Pub Date : 2018-10-31DOI: 10.14710/jtsiskom.6.4.2018.158-163
Ari Fadli, Mulki Indana Zulfa, Y. Ramadhani
Observation of growing academic data can be carried using data mining methods, for example, to obtain knowledge related to the determinants of timeliness of students graduation. This study conducted a performance comparison of the classification algorithms using decision tree (DT), support vector machine (SVM), and artificial neural network (ANN). This study used students academic data from Faculty of Engineering, Universitas Jenderal Soedirman in the 2014/2015 odd semester until the 2017/2018 odd semester and the attributes that conform to the academic regulations. The analytical method used is CRISP-DM. The results showed that SVM provided the best performance in an accuracy of 90.55% and AUC of 0.959, compared to other algorithms. A Model with SVM algorithm can be implemented in an early warning system for timeliness of student graduation.
{"title":"Performance Comparison of Data Mining Classification Algorithms for Early Warning System of Students Graduation Timeliness","authors":"Ari Fadli, Mulki Indana Zulfa, Y. Ramadhani","doi":"10.14710/jtsiskom.6.4.2018.158-163","DOIUrl":"https://doi.org/10.14710/jtsiskom.6.4.2018.158-163","url":null,"abstract":"Observation of growing academic data can be carried using data mining methods, for example, to obtain knowledge related to the determinants of timeliness of students graduation. This study conducted a performance comparison of the classification algorithms using decision tree (DT), support vector machine (SVM), and artificial neural network (ANN). This study used students academic data from Faculty of Engineering, Universitas Jenderal Soedirman in the 2014/2015 odd semester until the 2017/2018 odd semester and the attributes that conform to the academic regulations. The analytical method used is CRISP-DM. The results showed that SVM provided the best performance in an accuracy of 90.55% and AUC of 0.959, compared to other algorithms. A Model with SVM algorithm can be implemented in an early warning system for timeliness of student graduation.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67028535","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}
Bamboo species can be identified from the bamboo leaf images. This study conducted the identification of bamboo species based on leaf texture using Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) for texture feature extraction, and Euclidean distance for measure the image distance. This study used the images of bamboo species in Bengkulu province, that are bambusa Vulgaris Var Vulgaris, bambusa Multiplex, bambusa Vulgaris Var Striata, Gigantochloa Robusta, Gigantochloa Schortrchinii, Gigantochloa Serik, Schizostachyum Brachycladum, and Dendrocalamus Asper. The bamboo application was built using Matlab. The accuracy of the application was 100% for bamboo leaf test images captured using a smartphone camera and 81.25% for test images downloaded from the Internet.
{"title":"Identifikasi Jenis Bambu Berdasarkan Tekstur Daun dengan Metode Gray Level Co-Occurrence Matrix dan Gray Level Run Length Matrix","authors":"Endina Putri Purwandari, Rachmi Ulizah Hasibuan, Desi Andreswari","doi":"10.14710/jtsiskom.6.4.2018.146-151","DOIUrl":"https://doi.org/10.14710/jtsiskom.6.4.2018.146-151","url":null,"abstract":"Bamboo species can be identified from the bamboo leaf images. This study conducted the identification of bamboo species based on leaf texture using Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) for texture feature extraction, and Euclidean distance for measure the image distance. This study used the images of bamboo species in Bengkulu province, that are bambusa Vulgaris Var Vulgaris, bambusa Multiplex, bambusa Vulgaris Var Striata, Gigantochloa Robusta, Gigantochloa Schortrchinii, Gigantochloa Serik, Schizostachyum Brachycladum, and Dendrocalamus Asper. The bamboo application was built using Matlab. The accuracy of the application was 100% for bamboo leaf test images captured using a smartphone camera and 81.25% for test images downloaded from the Internet.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42177782","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 : 2018-10-31DOI: 10.14710/JTSISKOM.6.4.2018.152-157
Reza Fuad Rachmadi, I. Purnama
In this paper, we proposed a parallel spatial pyramid CNN classifier for image-based kinship verification problem. Two face images that compared for kinship verification treated as input for each parallel convolutional network of our classifier. Each parallel convolutional network constructed using spatial pyramid CNN classifier. At the end of the convolutional network, we use three fully connected layers to combine each spatial pyramid CNN features and decided the final kinship prediction. We tested the proposed classifier using large-scale kinship verification dataset, called FIW dataset, consists of seven kinship problems from 1,000 families. In our approach, we treated each kinship problem as a binary classification problem with two output. We train our classifier separately for each kinship problem with same training configuration. Overall, our proposed method can achieve an average accuracy of more than 60% and outperform the baseline method.
