Pub Date : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288551
F. Humani, Hilman Wisnu, Adyan Pamungkas Ganefi, D. Indra Sensuse, J. Sofian Lusa, Damayanti Elisabeth
Knowledge is a precious asset in an organization. If it is managed adequately, it affects the organization's business process successfully. The current business processes at PT XYZ's Security Command Center are still deemed ineffective in providing command and service to all employees. This research aims to develop the design and evaluation of a knowledge management system using the Design Sprint approach with a case study in PT XYZ's Security Command Center. This research was conducted using the Fernandez methodology to identify the needs of the organization's KMS features and using the Design Sprint methodology to identify the system requirements of KMS. System mockups are shown to Security Command Center employees to evaluate the system requirement. The results of this research indicate that the Design Sprint methodology carried out in only a short time of 5-days can help obtain the complete system requirements. Besides that, the method also shows that the systems built can be as close as the user's expectation. This study can be adapted to other organizations that need a security command center in order to help the organization's operational activities run smoothly.
{"title":"Knowledge Management System Design of the Security Command Center in A Financial and Banking Company with Contingency Factors and Sprint Design Methodology","authors":"F. Humani, Hilman Wisnu, Adyan Pamungkas Ganefi, D. Indra Sensuse, J. Sofian Lusa, Damayanti Elisabeth","doi":"10.1109/ICIC50835.2020.9288551","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288551","url":null,"abstract":"Knowledge is a precious asset in an organization. If it is managed adequately, it affects the organization's business process successfully. The current business processes at PT XYZ's Security Command Center are still deemed ineffective in providing command and service to all employees. This research aims to develop the design and evaluation of a knowledge management system using the Design Sprint approach with a case study in PT XYZ's Security Command Center. This research was conducted using the Fernandez methodology to identify the needs of the organization's KMS features and using the Design Sprint methodology to identify the system requirements of KMS. System mockups are shown to Security Command Center employees to evaluate the system requirement. The results of this research indicate that the Design Sprint methodology carried out in only a short time of 5-days can help obtain the complete system requirements. Besides that, the method also shows that the systems built can be as close as the user's expectation. This study can be adapted to other organizations that need a security command center in order to help the organization's operational activities run smoothly.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131308704","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288585
Y. Sari, M. Alkaff, M. Maulida
Rice is one of the food commodities that is most needed by the Indonesian people. Its condition requires farmers to maximize rice harvest as a rice-producing plant which one of them by providing fertilizer with the right dose. One of the methods used by rice farmers is to use a Leaf Color Chart to compare the color of rice leaves manually which might cause an error. Several research topics of classification based on plant image processing have been done to help the agriculture sector including rice. In this paper, the classification of rice leaves to determine the fertilizer dose by processing the rice leaf image using the HSV method is proposed. Results of rice leaf image processing are classified using fuzzy logic to calculate the right dose of fertilizer and developed as a mobile-based application. The proposed method achieved an accuracy value of 90% for the color of rice leaf and an accuracy value of 82.5% for the determination of fertilizer dose.
{"title":"Classification of Rice Leaf using Fuzzy Logic and Hue Saturation Value (HSV) to Determine Fertilizer Dosage","authors":"Y. Sari, M. Alkaff, M. Maulida","doi":"10.1109/ICIC50835.2020.9288585","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288585","url":null,"abstract":"Rice is one of the food commodities that is most needed by the Indonesian people. Its condition requires farmers to maximize rice harvest as a rice-producing plant which one of them by providing fertilizer with the right dose. One of the methods used by rice farmers is to use a Leaf Color Chart to compare the color of rice leaves manually which might cause an error. Several research topics of classification based on plant image processing have been done to help the agriculture sector including rice. In this paper, the classification of rice leaves to determine the fertilizer dose by processing the rice leaf image using the HSV method is proposed. Results of rice leaf image processing are classified using fuzzy logic to calculate the right dose of fertilizer and developed as a mobile-based application. The proposed method achieved an accuracy value of 90% for the color of rice leaf and an accuracy value of 82.5% for the determination of fertilizer dose.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126907139","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288580
Yohanes Fajar Sitohang, D. Pratami, Achmad Fuad Bay
A company engaged in providing telecommunications network services in Indonesia, carry out various network construction projects. All construction projects are run and led by a project manager who is responsible for the sustainability and success of the project. Unfortunately, the company has never assessed and evaluated the competence of a project manager. This causes project success can not always be achieved, some mistakes often repeat themselves, and sometimes there are irregularities when the project is running. Therefore, this research will evaluate the competence of the project manager to identify deficiencies that the project manager has and how to fix them. The evaluation will be carried out using the Project Manager Competency Development Framework (PMCDF) method developed by PMI (Project Management Institute) which can objectively assess the performance competency of the project manager. PMCDF has ten units of performance competencies that can be performed. It is necessary to eliminate competency units that will be chosen by the company experts through a pairwise comparison questionnaire and after that, the results of the questionnaire are processed using the AHP method. The selection of competency units aims to ensure that the competency units that are assessed and evaluated are units that have a big influence on the running of the project at the company. From the processing results, three competency units that have a major influence on the course of the project in the company project quality management (33%), project cost management (21 %), and project Human Resources (HR) management (17%). Through the results of the evaluation that has been carried out, the project manager already has sufficient competence in the project quality management unit, however, in the cost management unit and project HR management, there are still deficiencies that need to be fixed.
