with an accuracy value of 71.7%, a F1-score value of 73.4%, a precision value of 69.7%, a recall value of 77.6%, and an AUC value of 79.1%.
{"title":"Comparison of The Classification Data Mining Methods to Identify Civil Servants in Indonesian Social Insurance Company","authors":"A. Sasmito, Y. Ruldeviyani","doi":"10.1109/ISRITI51436.2020.9315444","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315444","url":null,"abstract":"Indonesian civil servants already have social security; however, the benefits' value has not sufficed life necessities in retirement. Indonesian social insurance company provides additional insurance products for civil servants, yet only 7 percent of civil servants are interested. Improved marketing by identifying civil servants through data mining will help boost product sales. Data mining uses the CRISP-DM approach, starting from understanding business processes, civil servant data, data preparation, and modeling to evaluation. Data mining techniques use classification with three algorithms: Decision Tree, Naive Bayes, and Neural Network. Data mining results show six influential attributes of civil servants, including sex, the number of children, age, remaining working period, marital status, and years of service. The neural network algorithm has better performance with an accuracy value of 71.7%, a F1-score value of 73.4%, a precision value of 69.7%, a recall value of 77.6%, and an AUC value of 79.1%.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121093337","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315371
Denni Huda Pratama, S. Suyanto
Swarm intelligence (SI) is widely applied for optimizing both continuous and discrete problems. Many papers have investigated them for continuous optimizations since most swarm-based algorithms are designed based on continuous movements, which are simply calculated using vector-based mathematical operations. It is quite easy to select the best SI algorithm for a given continuous problem. However, it is quite hard to pick an optimum SI algorithm for a discrete problem since the individual movement is difficult to develop. Therefore, in this paper, three SI algorithms: particle swarm optimization (PSO), firefly algorithm (FA), and bat algorithm (BA), are compared to solve some cases of traveling salesman problem (TSP). Evaluation on four TSP cases show that FA is the most effective and efficient since it dynamically evolves some individuals' groups and balances the exploitative-explorative movements.
{"title":"Comparison of PSO, FA, and BA for Discrete Optimization Problems","authors":"Denni Huda Pratama, S. Suyanto","doi":"10.1109/ISRITI51436.2020.9315371","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315371","url":null,"abstract":"Swarm intelligence (SI) is widely applied for optimizing both continuous and discrete problems. Many papers have investigated them for continuous optimizations since most swarm-based algorithms are designed based on continuous movements, which are simply calculated using vector-based mathematical operations. It is quite easy to select the best SI algorithm for a given continuous problem. However, it is quite hard to pick an optimum SI algorithm for a discrete problem since the individual movement is difficult to develop. Therefore, in this paper, three SI algorithms: particle swarm optimization (PSO), firefly algorithm (FA), and bat algorithm (BA), are compared to solve some cases of traveling salesman problem (TSP). Evaluation on four TSP cases show that FA is the most effective and efficient since it dynamically evolves some individuals' groups and balances the exploitative-explorative movements.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114446320","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315394
Dian Rusdiyanto
This paper proposed two different array of antenna designs for coastal surveillance radar application. The material antenna used Epoxy FR-4 with 4.6 of dielectric constant and simulated by CST Microwave Studio. The basic antenna is designed using a single rectangular shape at operating frequency 3 GHz, and then it continues to add 8-element of the array antenna. The 8-element array antenna consists of a one-dimensional feed network and a two-dimensional feed network. One-dimensional feed network is structured by a 1×8-element array antenna, while two-dimensional is a 2×4-element array. The simulation result showed that one-dimensional design achieved a better results in reflection factor, gain, and far-field radiation pattern parameters. On the other hand, two-dimensional has larger bandwidth that is around 235.3 MHz. In conclusion, both structures have good agreement with radar antenna specifications.