{"title":"Paralel Spatial Pyramid Convolutional Neural Network untuk Verifikasi Kekerabatan berbasis Citra Wajah","authors":"Reza Fuad Rachmadi, I. Purnama","doi":"10.14710/JTSISKOM.6.4.2018.152-157","DOIUrl":"https://doi.org/10.14710/JTSISKOM.6.4.2018.152-157","url":null,"abstract":"In this paper, we proposed a parallel spatial pyramid CNN classifier for image-based kinship verification problem. Two face images that compared for kinship verification treated as input for each parallel convolutional network of our classifier. Each parallel convolutional network constructed using spatial pyramid CNN classifier. At the end of the convolutional network, we use three fully connected layers to combine each spatial pyramid CNN features and decided the final kinship prediction. We tested the proposed classifier using large-scale kinship verification dataset, called FIW dataset, consists of seven kinship problems from 1,000 families. In our approach, we treated each kinship problem as a binary classification problem with two output. We train our classifier separately for each kinship problem with same training configuration. Overall, our proposed method can achieve an average accuracy of more than 60% and outperform the baseline method.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44770710","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 : 2018-10-31DOI: 10.14710/JTSISKOM.6.4.2018.139-145
Putu Agus Fredy, M. Abdurohman
This paper presents a study on an accurate soil moisture monitoring system based on its humidity from 9 sensor nodes using wireless sensor network (WSN) and M2M platform. The system used IEEE 802.15.4 (Zigbee) protocol. The system was connected to the application via the OpenMTC M2M platform. This monitoring system can measure soil moisture accurately and provide soil water content status on the application. The system was effective in measuring soil moisture at a distance of 0-25 meters where there was a barrier between gateway and sensor, and at a distance of 0-50 meter in line of sight. The position of the sensors that are within 3 meters of each other and the depth of each sensor 3 cm can measure soil moisture properly.
{"title":"Sistem Pemantau Kelembapan Tanah Akurat dengan Protokol Zigbee IEEE 802.15.4 pada Platform M2M OpenMTC","authors":"Putu Agus Fredy, M. Abdurohman","doi":"10.14710/JTSISKOM.6.4.2018.139-145","DOIUrl":"https://doi.org/10.14710/JTSISKOM.6.4.2018.139-145","url":null,"abstract":"This paper presents a study on an accurate soil moisture monitoring system based on its humidity from 9 sensor nodes using wireless sensor network (WSN) and M2M platform. The system used IEEE 802.15.4 (Zigbee) protocol. The system was connected to the application via the OpenMTC M2M platform. This monitoring system can measure soil moisture accurately and provide soil water content status on the application. The system was effective in measuring soil moisture at a distance of 0-25 meters where there was a barrier between gateway and sensor, and at a distance of 0-50 meter in line of sight. The position of the sensors that are within 3 meters of each other and the depth of each sensor 3 cm can measure soil moisture properly.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46249331","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 : 2018-07-31DOI: 10.14710/JTSISKOM.6.3.2018.100-105
F. Rachman
This research developed a gas monitoring system in the final waste disposal. The system has implemented the Internet of Things (IoT) using the ESP8266 Wi-Fi module to transmit methane (CH4) and carbon dioxide (CO2) data concentration, as well as temperature and humidity to the ThingSpeak server. Users can monitor and access these environmental data through social media Twitter and websites from anywhere. The fastest data delivery can be obtained with a time interval of 16 seconds on each data packet sent when there is an Internet connection.
{"title":"Sistem Pemantau Gas di Tempat Pembuangan Sampah Akhir Berbasis Internet of Things","authors":"F. Rachman","doi":"10.14710/JTSISKOM.6.3.2018.100-105","DOIUrl":"https://doi.org/10.14710/JTSISKOM.6.3.2018.100-105","url":null,"abstract":"This research developed a gas monitoring system in the final waste disposal. The system has implemented the Internet of Things (IoT) using the ESP8266 Wi-Fi module to transmit methane (CH4) and carbon dioxide (CO2) data concentration, as well as temperature and humidity to the ThingSpeak server. Users can monitor and access these environmental data through social media Twitter and websites from anywhere. The fastest data delivery can be obtained with a time interval of 16 seconds on each data packet sent when there is an Internet connection.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45670431","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 : 2018-07-31DOI: 10.14710/JTSISKOM.6.3.2018.115-121
Aliyu Ahmed, A. A. Lukman, Agajo James, O. O. Mikail, Buhari U. Umar, Emmanuel Samuel
Human vital physiological parameters (HVPP) monitoring with embedded sensors integration has improved the smart system technology in this era of a ubiquitous platform. Several IoT-based healthcare applications have been proposed for remote health monitoring. Most of the devices developed require one on one contact with doctors before any medical diagnosis is undertaken. Thereby, make it difficult for frequent visitation to the health center. In this paper, embedded heartbeat and temperature sensors for remote monitoring have been developed using Arduino lily as the system controller and processing unit. The Bluetooth low power enables with Android mobile apps is used for remote monitoring and communication of HVPP in a real time. This gives medical personnel and individual customers opportunity of monitoring their vital physiological parameters such as heartbeat rate and body temperature. However, it moderates sudden attack of chronic ailment like hypertension and reduces congestion of patient in the hospitals.