{"title":"Competency Evaluation of Project Manager Performance in Network Construction Projects","authors":"Yohanes Fajar Sitohang, D. Pratami, Achmad Fuad Bay","doi":"10.1109/ICIC50835.2020.9288580","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288580","url":null,"abstract":"A company engaged in providing telecommunications network services in Indonesia, carry out various network construction projects. All construction projects are run and led by a project manager who is responsible for the sustainability and success of the project. Unfortunately, the company has never assessed and evaluated the competence of a project manager. This causes project success can not always be achieved, some mistakes often repeat themselves, and sometimes there are irregularities when the project is running. Therefore, this research will evaluate the competence of the project manager to identify deficiencies that the project manager has and how to fix them. The evaluation will be carried out using the Project Manager Competency Development Framework (PMCDF) method developed by PMI (Project Management Institute) which can objectively assess the performance competency of the project manager. PMCDF has ten units of performance competencies that can be performed. It is necessary to eliminate competency units that will be chosen by the company experts through a pairwise comparison questionnaire and after that, the results of the questionnaire are processed using the AHP method. The selection of competency units aims to ensure that the competency units that are assessed and evaluated are units that have a big influence on the running of the project at the company. From the processing results, three competency units that have a major influence on the course of the project in the company project quality management (33%), project cost management (21 %), and project Human Resources (HR) management (17%). Through the results of the evaluation that has been carried out, the project manager already has sufficient competence in the project quality management unit, however, in the cost management unit and project HR management, there are still deficiencies that need to be fixed.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125420152","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288552
Retantyo Wardoyo, Aina Musdholifah, Gede Angga Pradipta, I. N. Hariyasa Sanjaya
Ensemble classifier method uses several base classifiers to predict a new test instance, while weighted majority voting has a scheme providing different weight values using several measurement parameters. However, the determination of the appropriate weight value to obtain an adequate ensemble model is a critical issue. This study, therefore, proposed a novel weighted majority voting scheme involving five base classifiers based on ensemble learning, including Random Forest, Decision Tree (C.45), Gradient Boosting Machine, XGBosst, and Bagging. The weighting scheme was formulated by analyzing the base classifier performance measured from the parameters of accuracy, recall, precision, and F Measure. The experiments were conducted using public datasets and umbilical cord data owned and the results showed the proposed method has the ability to improve performance in comparison with the base classifier and methods from previous studies with the best recorded in umbilical cord dataset with an average accuracy of 86.1%, a precision of 86%, a recall of 86%, and an F measure of 86%.
