Pub Date : 2022-07-27DOI: 10.1109/ICISIT54091.2022.9872926
Arif Ridho Lubis, S. Prayudani, M. Lubis, Okvi Nugroho
Coronavirus Disease of 2019 began in Wuhan in December 2019 and it was declared as a global pandemic by WHO. Until January 2021, it affected all of human activities on earth i.e., experiencing many obstacles from restrictions on activities, closure of tourist attractions to restrictions on face-to-face learning activities in schools or universities. Due to the policy of providing a broad influence on the community with various comments through social media, many twitter users make tweets containing positive and negative comments leading to statements about online learning or daring. The problem is that they contain so many different words, abbreviations, informal language, and symbols, creating difficulties to choose which words or groups of words that can produce positive or negative statements. K-Nearest Neighbors algorithm is used to classify positive and negative tweet data, the results were AUC for class 0: 0.754, 1: 0.635, 2: 0.721 and with a precision classification score of 0.86, recall is 0.85 so that the results of the classification of negative and positive sentences on the online learning tweet data were ROC-AUC of 0. 853 and the accuracy value of 0.885.
{"title":"Sentiment Analysis on Online Learning During the Covid-19 Pandemic Based on Opinions on Twitter using KNN Method","authors":"Arif Ridho Lubis, S. Prayudani, M. Lubis, Okvi Nugroho","doi":"10.1109/ICISIT54091.2022.9872926","DOIUrl":"https://doi.org/10.1109/ICISIT54091.2022.9872926","url":null,"abstract":"Coronavirus Disease of 2019 began in Wuhan in December 2019 and it was declared as a global pandemic by WHO. Until January 2021, it affected all of human activities on earth i.e., experiencing many obstacles from restrictions on activities, closure of tourist attractions to restrictions on face-to-face learning activities in schools or universities. Due to the policy of providing a broad influence on the community with various comments through social media, many twitter users make tweets containing positive and negative comments leading to statements about online learning or daring. The problem is that they contain so many different words, abbreviations, informal language, and symbols, creating difficulties to choose which words or groups of words that can produce positive or negative statements. K-Nearest Neighbors algorithm is used to classify positive and negative tweet data, the results were AUC for class 0: 0.754, 1: 0.635, 2: 0.721 and with a precision classification score of 0.86, recall is 0.85 so that the results of the classification of negative and positive sentences on the online learning tweet data were ROC-AUC of 0. 853 and the accuracy value of 0.885.","PeriodicalId":214014,"journal":{"name":"2022 1st International Conference on Information System & Information Technology (ICISIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133707779","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 : 2022-07-27DOI: 10.1109/ICISIT54091.2022.9872808
Ita Sulistiani, Windu Wulandari, Fathia Dwi Astuti, Widodo
Breast cancer is the second deadliest cancer after lung cancer. In 2021, ASCO-American Society of Clinical Oncology states that female invasive breast cancer increased by half a percent from 2008 to 2017. Breast cancer is induced by a misspelling of a cell, which causes the cell to become uncontrollable. If the problem is not treated soon within a few months, a large number of cells containing the wrong instructions can be detected as cancer. Machine learning has been widely used for developing breast cancer prediction models. Unfortunately, the problem of imbalanced datasets tends to have little to no attention in previous research using machine learning. This research aimed to develop breast cancer prediction models using Random Forest and Gaussian Naïve Bayes Classifier. Borderline Synthetic Minority Oversampling Technique (BSM) is applied to handle the imbalanced dataset problem; meanwhile, machine learning algorithms such as Random Forest and Gaussian Naïve Bayes algorithms were used to build the prediction models. Using UCI Machine Learning Wisconsin Breast Cancer Dataset (WBCD), the combination of BSM and Random Forest algorithm showed the highest recall score, approximately around 99.8%. Meanwhile, the BSM and Gaussian Naïve Bayes Classifier combination provided the lowest recall score among generated models, 78.2%.