{"title":"Human Vital Physiological Parameters Monitoring: A Wireless Body Area Technology Based Internet of Things","authors":"Aliyu Ahmed, A. A. Lukman, Agajo James, O. O. Mikail, Buhari U. Umar, Emmanuel Samuel","doi":"10.14710/JTSISKOM.6.3.2018.115-121","DOIUrl":"https://doi.org/10.14710/JTSISKOM.6.3.2018.115-121","url":null,"abstract":"Human vital physiological parameters (HVPP) monitoring with embedded sensors integration has improved the smart system technology in this era of a ubiquitous platform. Several IoT-based healthcare applications have been proposed for remote health monitoring. Most of the devices developed require one on one contact with doctors before any medical diagnosis is undertaken. Thereby, make it difficult for frequent visitation to the health center. In this paper, embedded heartbeat and temperature sensors for remote monitoring have been developed using Arduino lily as the system controller and processing unit. The Bluetooth low power enables with Android mobile apps is used for remote monitoring and communication of HVPP in a real time. This gives medical personnel and individual customers opportunity of monitoring their vital physiological parameters such as heartbeat rate and body temperature. However, it moderates sudden attack of chronic ailment like hypertension and reduces congestion of patient in the hospitals.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47756432","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 : 2018-07-31DOI: 10.14710/jtsiskom.6.3.2018.93-99
M. Djalal, Sonong Sonong
This research proposed a tuning method of power system stabilizer (PSS) using an intelligent method based on flower pollination algorithm (FPA) on Pajalesang generator located in Soppeng district. The observed result is the deviation response of velocity and rotor angle in case of disturbance. The case study used as the disturbance to this generator system is a load addition of 0.05 pu. The results show that velocity deviation response without PSS is 0.01152 pu to -0.0248 pu, using PSS trial is 0.007014 pu to -0.02174 pu, using PSS bat algorithm is 0.003972 pu to -0.01865 pu, and using the proposed method of PSS flower algorithm is 0.002149 pu to -0.01678 pu. The rotor angle response shows better results with reduced oscillation and rapidly leading to the steady-state condition. The performance of Pajalesang diesel power plant increased with the installation of FPA PSS, with parameters respectively Kpss=8.5956, T1= 0.0247, T2=0.2484, T3=0.4776, and T4=0.8827.
{"title":"PSS Tuning on Power Generator System using Flower Pollination Algorithm","authors":"M. Djalal, Sonong Sonong","doi":"10.14710/jtsiskom.6.3.2018.93-99","DOIUrl":"https://doi.org/10.14710/jtsiskom.6.3.2018.93-99","url":null,"abstract":"This research proposed a tuning method of power system stabilizer (PSS) using an intelligent method based on flower pollination algorithm (FPA) on Pajalesang generator located in Soppeng district. The observed result is the deviation response of velocity and rotor angle in case of disturbance. The case study used as the disturbance to this generator system is a load addition of 0.05 pu. The results show that velocity deviation response without PSS is 0.01152 pu to -0.0248 pu, using PSS trial is 0.007014 pu to -0.02174 pu, using PSS bat algorithm is 0.003972 pu to -0.01865 pu, and using the proposed method of PSS flower algorithm is 0.002149 pu to -0.01678 pu. The rotor angle response shows better results with reduced oscillation and rapidly leading to the steady-state condition. The performance of Pajalesang diesel power plant increased with the installation of FPA PSS, with parameters respectively Kpss=8.5956, T1= 0.0247, T2=0.2484, T3=0.4776, and T4=0.8827.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44604207","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 : 2018-07-31DOI: 10.14710/JTSISKOM.6.3.2018.122-128
Ahmad Riyandi, S. Sumardi, T. Prakoso
The moving vehicles require an antenna to communicate which is placed on the vehicles and at the ground station (ground control station, GCS). Generally, GCS uses a directional antenna equipped with the drive system with the conventional proportional, proportional-integral, or proportional-integral-derivative (PID) control, and step-tracking algorithms based on the received signal strength indicator (RSSI). This research used PID control method tuned with fuzzy logic based on Global Positioning System (GPS) to control a directional antenna at GCS. The resulting antenna tracker system was capable of tracking objects with a minimal error of 0° at azimuth and elevation angle and had a maximal error of 49° for a 49 km/hour speed object. The system had an average rise time of 0.7 seconds at an azimuth angle and 1.08 seconds at an elevation angle. This system can be used to control antenna direction for moving vehicles, such as an unmanned aerial vehicle (UAV) and rocket.