{"title":"Weighted Majority Voting by Statistical Performance Analysis on Ensemble Multiclassifier","authors":"Retantyo Wardoyo, Aina Musdholifah, Gede Angga Pradipta, I. N. Hariyasa Sanjaya","doi":"10.1109/ICIC50835.2020.9288552","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288552","url":null,"abstract":"Ensemble classifier method uses several base classifiers to predict a new test instance, while weighted majority voting has a scheme providing different weight values using several measurement parameters. However, the determination of the appropriate weight value to obtain an adequate ensemble model is a critical issue. This study, therefore, proposed a novel weighted majority voting scheme involving five base classifiers based on ensemble learning, including Random Forest, Decision Tree (C.45), Gradient Boosting Machine, XGBosst, and Bagging. The weighting scheme was formulated by analyzing the base classifier performance measured from the parameters of accuracy, recall, precision, and F Measure. The experiments were conducted using public datasets and umbilical cord data owned and the results showed the proposed method has the ability to improve performance in comparison with the base classifier and methods from previous studies with the best recorded in umbilical cord dataset with an average accuracy of 86.1%, a precision of 86%, a recall of 86%, and an F measure of 86%.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130400922","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288582
Irdina Wanda Syahputri, R. Ferdiana, S. Kusumawardani
Software engineering is the most important stage in developing a system. Software engineering is used to facilitate developers in developing systems in the form of a mobile, web, or artificial intelligence-based system. Systematic Review is a way to find data and related problems that can strengthen a person to conduct a study. In this paper. Researchers conducted a systematic review to find whether an Artificial Intelligence-based system requires Software Engineering when designing the system. The main purpose of this systematic review is to gather prior research related to developing Artificial Intelligence-based systems from design to the implementation phase and discover what methods are they common use in developing their systems and define what is the reason behind they selected method or even does not use Software Engineering methods in developing them.
{"title":"Does System Based on Artificial Intelligence Need Software Engineering Method? Systematic Review","authors":"Irdina Wanda Syahputri, R. Ferdiana, S. Kusumawardani","doi":"10.1109/ICIC50835.2020.9288582","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288582","url":null,"abstract":"Software engineering is the most important stage in developing a system. Software engineering is used to facilitate developers in developing systems in the form of a mobile, web, or artificial intelligence-based system. Systematic Review is a way to find data and related problems that can strengthen a person to conduct a study. In this paper. Researchers conducted a systematic review to find whether an Artificial Intelligence-based system requires Software Engineering when designing the system. The main purpose of this systematic review is to gather prior research related to developing Artificial Intelligence-based systems from design to the implementation phase and discover what methods are they common use in developing their systems and define what is the reason behind they selected method or even does not use Software Engineering methods in developing them.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132793699","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288523
Muhamad Fatchan, Mauridhi Hery Purnomo, Affandy, A. Zainul Fanani, Linda Marlinda
One of the obstacles to detecting facial emotions is the lack of analysis in the process of emotional expression on human faces based on a photo camera. Identifying features and implementing different feature combinations can improve accuracy, in most cases. Trial detection through the use of feature vectors with higher dimensions that contain facial emotions can provide a lot of information on the accuracy of the data. The purpose of this study is to determine the highest accuracy results from the comparison of Support Vector Machine and Neural Network Algorithms. It can be seen that the Support Vector Machine has the highest accuracy value, which is 87%, while the Neural Network algorithm only has an accuracy of 85%. The results of the ROC Curve show that the Support Vector Machine achieves the best AUC value, namely 0.97. Comparison between Neural Network algorithm and Support Vector Machine for prediction of emotions using data types with many variations of emotions including anger, sadness, happy and surprise. The Support Vector Machine method is an accurate algorithm and this method is also very dominant over other methods. Based on the Accuracy, AUC, and T-test method, this method falls into the best classification.
{"title":"Support Vector Machine and Neural Network Algorithm Approach to Classifying Facial Expression Recognition","authors":"Muhamad Fatchan, Mauridhi Hery Purnomo, Affandy, A. Zainul Fanani, Linda Marlinda","doi":"10.1109/ICIC50835.2020.9288523","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288523","url":null,"abstract":"One of the obstacles to detecting facial emotions is the lack of analysis in the process of emotional expression on human faces based on a photo camera. Identifying features and implementing different feature combinations can improve accuracy, in most cases. Trial detection through the use of feature vectors with higher dimensions that contain facial emotions can provide a lot of information on the accuracy of the data. The purpose of this study is to determine the highest accuracy results from the comparison of Support Vector Machine and Neural Network Algorithms. It can be seen that the Support Vector Machine has the highest accuracy value, which is 87%, while the Neural Network algorithm only has an accuracy of 85%. The results of the ROC Curve show that the Support Vector Machine achieves the best AUC value, namely 0.97. Comparison between Neural Network algorithm and Support Vector Machine for prediction of emotions using data types with many variations of emotions including anger, sadness, happy and surprise. The Support Vector Machine method is an accurate algorithm and this method is also very dominant over other methods. Based on the Accuracy, AUC, and T-test method, this method falls into the best classification.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122954591","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288553
Maimunah, Mukhtar Hanafi, Bayu Agustian
Indonesian herbal drink, called Jamu, is one of the cultural heritage herbal drinks which is one of the characteristics of Indonesian culture. The raw material for herbal medicine is the herbal plant that has many benefits with distinctive characteristics, i.e. color, smell, and texture. In Indonesia, there are types of raw materials for herbal medicine, called empon-empon, galangal, and turmeric which are similar in color, shape, and smell. Therefore, ordinary people sometimes difficult to classify. In this study, the types of empon-empon based on their smell were classified into four classes, namely, ginger, galangal, and turmeric-based on their odor. The smell of the empon-empon is obtained from the e-nose which designed using the TGS2611, TGS813, and MQ136 sensors connected to the Arduino Uno. The smell characteristic of empon-empon is used as the value of the sensor voltage. The voltage values that have been obtained are classified using a deep neural network. Based on the results of the classification, it is found that the deep neural network can classify the types of empon-empon based on odor with an accuracy of 86%.