{"title":"Breast Cancer Prediction Using Random Forest and Gaussian Naïve Bayes Algorithms","authors":"Ita Sulistiani, Windu Wulandari, Fathia Dwi Astuti, Widodo","doi":"10.1109/ICISIT54091.2022.9872808","DOIUrl":"https://doi.org/10.1109/ICISIT54091.2022.9872808","url":null,"abstract":"Breast cancer is the second deadliest cancer after lung cancer. In 2021, ASCO-American Society of Clinical Oncology states that female invasive breast cancer increased by half a percent from 2008 to 2017. Breast cancer is induced by a misspelling of a cell, which causes the cell to become uncontrollable. If the problem is not treated soon within a few months, a large number of cells containing the wrong instructions can be detected as cancer. Machine learning has been widely used for developing breast cancer prediction models. Unfortunately, the problem of imbalanced datasets tends to have little to no attention in previous research using machine learning. This research aimed to develop breast cancer prediction models using Random Forest and Gaussian Naïve Bayes Classifier. Borderline Synthetic Minority Oversampling Technique (BSM) is applied to handle the imbalanced dataset problem; meanwhile, machine learning algorithms such as Random Forest and Gaussian Naïve Bayes algorithms were used to build the prediction models. Using UCI Machine Learning Wisconsin Breast Cancer Dataset (WBCD), the combination of BSM and Random Forest algorithm showed the highest recall score, approximately around 99.8%. Meanwhile, the BSM and Gaussian Naïve Bayes Classifier combination provided the lowest recall score among generated models, 78.2%.","PeriodicalId":214014,"journal":{"name":"2022 1st International Conference on Information System & Information Technology (ICISIT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130779995","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 : 2022-07-27DOI: 10.1109/ICISIT54091.2022.9872666
Baiq Yuniar Yustiarini, Favian Dewanta, H. Nuha
Delivering information from Internet of Things (IoT) devices to a cloud server possesses several security issues, e.g. information eavesdropping, modification, and theft. Therefore, communication between IoT devices and the cloud server should be protected by encryption methods. However, there are few encryption techniques options that are suitable for the need for lightweight communication as demanded by the IoT devices. Due to these circumstances, the NSA launched an encryption algorithm for IoT named Simon and Speck, which are maximally efficient while still providing the advertised level of security, as determined by the key size. This study aims to test and compare the Simon-Speck and AES encryption algorithms and their effect on networking performance on IoT devices. The parameters in this test are delay, throughput, the efficiency of memory usage from the encryption algorithm, and the value of the avalanche effect. Experimental results show that the Speck algorithm outperforms the Simon and the AES algorithms in terms of communication delay and memory usage. Regarding the avalanche effect values, the Simon algorithm possesses the highest avalanche effect value on average against the Speck and the AES algorithms.
{"title":"A Comparative Method for Securing Internet of Things (IoT) Devices: AES vs Simon-Speck Encryptions","authors":"Baiq Yuniar Yustiarini, Favian Dewanta, H. Nuha","doi":"10.1109/ICISIT54091.2022.9872666","DOIUrl":"https://doi.org/10.1109/ICISIT54091.2022.9872666","url":null,"abstract":"Delivering information from Internet of Things (IoT) devices to a cloud server possesses several security issues, e.g. information eavesdropping, modification, and theft. Therefore, communication between IoT devices and the cloud server should be protected by encryption methods. However, there are few encryption techniques options that are suitable for the need for lightweight communication as demanded by the IoT devices. Due to these circumstances, the NSA launched an encryption algorithm for IoT named Simon and Speck, which are maximally efficient while still providing the advertised level of security, as determined by the key size. This study aims to test and compare the Simon-Speck and AES encryption algorithms and their effect on networking performance on IoT devices. The parameters in this test are delay, throughput, the efficiency of memory usage from the encryption algorithm, and the value of the avalanche effect. Experimental results show that the Speck algorithm outperforms the Simon and the AES algorithms in terms of communication delay and memory usage. Regarding the avalanche effect values, the Simon algorithm possesses the highest avalanche effect value on average against the Speck and the AES algorithms.","PeriodicalId":214014,"journal":{"name":"2022 1st International Conference on Information System & Information Technology (ICISIT)","volume":"329 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134129080","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 : 2022-07-27DOI: 10.1109/ICISIT54091.2022.9872889
M. T. Anwar, Lucky Heriyanto, Denny Rianditha Arief Permana, Gita Mustika Rahmah
Demand forecasting is an important task in every business including car manufacturing. The high initial production cost of cars places even more importance on demand forecasting especially for a specific type of car such as the Low-Cost Green Car (LCGC). Within its current 8 years journey, the number of demands for LCGC cars has experienced some fluctuation which makes the need for accurate demand forecasting even more important. This research aims to accurately predict the demand for LCGC cars in Indonesia using the Long Short-Term Memory (LSTM) method. However, it is difficult to find the best parameter settings for a neural network-based model such as LSTM. Therefore, this research will explore the effect of different parameter settings on the model accuracy. The data used in this research is the number of monthly domestic LCGC car sales from September 2013 to December 2021 obtained from the Association of Indonesian Automotive Industries (GAIKINDO). The experiments were conducted using the Tensorflow package in Python and were evaluated for their performance using MAE and MAPE. The experimental results showed that the LSTM model can accurately predict car sales/demands with an MAE of up to 977.6 and MAPE of 6.8% (accuracy 93.2%).