{"title":"Comparison Of Eight Elements Array Structure Design For Coastal Surveillance Radar","authors":"Dian Rusdiyanto","doi":"10.1109/ISRITI51436.2020.9315394","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315394","url":null,"abstract":"This paper proposed two different array of antenna designs for coastal surveillance radar application. The material antenna used Epoxy FR-4 with 4.6 of dielectric constant and simulated by CST Microwave Studio. The basic antenna is designed using a single rectangular shape at operating frequency 3 GHz, and then it continues to add 8-element of the array antenna. The 8-element array antenna consists of a one-dimensional feed network and a two-dimensional feed network. One-dimensional feed network is structured by a 1×8-element array antenna, while two-dimensional is a 2×4-element array. The simulation result showed that one-dimensional design achieved a better results in reflection factor, gain, and far-field radiation pattern parameters. On the other hand, two-dimensional has larger bandwidth that is around 235.3 MHz. In conclusion, both structures have good agreement with radar antenna specifications.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129629486","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315380
Rangga Dwi Alamsyah, S. Suyanto
Gender classification based on voice is crucial for speech recognition, which can be applied to various applications. It is generally developed using conventional machine learning and deep learning approaches. In this research, a gender classification model based on speech is developed using Bidirectional Long Short-Term Memory (BLSTM). The Mel Frequency Cepstral Coefficient (MFCC) is exploited to extract the features to train the BLSTM. Evaluation using a low dataset of 1,000 utterances, 500 males and 500 females, for five runs shows that the model is accurately capable of classifying the gender of the speakers. With a train-test split portion of 80:20, the model obtains an average accuracy of 86.7%, where the highest and the lowest accuracy are 90.5% and 81.0%, respectively. Reducing the portion decreases its performance. It is still stable for the 50:50 train-test split.
基于语音的性别分类是语音识别的关键，可以应用于各种应用。它通常使用传统的机器学习和深度学习方法开发。本研究利用双向长短期记忆(Bidirectional Long - short - Memory, BLSTM)建立了基于语音的性别分类模型。利用Mel频率倒谱系数(MFCC)提取特征来训练BLSTM。使用1000个话语的低数据集(500个男性和500个女性)进行5次运行的评估表明，该模型能够准确地分类说话者的性别。在训练测试分割比例为80:20的情况下，模型平均准确率为86.7%，其中最高准确率为90.5%，最低准确率为81.0%。减少部分会降低其性能。在50:50的火车测试中，它仍然是稳定的。
{"title":"Speech Gender Classification Using Bidirectional Long Short Term Memory","authors":"Rangga Dwi Alamsyah, S. Suyanto","doi":"10.1109/ISRITI51436.2020.9315380","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315380","url":null,"abstract":"Gender classification based on voice is crucial for speech recognition, which can be applied to various applications. It is generally developed using conventional machine learning and deep learning approaches. In this research, a gender classification model based on speech is developed using Bidirectional Long Short-Term Memory (BLSTM). The Mel Frequency Cepstral Coefficient (MFCC) is exploited to extract the features to train the BLSTM. Evaluation using a low dataset of 1,000 utterances, 500 males and 500 females, for five runs shows that the model is accurately capable of classifying the gender of the speakers. With a train-test split portion of 80:20, the model obtains an average accuracy of 86.7%, where the highest and the lowest accuracy are 90.5% and 81.0%, respectively. Reducing the portion decreases its performance. It is still stable for the 50:50 train-test split.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129745540","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315515
Syifa Khairunnisa, S. Suyanto, Prasti Eko Yunanto
Various methods of machine learning have been implemented in the medical field to classify various diseases, such as diabetes. The k-nearest neighbors (KNN) is one of the most known approaches for predicting diabetes. Many researchers have found by combining KNN with one or more other algorithms may provide a better result. In this paper, a combination of three procedures, removing noise, reducing the dimension, and weighting distance, is proposed to improve a standard voting-based KNN to classify Pima Indians Diabetes Dataset (PIDD) into two classes. First, the noises in the training set are removed using k-means clustering (KMC) to make the voter data in both classes more competent. Second, its dimensional is then reduced to decrease the intra-class data distances but increase the inter-class ones. Two methods of dimensional reduction: principal component analysis (PCA) and autoencoder (AE), are applied to investigate the linearity of the dataset. Since there is an imbalance on the dataset, a proportional weight is incorporated into the distance formula to get the fairness of the voting. A 5-fold cross validation-based evaluation shows that each proposed procedure works very well in enhancing the KNN. KMC is capable of increasing the accuracy of KNN from 81.6% to 86.7%. Combining KMC and PCA improves the KNN accuracy to be 90.9%. Next, a combination of KMC and AE enhances the KNN to gives an accuracy of 97.8%. Combining three proposed procedures of KMC, PCA, and Weighted KNN (WKNN) increases the accuracy to be 94.5%. Finally, the combination of KMC, AE, and WKNN reaches the highest accuracy of 98.3%. The facts that AE produces higher accuracies than PCA inform that the features in the dataset have a high non-linearity.
