Scientific Journal of Informatics最新文献_第5页

Analysis of Digital Supply Chain Management in E-procurement Service Usage Using Decision Making Trial Method and Evaluation Laboratory in National Public Procurement Agency 基于决策试验方法和评估实验室的国家公共采购机构电子采购服务数字化供应链管理分析

Scientific Journal of Informatics Pub Date : 2023-04-20 DOI: 10.15294/sji.v10i2.42224

Anggar Jati Saesario, Devi Ajeng Efrilianda

{"title":"Analysis of Digital Supply Chain Management in E-procurement Service Usage Using Decision Making Trial Method and Evaluation Laboratory in National Public Procurement Agency","authors":"Anggar Jati Saesario, Devi Ajeng Efrilianda","doi":"10.15294/sji.v10i2.42224","DOIUrl":"https://doi.org/10.15294/sji.v10i2.42224","url":null,"abstract":"This study aimed to identify the implementation of the digital supply chain and its priority in the National Public Procurement Agency’s e-procurement service. To this end, a quantitative study with Decision-Making Trial and Evaluation Laboratory (DEMATEL) method using Matlab R2105a. DEMATEL result showed that eight criteria in the dispatcher group became the main priority in DSCM implementation in LKPP’s e-procurement process. In comparison, the other eight criteria in the receiver group serve as the advanced criteria in DSCM implementation. The planning criterion was the most prioritized and the most influential DSCM criterion in LKPP’s e-procurement service, which covers the distribution requirement process. The three most important DSCM criteria in LKPP’s e-procurement service were the distribution requirement process, delivery and inspection, and transportation management. The analysis showed that some DSCM criteria in LKPP’s e-procurement had met the DSCM principle. Further studies using other methods like Analytic Hierarchy Process (AHP) and Analytical Network Process (ANP) are needed.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41610920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Scoring Using Term Frequency Inverse Document Frequency Document Frequency and Cosine Similarity 术语频率逆文档频率文档频率和余弦相似度的自动评分

Scientific Journal of Informatics Pub Date : 2023-04-20 DOI: 10.15294/sji.v10i2.42209

W. Yulita, M. Untoro, Mugi Praseptiawan, Ilham Firman Ashari, Aidil Afriansyah, Ahmad Naim Bin Che Pee

{"title":"Automatic Scoring Using Term Frequency Inverse Document Frequency Document Frequency and Cosine Similarity","authors":"W. Yulita, M. Untoro, Mugi Praseptiawan, Ilham Firman Ashari, Aidil Afriansyah, Ahmad Naim Bin Che Pee","doi":"10.15294/sji.v10i2.42209","DOIUrl":"https://doi.org/10.15294/sji.v10i2.42209","url":null,"abstract":"Purpose: In the learning process, most of the tests to assess learning achievement have been carried out by providing questions in the form of short answers or essay questions. The variety of answers given by students makes a teacher have to focus on reading them. This scoring process is difficult to guarantee quality if done manually. In addition, each class is taught by a different teacher, which can lead to unequal grades obtained by students due to the influence of differences in teacher experience. Therefore the purpose of this study is to develop an assessment of the answers. Automated short answer scoring is designed to automatically grade and evaluate students' answers based on a series of trained answer documents.Methods: This is ‘how’ you did it. Let readers know exactly what you did to reach your results. For example, did you undertake interviews? Did you carry out an experiment in the lab? What tools, methods, protocols or datasets did you use The method used is TF-IDF-DF and Similarity and scoring computation. Theword weight used is the term Frequency-Inverse Documents Frequency -Document Frequency (TF-IDF-DF) method. The data used is 5 questions with each question answered by 30 students, while the students' answers are assessed by teachers/experts to determine the real score. The study was evaluated by Mean Absolute Error (MAE).Result: The evaluation results obtained Mean Absolute Error (MAE) with a resulting value of 0.123.Value: The word weighting method used is the Term Frequency Inverse Document Frequency DocumentFrequency (TF-IDF-DF) which is an improvement over the Term Frequency Inverse Document Frequency (TF-IDF) method. This method is a method of weighting words that will be applied before calculating the similarity of sentences between teachers and students.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47940846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Water Feedback Solutions Using Internet of Thing (IoT) Enabled Water Pumps Powered by Solar Panels 使用物联网（IoT）驱动的太阳能电池板水泵的混合水反馈解决方案

Scientific Journal of Informatics Pub Date : 2023-02-20 DOI: 10.15294/sji.v10i1.41007