{"title":"Optimizing LSTM Model for Low-Cost Green Car Demand Forecasting","authors":"M. T. Anwar, Lucky Heriyanto, Denny Rianditha Arief Permana, Gita Mustika Rahmah","doi":"10.1109/ICISIT54091.2022.9872889","DOIUrl":"https://doi.org/10.1109/ICISIT54091.2022.9872889","url":null,"abstract":"Demand forecasting is an important task in every business including car manufacturing. The high initial production cost of cars places even more importance on demand forecasting especially for a specific type of car such as the Low-Cost Green Car (LCGC). Within its current 8 years journey, the number of demands for LCGC cars has experienced some fluctuation which makes the need for accurate demand forecasting even more important. This research aims to accurately predict the demand for LCGC cars in Indonesia using the Long Short-Term Memory (LSTM) method. However, it is difficult to find the best parameter settings for a neural network-based model such as LSTM. Therefore, this research will explore the effect of different parameter settings on the model accuracy. The data used in this research is the number of monthly domestic LCGC car sales from September 2013 to December 2021 obtained from the Association of Indonesian Automotive Industries (GAIKINDO). The experiments were conducted using the Tensorflow package in Python and were evaluated for their performance using MAE and MAPE. The experimental results showed that the LSTM model can accurately predict car sales/demands with an MAE of up to 977.6 and MAPE of 6.8% (accuracy 93.2%).","PeriodicalId":214014,"journal":{"name":"2022 1st International Conference on Information System & Information Technology (ICISIT)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116187345","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 : 2022-07-27DOI: 10.1109/ICISIT54091.2022.9873043
Muhammad Akmal Juniawan, Novialdi Ashari, Rizdiani Tri Prastiti, Suci Inayah, F. Gunawan, P. Putra
An ERP (Enterprise Resource Planning) system is an information technology (IT) solution that allows, manages business processes, improves the efficiency of management decisions and innovative business operations of telecommunication companies. The major goal of this research is to identify crucial success factors and assess their impact on ERP implementation in the telecommunications industry, specifically at ABC Telco Company. The information was gathered using a semi-structured interview and a Pareto Analysis questionnaire survey. Based on the results, The one CSFs from the interview result is included as the “vital few” CSFs that occupy a significant portion (80%) of the total percentage of occurrences based on survey result which is Effectiveness of Project Leader that mean this CSFs must be attentive and focused by ERP practitioners in the Telecommunication industry along with the others most influence and “vital few” CSFs and they must ensure the remaining CSFs should not be ignored. Furthermore, it is recommended to assign the capable project leader to make a strategy for implementation of the ERP and determine what approach the team should take to achieve that success of implementation. Once these goals are put in place and enhanced, the other CSFs should be consolidated.