{"title":"Removing Noise, Reducing dimension, and Weighting Distance to Enhance $k$-Nearest Neighbors for Diabetes Classification","authors":"Syifa Khairunnisa, S. Suyanto, Prasti Eko Yunanto","doi":"10.1109/ISRITI51436.2020.9315515","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315515","url":null,"abstract":"Various methods of machine learning have been implemented in the medical field to classify various diseases, such as diabetes. The k-nearest neighbors (KNN) is one of the most known approaches for predicting diabetes. Many researchers have found by combining KNN with one or more other algorithms may provide a better result. In this paper, a combination of three procedures, removing noise, reducing the dimension, and weighting distance, is proposed to improve a standard voting-based KNN to classify Pima Indians Diabetes Dataset (PIDD) into two classes. First, the noises in the training set are removed using k-means clustering (KMC) to make the voter data in both classes more competent. Second, its dimensional is then reduced to decrease the intra-class data distances but increase the inter-class ones. Two methods of dimensional reduction: principal component analysis (PCA) and autoencoder (AE), are applied to investigate the linearity of the dataset. Since there is an imbalance on the dataset, a proportional weight is incorporated into the distance formula to get the fairness of the voting. A 5-fold cross validation-based evaluation shows that each proposed procedure works very well in enhancing the KNN. KMC is capable of increasing the accuracy of KNN from 81.6% to 86.7%. Combining KMC and PCA improves the KNN accuracy to be 90.9%. Next, a combination of KMC and AE enhances the KNN to gives an accuracy of 97.8%. Combining three proposed procedures of KMC, PCA, and Weighted KNN (WKNN) increases the accuracy to be 94.5%. Finally, the combination of KMC, AE, and WKNN reaches the highest accuracy of 98.3%. The facts that AE produces higher accuracies than PCA inform that the features in the dataset have a high non-linearity.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129858232","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315517
Gregorius Dwi Perkasa, Niki Min Hidayati Robbi, I. Mustika, Widyawan
Cognitive Radio Network (CNR) is a dynamic network where the users can adjust spectrum usage dynamically in accordance to the operational environment to minimize interference. However, it still has a major problem regarding the channel allocation used by the nodes. This problem exists because channel allocations are completely randomly generated so that they might cause interference to users on the same channel. To handle resource allocation problems in the CRN, the authors proposed a solution using the Grey Wolf Optimizer (GWO). This optimizer algorithm is an optimization included in the metaheuristic algorithm with the source of inspiration from the behavior of the gray wolf colony in hunting prey. In this job, Alpha serves as a prime candidate in finding the best channel. The ultimate goal of using this GWO optimization is to get the most optimal channel allocation scheme for each node in the cognitive radio network so that it has minimal interference and maximum network throughput. The authors have modified the fitness function and coding scheme of GWO to get the best share of resources from the CRN. From the simulations tested, the results showed that channel allocation using the GWO algorithm was able to increase throughput and reduce network interference.