A. Buchori, A. Kusmantoro, Aan Burhanuddin, T. Hiyama

{"title":"Hybrid Water Feedback Solutions Using Internet of Thing (IoT) Enabled Water Pumps Powered by Solar Panels","authors":"A. Buchori, A. Kusmantoro, Aan Burhanuddin, T. Hiyama","doi":"10.15294/sji.v10i1.41007","DOIUrl":"https://doi.org/10.15294/sji.v10i1.41007","url":null,"abstract":"Purpose: Renewable energy in Indonesia is very abundant, one of which is solar energy. With Indonesia's location on the equator, the abundant potential of solar energy can be utilized as an environmentally friendly source of electricity. To increase the potential of solar energy as a source of electricity, it is equipped with a battery storage system. The most widely used electrical energy storage system is the battery. The purpose of this study is the use of solar energy for DC water pumps. The proposed system is also equipped with power, voltage and current monitoring on the solar panel side and on the load side (water pump).Methods/Study design/approach: The method used is to conduct a literature review to study and find out the development of a power monitoring system using solar panels. The next step is to measure solar radiation, calculate PV capacity and SCC. Furthermore, designing, modeling, simulating, analyzing, and implementing the optimal topology for water pump control using solar panels.Result/Findings: The results of the research are water pump control coordination devices using Sonoff with an IoT-based monitoring system. This device is capable of controlling PV and battery power flow. A prototype of a solar water pump has been produced which has been validated by experts in the field of appropriate technology with feasible results and received a positive response from farmers in Demak to be immediately implemented in the fields to help with the water crisis in agricultural landNovelty/Originality/Value: the advantage of this solar water pump is that the product is equipped with the internet of things (IoT) which can control the use of water pumps in the fields with our android devices wherever we are, this makes it easy for farmers to apply them, then the water pumps also do not use electricity which makes this water pump not harmful to farmers, because in the past many farmers were electrocuted and died.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48219812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Developing a Digital Scales System using Internet of Things Technology on Indonesia Digital Farm 利用物联网技术在印尼数字农场开发数字秤系统

Scientific Journal of Informatics Pub Date : 2023-02-02 DOI: 10.15294/sji.v10i1.40956

Kusrini Kusrini, Banu Santoso, E. Pramono, Muhammad Koprawi, Jeki Kuswanto, Elik Hari Muktafin, Ichsan Wasiso

引用次数: 1

Simulation Study of Imbalanced Classification on High-Dimensional Gene Expression Data 高维基因表达数据的不平衡分类模拟研究

Scientific Journal of Informatics Pub Date : 2023-02-01 DOI: 10.15294/sji.v10i1.40589

Masithoh Yessi Rochayani, U. Sa’adah, A. Astuti

{"title":"Simulation Study of Imbalanced Classification on High-Dimensional Gene Expression Data","authors":"Masithoh Yessi Rochayani, U. Sa’adah, A. Astuti","doi":"10.15294/sji.v10i1.40589","DOIUrl":"https://doi.org/10.15294/sji.v10i1.40589","url":null,"abstract":"Purpose: Classification of gene expression helps study disease. However, it faces two obstacles: an imbalanced class and a high dimension. The motivation of this study is to examine the effectiveness of undersampling before feature selection on high-dimensional data with imbalanced classes.Methods: Least Absolute Shrinkage and Selection Operator (Lasso), which can select features, can handle high-dimensional data modeling. Random undersampling (RUS) can be used to deal with imbalanced classes. The Classification and Decision Tree (CART) algorithm is used to construct a classification model because it can produce an interpretable model. Thirty simulated datasets with varying imbalance ratios are used to test the proposed approaches, which are Lasso-CART and RUS-Lasso-CART. The simulated data are generated from parameters of real gene expression data.Results: The simulation study results show that when the minority class accounts for more than 25% of the observation size, the Lasso-CART method is appropriate. Meanwhile, RUS-Lasso-CART is effective when the minority class size is at least 20 observations.Novelty: The novelty of this simulation study is using the RUS-Lasso-CART hybrid method to address the classification problem of high-dimensional gene expression data with imbalanced classes.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45346009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentiment Analysis on Social Media Against Public Policy Using Multinomial Naive Bayes 基于多项朴素贝叶斯的社交媒体对公共政策的情绪分析

Scientific Journal of Informatics Pub Date : 2023-01-16 DOI: 10.15294/sji.v10i1.39952