{"title":"Deep Neural Network Method to Classify Empon-Empon Herb Based on E-Nose","authors":"Maimunah, Mukhtar Hanafi, Bayu Agustian","doi":"10.1109/ICIC50835.2020.9288553","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288553","url":null,"abstract":"Indonesian herbal drink, called Jamu, is one of the cultural heritage herbal drinks which is one of the characteristics of Indonesian culture. The raw material for herbal medicine is the herbal plant that has many benefits with distinctive characteristics, i.e. color, smell, and texture. In Indonesia, there are types of raw materials for herbal medicine, called empon-empon, galangal, and turmeric which are similar in color, shape, and smell. Therefore, ordinary people sometimes difficult to classify. In this study, the types of empon-empon based on their smell were classified into four classes, namely, ginger, galangal, and turmeric-based on their odor. The smell of the empon-empon is obtained from the e-nose which designed using the TGS2611, TGS813, and MQ136 sensors connected to the Arduino Uno. The smell characteristic of empon-empon is used as the value of the sensor voltage. The voltage values that have been obtained are classified using a deep neural network. Based on the results of the classification, it is found that the deep neural network can classify the types of empon-empon based on odor with an accuracy of 86%.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128971695","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}
A stroke is an attack that often requires long-term rehabilitation. One result of this condition can be seen from abnormal electrical signals in the brain, recorded by an electroencephalogram (EEG). Therefore, EEG can be used for monitoring and evaluation of post-stroke rehabilitation. Neurologists usually observe EEG signals based on their density, amplitude, waveform, and comparison of the channel pairs, but this analysis is not easy. Besides, using machine learning, such as Backpropagation, is sometimes constrained by random initial weights. This state can lead to a long convergence. This paper proposes the selection of initial weights in Backpropagation training using Genetic Algorithms. The use of Genetic Algorithms can optimize the initial weight selection in Backpropagation. The EEG signal used has been extracted into Alpha, Theta, Delta, and Mu waves. The experimental results show that using the Genetic Algorithm can increase non-training data accuracy to 75%, compared to only 65% without the genetic algorithm. Genetic Algorithms can overcome overfitting and local maximums. The results also show that the use of Wavelet transform for feature extraction can increase the accuracy from 60% to 75%. The optimization of training parameters also determines the accuracy.
{"title":"Learning Optimization Using Genetic Algorithm in Post-Stroke EEG Signal Classification","authors":"Esmeralda Contessa Djamal, Mita Amara, Daswara Djajasasmita, Sandy Lesmana Liem Limanjaya","doi":"10.1109/ICIC50835.2020.9288531","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288531","url":null,"abstract":"A stroke is an attack that often requires long-term rehabilitation. One result of this condition can be seen from abnormal electrical signals in the brain, recorded by an electroencephalogram (EEG). Therefore, EEG can be used for monitoring and evaluation of post-stroke rehabilitation. Neurologists usually observe EEG signals based on their density, amplitude, waveform, and comparison of the channel pairs, but this analysis is not easy. Besides, using machine learning, such as Backpropagation, is sometimes constrained by random initial weights. This state can lead to a long convergence. This paper proposes the selection of initial weights in Backpropagation training using Genetic Algorithms. The use of Genetic Algorithms can optimize the initial weight selection in Backpropagation. The EEG signal used has been extracted into Alpha, Theta, Delta, and Mu waves. The experimental results show that using the Genetic Algorithm can increase non-training data accuracy to 75%, compared to only 65% without the genetic algorithm. Genetic Algorithms can overcome overfitting and local maximums. The results also show that the use of Wavelet transform for feature extraction can increase the accuracy from 60% to 75%. The optimization of training parameters also determines the accuracy.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114263200","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288571
Riko Saputra, Julpri Andika, M. Alaydrus
Wireless Sensor Network (WSN) is a heterogeneous type of network consisting of scattered sensor nodes and working together for data collection, processing, and transmission functions[1], [2]. Because WSN is widely used in vital matters, aspects of its security must also be considered. There are many types of attacks that might be carried out to disrupt WSN networks. The methods of attack that exist in WSN include jamming attack, tampering, Sybil attack, wormhole attack, hello flood attack, and, blackhole attack[3]. Blackhole attacks are one of the most dangerous attacks on WSN networks. Enhanced Check Agent method is designed to detect black hole attacks by sending a checking agent to record nodes that are considered black okay. The implementation will be tested right on a wireless sensor network using ZigBee technology. Network topology uses a mesh where each node can have more than one routing table[4]. The Enhanced Check Agent method can increase throughput to 100 percent.