{"title":"Interference Mitigation in Cognitive Radio Network Based on Grey Wolf Optimizer Algorithm","authors":"Gregorius Dwi Perkasa, Niki Min Hidayati Robbi, I. Mustika, Widyawan","doi":"10.1109/ISRITI51436.2020.9315517","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315517","url":null,"abstract":"Cognitive Radio Network (CNR) is a dynamic network where the users can adjust spectrum usage dynamically in accordance to the operational environment to minimize interference. However, it still has a major problem regarding the channel allocation used by the nodes. This problem exists because channel allocations are completely randomly generated so that they might cause interference to users on the same channel. To handle resource allocation problems in the CRN, the authors proposed a solution using the Grey Wolf Optimizer (GWO). This optimizer algorithm is an optimization included in the metaheuristic algorithm with the source of inspiration from the behavior of the gray wolf colony in hunting prey. In this job, Alpha serves as a prime candidate in finding the best channel. The ultimate goal of using this GWO optimization is to get the most optimal channel allocation scheme for each node in the cognitive radio network so that it has minimal interference and maximum network throughput. The authors have modified the fitness function and coding scheme of GWO to get the best share of resources from the CRN. From the simulations tested, the results showed that channel allocation using the GWO algorithm was able to increase throughput and reduce network interference.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128245245","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315330
Suwadi, Wirawan, Mike Yuliana
The key extraction scheme using the randomness of the received signal strength is the general technique to secure messages in a wireless environment. However, the imperfect received signal strength reciprocity needs to be overcome to reduce the bit mismatch so it will increase the probability of getting the same key. In this paper, we propose a key extraction scheme between several users, namely a multi-user key extraction scheme that adds a polynomial regression method. This scheme is proven to reduce imperfect signal reciprocity due to noise interference and the limited ability of wireless devices. In addition, we also use the multi-bit extraction method to enhance the speed of key extraction. The results of the tests showed that our proposed multi-user key extraction scheme proved to be able to improve the performance of the key extraction scheme by reducing the bit mismatch up to 30% and improving the key extraction speed up to 35% compared to the existing key extraction scheme
{"title":"Performance Enhancement of Multi-User Key Extraction Scheme (MKES) Based on Imperfect Signal Reciprocity","authors":"Suwadi, Wirawan, Mike Yuliana","doi":"10.1109/ISRITI51436.2020.9315330","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315330","url":null,"abstract":"The key extraction scheme using the randomness of the received signal strength is the general technique to secure messages in a wireless environment. However, the imperfect received signal strength reciprocity needs to be overcome to reduce the bit mismatch so it will increase the probability of getting the same key. In this paper, we propose a key extraction scheme between several users, namely a multi-user key extraction scheme that adds a polynomial regression method. This scheme is proven to reduce imperfect signal reciprocity due to noise interference and the limited ability of wireless devices. In addition, we also use the multi-bit extraction method to enhance the speed of key extraction. The results of the tests showed that our proposed multi-user key extraction scheme proved to be able to improve the performance of the key extraction scheme by reducing the bit mismatch up to 30% and improving the key extraction speed up to 35% compared to the existing key extraction scheme","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128610447","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}
Pub Date : 2020-12-10DOI: 10.1109/isriti51436.2020.9315455
Desi Rianti, A. Hikmaturokhman, Dina Rachmawaty
The Pulogadung industrial area is a widely known developing industrial area that is perfectly ideal for the implementation of 5G technology to help run the Indonesian economy. Nonetheless, it is noteworthy that knowing the level of economic feasibility of an operator is highly crucial before making an investment in a network performance. This study focuses on an analysis of 5G network design in terms of coverage using the Urban Micro propagation model in the Uplink (UL) and Downlink (DL) Outdoor to Outdoor (O2O) Line of Sight (LOS) scenarios. In addition, it also aims to cover the discussion on the economic level of project feasibility using an optimistic scenario assuming 80% users, which is based on the projected increase in population growth of 5G users using the bass growth model method since its implementation in 2021–2030. The economic analysis used the parameters of Capital Expenditure (CAPEX), Operational Expenditure (OPEX), Net Present Value (NPV), Internal Rate of Return (IRR) to determine the feasibility of planning a 5G New Radio network in the Pulogadung Industrial Area. The calculation of Cost Benefit in the optimistic techno-economic scenario shows that each UL O2O LOS NPV scenario resulted in Rp. 28.369.498.095.53 with an IRR of 31.18%, while DL O2O LOS NPV resulted in Rp. 24.862.173.071.28 with an IRR of 26.68%. This result indicates that the performance of the 5G NR network in the Pulogadung Industrial Estate assuming the projections for the next 10 years is feasible.