{"title":"Exploring Critical Success Factors for Enterprise Resource Planning Implementation: A Telecommunication Company Viewpoint","authors":"Muhammad Akmal Juniawan, Novialdi Ashari, Rizdiani Tri Prastiti, Suci Inayah, F. Gunawan, P. Putra","doi":"10.1109/ICISIT54091.2022.9873043","DOIUrl":"https://doi.org/10.1109/ICISIT54091.2022.9873043","url":null,"abstract":"An ERP (Enterprise Resource Planning) system is an information technology (IT) solution that allows, manages business processes, improves the efficiency of management decisions and innovative business operations of telecommunication companies. The major goal of this research is to identify crucial success factors and assess their impact on ERP implementation in the telecommunications industry, specifically at ABC Telco Company. The information was gathered using a semi-structured interview and a Pareto Analysis questionnaire survey. Based on the results, The one CSFs from the interview result is included as the “vital few” CSFs that occupy a significant portion (80%) of the total percentage of occurrences based on survey result which is Effectiveness of Project Leader that mean this CSFs must be attentive and focused by ERP practitioners in the Telecommunication industry along with the others most influence and “vital few” CSFs and they must ensure the remaining CSFs should not be ignored. Furthermore, it is recommended to assign the capable project leader to make a strategy for implementation of the ERP and determine what approach the team should take to achieve that success of implementation. Once these goals are put in place and enhanced, the other CSFs should be consolidated.","PeriodicalId":214014,"journal":{"name":"2022 1st International Conference on Information System & Information Technology (ICISIT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122688426","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 : 2022-07-27DOI: 10.1109/ICISIT54091.2022.9873082
S. Rizal, N. K. C. Pratiwi, N. Ibrahim, Nathaniel Syalomta, Muhammad Ikhwan Khalid Nasution, Indah Mutiah Utami Mz, Deva Aulia Putri Oktavia
Nutrient deficiency often occurs in rice plants, thus affecting the level of production and quality of rice. Nutrient deficiency, in general, can be seen from the color and shape of sick leaves; therefore, it can be detected early to reduce the symptoms of nutritional deficiency in rice plants. This study classifies the symptoms of nutritional deficiency in rice plants using the Convolutional Neural Network (CNN) with ResNet 50 and ResNet 152 architectures. There are 1156 images with datasets sourced from Kaggle, divided into nitrogen (N) deficiency and Phosphorus(P) deficiency. And Potassium (K) deficiency. The dataset augmentation process used oversampling techniques to balance the data. The best results were obtained from the ResNet 50 architecture with accuracy and validation values yielding 98% and testing values 97%
{"title":"Classification Of Nutrition Deficiency In Rice Plant Using CNN","authors":"S. Rizal, N. K. C. Pratiwi, N. Ibrahim, Nathaniel Syalomta, Muhammad Ikhwan Khalid Nasution, Indah Mutiah Utami Mz, Deva Aulia Putri Oktavia","doi":"10.1109/ICISIT54091.2022.9873082","DOIUrl":"https://doi.org/10.1109/ICISIT54091.2022.9873082","url":null,"abstract":"Nutrient deficiency often occurs in rice plants, thus affecting the level of production and quality of rice. Nutrient deficiency, in general, can be seen from the color and shape of sick leaves; therefore, it can be detected early to reduce the symptoms of nutritional deficiency in rice plants. This study classifies the symptoms of nutritional deficiency in rice plants using the Convolutional Neural Network (CNN) with ResNet 50 and ResNet 152 architectures. There are 1156 images with datasets sourced from Kaggle, divided into nitrogen (N) deficiency and Phosphorus(P) deficiency. And Potassium (K) deficiency. The dataset augmentation process used oversampling techniques to balance the data. The best results were obtained from the ResNet 50 architecture with accuracy and validation values yielding 98% and testing values 97%","PeriodicalId":214014,"journal":{"name":"2022 1st International Conference on Information System & Information Technology (ICISIT)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132442169","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 : 2022-07-27DOI: 10.1109/ICISIT54091.2022.9873041
J. Hendry, Fakih Irsyadi, Nur Rohman Rosyid, Ilham Riska Subekti, Andri Khoirul Huda
The multirotor drones have been used extensively in modern farming. This device can help to tackle many tasks that is limited to human such as planting, watering, and so on. Hence, it can save farmer’s time and energy. Drones mostly use battery or supercapacitor as their power supply. It means that they cannot fly forever without charged. Battery recharging takes time. Hence, some researches have already conducted to make the battery charging easy to do the drone can continue to do the tasks without the need to stop the operation. In this research, we propose a concept model for battery recharge without stopping the drone from operation. It converts sounds from multirotor drone’s motor into current by using piezoelectric sensor. Based on calculation and analysis on frequency spectrum, ideal fundamental frequency is 120 Hz that yields length of piezoelectric sensor’s resonator 11 cm.