W. B. Zulfikar, A. R. Atmadja, S. F. Pratama

{"title":"Sentiment Analysis on Social Media Against Public Policy Using Multinomial Naive Bayes","authors":"W. B. Zulfikar, A. R. Atmadja, S. F. Pratama","doi":"10.15294/sji.v10i1.39952","DOIUrl":"https://doi.org/10.15294/sji.v10i1.39952","url":null,"abstract":"Purpose: The purpose of this study is to analyze text documents from Twitter about public policies in handling COVID-19 that are currently or have been determined. The text documents are classified into positive and negative sentiments by using Multinomial Naive Bayes.Methods: In this research, CRISP-DM is used as a method for conducting sentiment analysis, starting from the business understanding process, data understanding, data preparation, modeling, and evaluation. Multinomial Naive Bayes has been applied in building classification based on text documents. The results of this study made a model that can be used in classifying texts with maximum accuracy.Result: The results of this research are focused on the model or pattern generated by the Multinomial Naive Bayes Algorithm. The classification results of social media users' tweets against the new normal policy obtained good results with an accuracy value of 90.25%. After classifying the tweets of social media users regarding the new normal policy, the results show that more than 70% agreed and supported the new normal policy.Novelty: This study resulted in how classification can be done with Multinomial Naive Bayes and this algorithm can work well in recognizing text sentiments that generate positive or negative opinions regarding public policies handling COVID-19. So, the research provided conclusions about the views of people around the world on new normal public policy.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42588300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Monthly Rainfall Prediction Using the Backpropagation Neural Network (BPNN) Algorithm in Maros Regency 基于反向传播神经网络(BPNN)算法的Maros reggency月降雨量预测

Scientific Journal of Informatics Pub Date : 2023-01-11 DOI: 10.15294/sji.v10i1.37982

M. A. T. Aslim, J. Jasruddin, P. Palloan, H. Helmi, M. Arsyad, Hari Triwibowo

{"title":"Monthly Rainfall Prediction Using the Backpropagation Neural Network (BPNN) Algorithm in Maros Regency","authors":"M. A. T. Aslim, J. Jasruddin, P. Palloan, H. Helmi, M. Arsyad, Hari Triwibowo","doi":"10.15294/sji.v10i1.37982","DOIUrl":"https://doi.org/10.15294/sji.v10i1.37982","url":null,"abstract":"Purpose: This study aims to identify the right combination of network architecture, learning rate, and epoch in making predictions at each rainfall post in Maros Regency. In addition, this study also predicts the monthly rainfall profile in 2021-2025 in Maros Regency.Methods: The method in this study is the backpropagation neural network algorithm to learn and predict the data. BPNN is one of the most commonly used non-linear methods in making predictions recently. The data used in this study is monthly rainfall data from 2000-2020 as training and testing data at four rainfall stations including BPP Batubassi, Staklim Maros, Stamet Hasanuddin, and BPP Tanralili.Result: The results showed that the combination of network architecture, learning rate, and epoch obtained at each rainfall post was different. The highest level of prediction accuracy was obtained on 5 layers rather than 3 or 4 layers of network architecture with prediction accuracy at each rainfall post respectively 76.91%, 72.47%, 75.24%, and 76.53%. The predictions of rainfall from 2021-2025 are following the monsoon rain pattern with the highest rainfall in January 2025 of 964.1 mm, while the largest annual rainfall is obtained in 2023 with a total of 3359.6 mm.Novelty: In this study, various combinations of network architecture parameters consisting of learning rate, epoch, and architecture at each rainfall post obtained different results. Particularly in the Maros Regency, the combination that is most suitable for use in predicting monthly rainfall at the Batubassi BPP post is learning rate 0.7, epoch 50000, and network architecture 11-6-10-7-5, at Staklim Maros post is learning rate 0.5, epoch 50000, and network architecture 11-5-9-10-5, at Stamet Hasanuddin post is learning rate 0.8, epoch 20000, and network architecture 11-5-8-6-5, and at BPP Tanralili post is learning rate 0.5, epoch 10000, and 11-5-9-9-5 network architecture.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45496576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Software Effort Estimation Using Logarithmic Fuzzy Preference Programming and Least Squares Support Vector Machines 基于对数模糊偏好规划和最小二乘支持向量机的软件工作量估算

Scientific Journal of Informatics Pub Date : 2022-12-26 DOI: 10.15294/sji.v10i1.39865