普洛加东工业区是一个广为人知的发展中工业区，非常适合实施5G技术，帮助印尼经济运行。尽管如此，值得注意的是，在对网络性能进行投资之前，了解运营商的经济可行性水平至关重要。本研究着重分析了5G网络设计在Uplink (UL)和Downlink (DL) Outdoor to Outdoor (O2O) Line of Sight (LOS)场景下使用城市微传播模型的覆盖范围。此外，它还旨在涵盖项目可行性的经济层面的讨论，使用假设80%用户的乐观情景，这是基于使用低音增长模型方法自2021-2030年实施以来对5G用户人口增长的预测。经济分析使用资本支出(CAPEX)、运营支出(OPEX)、净现值(NPV)、内部收益率(IRR)等参数来确定在普罗洞工业区规划5G新无线网络的可行性。在技术经济乐观情景下的成本效益计算表明，UL O2O各情景的LOS NPV值为Rp. 28.369.498.095.53, IRR为31.18%;DL O2O各情景的LOS NPV值为Rp. 24.862.173.071.28, IRR为26.68%。这一结果表明，假设未来10年的预测，普罗洞工业园区5G NR网络的性能是可行的。
{"title":"Techno-Economic 5G New Radio Planning Using 26 GHz Frequency at Pulogadung Industrial Area","authors":"Desi Rianti, A. Hikmaturokhman, Dina Rachmawaty","doi":"10.1109/isriti51436.2020.9315455","DOIUrl":"https://doi.org/10.1109/isriti51436.2020.9315455","url":null,"abstract":"The Pulogadung industrial area is a widely known developing industrial area that is perfectly ideal for the implementation of 5G technology to help run the Indonesian economy. Nonetheless, it is noteworthy that knowing the level of economic feasibility of an operator is highly crucial before making an investment in a network performance. This study focuses on an analysis of 5G network design in terms of coverage using the Urban Micro propagation model in the Uplink (UL) and Downlink (DL) Outdoor to Outdoor (O2O) Line of Sight (LOS) scenarios. In addition, it also aims to cover the discussion on the economic level of project feasibility using an optimistic scenario assuming 80% users, which is based on the projected increase in population growth of 5G users using the bass growth model method since its implementation in 2021–2030. The economic analysis used the parameters of Capital Expenditure (CAPEX), Operational Expenditure (OPEX), Net Present Value (NPV), Internal Rate of Return (IRR) to determine the feasibility of planning a 5G New Radio network in the Pulogadung Industrial Area. The calculation of Cost Benefit in the optimistic techno-economic scenario shows that each UL O2O LOS NPV scenario resulted in Rp. 28.369.498.095.53 with an IRR of 31.18%, while DL O2O LOS NPV resulted in Rp. 24.862.173.071.28 with an IRR of 26.68%. This result indicates that the performance of the 5G NR network in the Pulogadung Industrial Estate assuming the projections for the next 10 years is feasible.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129888496","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}
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