{"title":"Piezoelectric-based Battery Charging on Multirotor Drone for Modern Farming: A Concept Model","authors":"J. Hendry, Fakih Irsyadi, Nur Rohman Rosyid, Ilham Riska Subekti, Andri Khoirul Huda","doi":"10.1109/ICISIT54091.2022.9873041","DOIUrl":"https://doi.org/10.1109/ICISIT54091.2022.9873041","url":null,"abstract":"The multirotor drones have been used extensively in modern farming. This device can help to tackle many tasks that is limited to human such as planting, watering, and so on. Hence, it can save farmer’s time and energy. Drones mostly use battery or supercapacitor as their power supply. It means that they cannot fly forever without charged. Battery recharging takes time. Hence, some researches have already conducted to make the battery charging easy to do the drone can continue to do the tasks without the need to stop the operation. In this research, we propose a concept model for battery recharge without stopping the drone from operation. It converts sounds from multirotor drone’s motor into current by using piezoelectric sensor. Based on calculation and analysis on frequency spectrum, ideal fundamental frequency is 120 Hz that yields length of piezoelectric sensor’s resonator 11 cm.","PeriodicalId":214014,"journal":{"name":"2022 1st International Conference on Information System & Information Technology (ICISIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130550902","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 : 2022-07-27DOI: 10.1109/ICISIT54091.2022.9872858
Arif Ridho Lubis, S. Prayudani, Y. Fatmi, Y. Y. Lase, Al-Khowarizmi
Classification in data mining is one technique in recognizing all types of data. Where data can be in the form of text, numeric, images and others. One of the superior classification techniques is the KNN algorithm. The KNN algorithm is a distance search using Euclidean distance. image data classification using the HOG process is needed to modify the KNN. The purpose of this paper is to classify patients with classifying skin cancer patients using the KNN method where the Histogram of Oriented Gradients (HOG) process is used to assist in extracting data for skin cancer patients, which consists of benign and malignant cancers. However, in this paper, the images included in this article are pictures of skin cancer sufferers, which consist of malignant and benign. The data obtained were 660 datasets of which 630 were used as training data and 30 were used as test data. The training and testing went well, this was shown by getting a MAPE of O.06705477%. So that the classification process can be accepted because it shows a small validity.
{"title":"In Image Classification of Skin Cancer Sufferers: Modification of K-Nearest Neighbor with Histogram of Oriented Gradients Approach","authors":"Arif Ridho Lubis, S. Prayudani, Y. Fatmi, Y. Y. Lase, Al-Khowarizmi","doi":"10.1109/ICISIT54091.2022.9872858","DOIUrl":"https://doi.org/10.1109/ICISIT54091.2022.9872858","url":null,"abstract":"Classification in data mining is one technique in recognizing all types of data. Where data can be in the form of text, numeric, images and others. One of the superior classification techniques is the KNN algorithm. The KNN algorithm is a distance search using Euclidean distance. image data classification using the HOG process is needed to modify the KNN. The purpose of this paper is to classify patients with classifying skin cancer patients using the KNN method where the Histogram of Oriented Gradients (HOG) process is used to assist in extracting data for skin cancer patients, which consists of benign and malignant cancers. However, in this paper, the images included in this article are pictures of skin cancer sufferers, which consist of malignant and benign. The data obtained were 660 datasets of which 630 were used as training data and 30 were used as test data. The training and testing went well, this was shown by getting a MAPE of O.06705477%. So that the classification process can be accepted because it shows a small validity.","PeriodicalId":214014,"journal":{"name":"2022 1st International Conference on Information System & Information Technology (ICISIT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126769740","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：