Adnan Purwanto, L. Manik

{"title":"Software Effort Estimation Using Logarithmic Fuzzy Preference Programming and Least Squares Support Vector Machines","authors":"Adnan Purwanto, L. Manik","doi":"10.15294/sji.v10i1.39865","DOIUrl":"https://doi.org/10.15294/sji.v10i1.39865","url":null,"abstract":"Purpose: Effort Estimation is a process by which one can predict the development time and cost to develop a software process or product. Many approaches have been tried to predict this probabilistic process accurately, but no single technique has been consistently successful. There have been many studies on software effort estimation using Fuzzy or Machine Learning. For this reason, this study aims to combine Fuzzy and Machine Learning and get better results.Methods: Various methods and combinations have been carried out in previous research, this research tries to combine Fuzzy and Machine Learning methods, namely Logarithmic Fuzzy Preference Programming (LFPP) and Least Squares Support Vector Machines Machine (LSSVM). LFPP is used to recalculate the cost driver weights and generate Effort Adjustment Point (EAP). The EAP and Lines of Code values are then entered as input for LSSVM. The output results are then measured using the Mean Magnitude of Relative Error (MMRE) and Root-Mean-Square Error (RMSE). In this study, COCOMO and NASA datasets were used.Result: The results obtained are MMRE of 0.015019 and RMSE of 1.703092 on the COCOMO dataset, while on the NASA dataset the results of MMRE are 0.007324 and RMSE are 6.037986. Then 100% of the prediction results meet the 1% range of actual effort on the COCOMO dataset, while on the NASA dataset, the results show that 89,475 meet the 1% range of actual effort and 100% meet the 5% range of actual effort. The results of this study also show a better level of accuracy than using the COCOMO Intermediate method.Novelty: This study uses a combination of LFPP and LSSVM, which is an improvement from previous studies that used a combination of FAHP and LSSVM. The method used is also different where LFPP produces better output than FAHP and all data in the dataset is used for training and testing, whereas in previous research it only used a small part of the data.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44767273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Classification SARS-CoV-2 Disease based on CT-Scan Image Using Convolutional Neural Network 基于CT扫描图像的卷积神经网络对严重急性呼吸系统综合征冠状病毒2型疾病的分类

Scientific Journal of Informatics Pub Date : 2022-11-30 DOI: 10.15294/sji.v9i2.36583

Kelvin Leonardi Kohsasih, B. Hayadi

{"title":"Classification SARS-CoV-2 Disease based on CT-Scan Image Using Convolutional Neural Network","authors":"Kelvin Leonardi Kohsasih, B. Hayadi","doi":"10.15294/sji.v9i2.36583","DOIUrl":"https://doi.org/10.15294/sji.v9i2.36583","url":null,"abstract":"Purpose: Convolutional Neural Network (CNN) is one of the most popular and widely used deep learning algorithms. These algorithms are commonly used in various applications, including image processing in medical and digital forensics, speech recognition, and other academic disciplines. SARS-CoV-2 (COVID-19) is a disease that first appeared in Wuhan, China, and has symptoms similar to pneumonia. This study aims to classify the covid-19 virus by proposing a deep learning model to prevent infection rates.Methods: The dataset used in this study is a public dataset originating from a hospital in Sao Paulo, Brazil. The data images consisted of 1252 infected with covid and 1230 data classified as non-covid but have other lung diseases. The classification method proposed in this research is a CNN model based on Resnet 50.Result: The experimental results show that the proposed Resnet 50-based convolutional neural network model works well in classifying SARS-CoV-2 disease using CT-Scan images. Our proposed model obtains 95% accuracy, precision, recall, and f1 values on the Epoch 500.Novelty: In this experiment, we utilized the Resnet50-based CNN model to classify the SARS-CoV-2 (COVID-19) disease using CT-Scan images and got good performance.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46887630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Customer Segmentation Using the Integration of the Recency Frequency Monetary Model and the K-Means Cluster Algorithm 基于频率货币模型和k均值聚类算法的客户细分

Scientific Journal of Informatics Pub Date : 2022-11-30 DOI: 10.15294/sji.v9i2.39437

A. Alamsyah, P. Prasetyo, S. Sunyoto, S. H. Bintari, Danang Dwi Saputro, Shohihatur Rohman, Rizka Nur Pratama

{"title":"Customer Segmentation Using the Integration of the Recency Frequency Monetary Model and the K-Means Cluster Algorithm","authors":"A. Alamsyah, P. Prasetyo, S. Sunyoto, S. H. Bintari, Danang Dwi Saputro, Shohihatur Rohman, Rizka Nur Pratama","doi":"10.15294/sji.v9i2.39437","DOIUrl":"https://doi.org/10.15294/sji.v9i2.39437","url":null,"abstract":"Purpose: This research aims to do customer segmentation in retail companies by implementing the Recency Frequency Monetary (RFM) K-Means cluster model and algorithm optimized by the Elbow method.Methods: This study uses several methods. The RFM model method was chosen to segment customers because it is one of the optimal methods for segmenting customers. The K-Means cluster algorithm method was chosen because it is easy to interpret, implement, fast in convergence, and adapt, but lacks sensitivity to the initial partitioning of the number of clusters. To help classify each category of customers and know the level of loyalty, they use a combination of the RFM model and the K-Means method. The Elbow method is used to improve the performance of the K-Means algorithm by correcting the weakness of the K-Means algorithm, which helps to choose the optimal k value to be used when clustering.Result: This research produces customer segmentation 3 clusters with a Sum of Square Error (SSE) value of 25,829.39 and a Callinski-Harabaz Index (CHI) value of 36,625.89. The SSE and CHI values are the largest ones, so they are the optimal cluster values.Novelty: The application of the integrated RFM model and the K-Means cluster algorithm optimized by the Elbow method can be used as a method for customer segmentation.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47530511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1