{"title":"Detection of Blackhole Attack in Wireless Sensor Network Using Enhanced Check Agent","authors":"Riko Saputra, Julpri Andika, M. Alaydrus","doi":"10.1109/ICIC50835.2020.9288571","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288571","url":null,"abstract":"Wireless Sensor Network (WSN) is a heterogeneous type of network consisting of scattered sensor nodes and working together for data collection, processing, and transmission functions[1], [2]. Because WSN is widely used in vital matters, aspects of its security must also be considered. There are many types of attacks that might be carried out to disrupt WSN networks. The methods of attack that exist in WSN include jamming attack, tampering, Sybil attack, wormhole attack, hello flood attack, and, blackhole attack[3]. Blackhole attacks are one of the most dangerous attacks on WSN networks. Enhanced Check Agent method is designed to detect black hole attacks by sending a checking agent to record nodes that are considered black okay. The implementation will be tested right on a wireless sensor network using ZigBee technology. Network topology uses a mesh where each node can have more than one routing table[4]. The Enhanced Check Agent method can increase throughput to 100 percent.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114489378","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288613
R. Arafiyah, Z. Hasibuan, Harry Budi Santoso
Monitoring the progress of students is part of the teacher's job which is very important and very time-consuming. Especially if there are many students with various subjects. This is the experience of most primary school teachers in Indonesia. One way to solve this problem is to predict student progress. In this study, the students' progress was predicted using Random Forest. The Random Forest algorithm is used because it can classify data that has incomplete attributes, which are usually found in student assessment data. The prediction model was built based on assessment data from 2 classes with 46 elementary school students in subjects: Indonesian, mathematics, SBdP (Cultural Arts and Crafts), PPKN (Pancasila and Citizenship Education), and Computers. The dataset comes from the formative and summative assessment results from 3 aspects (cognitive, psychomotor, and affective). The resulting model performance will be measured using accuracy and recall. The results showed that using a dataset of 5 subjects from 46 students, the Random Forest algorithm produced a learning progress model with 100% accuracy for training data and 94% for testing data. Meanwhile, the learning progress prediction model for each subject has 100% accuracy on training data and more than 96% on test data.
{"title":"Learning Progress Modeling for Monitoring Student","authors":"R. Arafiyah, Z. Hasibuan, Harry Budi Santoso","doi":"10.1109/ICIC50835.2020.9288613","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288613","url":null,"abstract":"Monitoring the progress of students is part of the teacher's job which is very important and very time-consuming. Especially if there are many students with various subjects. This is the experience of most primary school teachers in Indonesia. One way to solve this problem is to predict student progress. In this study, the students' progress was predicted using Random Forest. The Random Forest algorithm is used because it can classify data that has incomplete attributes, which are usually found in student assessment data. The prediction model was built based on assessment data from 2 classes with 46 elementary school students in subjects: Indonesian, mathematics, SBdP (Cultural Arts and Crafts), PPKN (Pancasila and Citizenship Education), and Computers. The dataset comes from the formative and summative assessment results from 3 aspects (cognitive, psychomotor, and affective). The resulting model performance will be measured using accuracy and recall. The results showed that using a dataset of 5 subjects from 46 students, the Random Forest algorithm produced a learning progress model with 100% accuracy for training data and 94% for testing data. Meanwhile, the learning progress prediction model for each subject has 100% accuracy on training data and more than 96% on test data.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127385231","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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