{"title":"PID Parameters Auto-Tuning on GPS-based Antenna Tracker Control using Fuzzy Logic","authors":"Ahmad Riyandi, S. Sumardi, T. Prakoso","doi":"10.14710/JTSISKOM.6.3.2018.122-128","DOIUrl":"https://doi.org/10.14710/JTSISKOM.6.3.2018.122-128","url":null,"abstract":"The moving vehicles require an antenna to communicate which is placed on the vehicles and at the ground station (ground control station, GCS). Generally, GCS uses a directional antenna equipped with the drive system with the conventional proportional, proportional-integral, or proportional-integral-derivative (PID) control, and step-tracking algorithms based on the received signal strength indicator (RSSI). This research used PID control method tuned with fuzzy logic based on Global Positioning System (GPS) to control a directional antenna at GCS. The resulting antenna tracker system was capable of tracking objects with a minimal error of 0° at azimuth and elevation angle and had a maximal error of 49° for a 49 km/hour speed object. The system had an average rise time of 0.7 seconds at an azimuth angle and 1.08 seconds at an elevation angle. This system can be used to control antenna direction for moving vehicles, such as an unmanned aerial vehicle (UAV) and rocket.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46869591","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 : 2018-07-31DOI: 10.14710/jtsiskom.6.3.2018.110-114
T. Wulandari, Ajib Susanto
The risk of pregnancy is a contributing factor in increasing mother maternal mortality (MMR). This study aimed to produce a pregnancy risk detection system based on patient examination results. This research combines fuzzy Mamdani and Simple Additive Weighting (SAW) methods using 11 criteria to determine the risk of pregnant women, that is low, high, and very high. The criteria that determine the risk of pregnancy are expressed as fuzzy statements. In system testing to 100 pregnant women patients, obtained an accuracy of 88% using recognition rate method.
{"title":"Deteksi Tingkat Risiko Kehamilan dengan Metode Fuzzy Mamdani dan Simple Additive Weighting","authors":"T. Wulandari, Ajib Susanto","doi":"10.14710/jtsiskom.6.3.2018.110-114","DOIUrl":"https://doi.org/10.14710/jtsiskom.6.3.2018.110-114","url":null,"abstract":"The risk of pregnancy is a contributing factor in increasing mother maternal mortality (MMR). This study aimed to produce a pregnancy risk detection system based on patient examination results. This research combines fuzzy Mamdani and Simple Additive Weighting (SAW) methods using 11 criteria to determine the risk of pregnant women, that is low, high, and very high. The criteria that determine the risk of pregnancy are expressed as fuzzy statements. In system testing to 100 pregnant women patients, obtained an accuracy of 88% using recognition rate method.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47626896","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 : 2018-07-31DOI: 10.14710/JTSISKOM.6.3.2018.106-109
Tri Septianto, E. Setyati, Joan Santoso
The object of the inscription has a feature that is difficult to recognize because it is generally eroded and faded. This study analyzed the performance of CNN using LeNet model to recognize the object of year digit found on the relic inscriptions of Majapahit Kingdom. Object recognition with LeNet model had a maximum accuracy of 85.08% at 10 epoch in 6069 seconds. This LeNet's performance was better than the VGG as the comparison model with a maximum accuracy of 11.39% at 10 epoch in 40223 seconds.
{"title":"Model CNN LeNet dalam Rekognisi Angka Tahun pada Prasasti Peninggalan Kerajaan Majapahit","authors":"Tri Septianto, E. Setyati, Joan Santoso","doi":"10.14710/JTSISKOM.6.3.2018.106-109","DOIUrl":"https://doi.org/10.14710/JTSISKOM.6.3.2018.106-109","url":null,"abstract":"The object of the inscription has a feature that is difficult to recognize because it is generally eroded and faded. This study analyzed the performance of CNN using LeNet model to recognize the object of year digit found on the relic inscriptions of Majapahit Kingdom. Object recognition with LeNet model had a maximum accuracy of 85.08% at 10 epoch in 6069 seconds. This LeNet's performance was better than the VGG as the comparison model with a maximum accuracy of 11.39% at 10 epoch in 40223 seconds.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46366873","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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