bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of Data Mining Algorithms Using Artificial Neural Networks (ANN) and Naive Bayes for Preterm Birth Prediction
Authors: Puspitasari Diah , Ramanda Kresna , Supriyatna Adi , Wahyudi Mochamad , Sikumbang Erma Delima , Sukmana Sulaeman Hadi 
Published: November 2020

Abstract
Premature birth is still a big problem in Indonesia, in general, 15 million babies are born prematurely every year, more than 1 million babies die from complications due to premature birth. The main purpose of this study is to compare the Artificial Neural Network and Naive Bayes datamining algorithm models to predict preterm birth so as to obtain clinical evidence in preterm birth long before confinement so that sudden preterm birth can be converted to normal nativity. The model proposed in research on the prediction of preterm birth is by applying an Artificial Neural Network (ANN) algorithm and Naive Bayes algorithm. Where the two algorithms will be compared the level of accuracy and the value of the AUC against the prediction of preterm birth The results obtained that the prediction of preterm birth using the Artificial Neural Network (ANN) algorithm produces an accuracy value of 90.67% and an ROC value of 0.954. While the Naive Bayes algorithm produces an accuracy value of 84.53% and an ROC value of 0.929. For this reason, it can be concluded that the Artificial Neural Network (ANN) algorithm has a superior accuracy of 6.14% and 0.025 for its ROC value in predicting preterm birth.
Sentiment Analysis Review Of Smartphones With Artificial Intelligent Camera Technology Using Naive Bayes and n-gram Character Selection
Authors: Aulianita R , Utami LA , Musyaffa N , Wijaya G , Mukhayaroh A , Yoraeni A 
Published: November 2020

Abstract
Mobile has become a basic necessity at this time. Everyone certainly has a cellphone according to their daily needs. To capture connections and carry out various activities with just one hand. The object of this research is a review of smartphones that have the best artificial intelligent cameras. Data processing methods used in research using the Naïve Bayes algorithm. Naïve Bayes is known as one of the methods with the best classification accuracy results for text mining. The research objective is to facilitate customers who will buy a smartphone with the best AI camera without having to read product reviews. So that it can see based on the classification of positive text and label negative text classification. In this study, n-gram is used as a character selector to provide better accuracy results. Based on the results of research conducted, the accuracy of Naïve Bayes results is 72.00%, then Naïve Bayes with n-gram selection accuracy is N-gram = 2, 72.00% accuracy results, n-gram = 3, 75.00% accuracy results, and n-gram = 4 accuracy results 74.50%. In this study, carried out 10 times the experiment to measure the increased accuracy of the addition of n grams. Thus concluding that the application of the n-gram character can increase the accuracy of the Naïve Bayes algorithm.
Improving The Effectiveness of Classification Using The Data Level Approach and Feature Selection Techniques in Online Shoppers Purchasing Intention Prediction
Authors: Kurniawan I , Abdussomad  , Akbar M F , Saepudin D F , Azis M S , Tabrani M 
Published: November 2020

Abstract
Online shopping is a form of trading using electronic devices that allows consumers to buy goods or services from sellers via the internet. Other names for these activities are: e-web-shop, e-shop, e-shop, internet shop, web-shop, web-store, online shop, and virtual shop. An online store generates purchases of products or services at retailers or shopping centers, which are referred to as business-to-consumer (B2C) online shopping. n another process where a business buys from another business, it is called business-to-business (B2B). Nowadays online shopping has become more sophisticated with trading via mobile phones (m-commerce). Cellular phones have been optimized with an application to buy from online sites. In this study, we proposed a data level approach and feature selection techniques as a solution for the classification of imbalanced data. The imbalance class classification is one of the classic problems in the field of artificial intelligence, especially for classification in machine learning. Imbalanced data have been proven to reduce the performance of machine learning algorithms, where imbalance data means that the total data from each class is significantly different. The proposed method is evaluated using a dataset from the UCI repository and area under the curve (AUC) as the main evaluation. The results have shown that the proposed method produces good performance. (AUC¿ 0.8). Overall the second experiment outperformed and was better than the first and third experiments because the main evaluation in the unbalanced class classification is AUC. Therefore, it can be concluded that the proposed method produces optimal performance both for large scale data sets. Overall the second experiment outperformed and better than the first and third experiments, because the main evaluation in the unbalanced class classification was AUC.
Comparison of Naive Bayes Algorithm and Support Vector Machine using PSO Feature Selection for Sentiment Analysis on E-Wallet Review
Authors: Putri Dwi Andini , Kristiyanti Dinar Ajeng , Indrayuni Elly , Nurhadi Acmad , Hadinata Denda Rinaldi 
Published: November 2020

Abstract
Cashless payment habits have been widely applied to the transportation system, restaurants and shops in the mall area. So, it is normal if the growth of mobile payment services is currently very rapid. The ease of doing transactions and promotional offers in the form of points and cashback in digital wallet applications (e-wallets) is very beneficial for its users. One of the most popular e-wallets is OVO. With so many reviews about OVO customer opinions on social media, there has also been a lot of public opinion. These opinions can produce negative or positive statements. Sentiment analysis is the mining of opinions or text to classify opinions or user reviews, of a brand reviews, product reviews, or service reviews into the category of positive or negative opinion. The methods used in this research are Naive Bayes and SVM. Both of these algorithms are the best algorithms widely used in text classification research. However, both of these algorithms have weaknesses in several parameters. So, in this study Feature Selection is used to improve its performance. The evaluation was carried out using 10-fold cross validation. Measurement accuracy is measured by confusion matrix and ROC curves. This study uses 500 positive reviews and 500 negative reviews as data training. The results of this study indicate that the use of PSO-based Naive Bayes algorithm produces an accuracy value of 93.10 percent with an AUC value of 0.750. While the results of research from the PSO-based SVM algorithm are 91.30 percent with an AUC value of 0.970. Based on these results the accuracy value generated by the Naive Bayes algorithm is classified as Fair Classification and SVM is classified as Excellent Classification. The AUC value generated by the Naive Bayes algorithm is also smaller than SVM. Therefore, in this study found that SVM is the best algorithm in classifying text.
Determination Wart Treatment Method Using Data Mining Technique
Authors: Amalia Hilda , Yunita  , Pohan Achmad Baroqah , Puspita Ari , Lestari Ade Fitria , Retnasari Tri 
Published: November 2020

Abstract
Wart disease is a benign tumor that can grow on all parts of human skin. Although this wart disease belongs to a type of benign tumor, this wart disease can interfere with physical appearance and disturb the psychological sufferers if the infected part of the skin is the area around the face. This wart disease will also be very dangerous if there is infection in vital parts of the body such as the genitals. For treatment or treatment of wart harm, two methods are used: immunotherapy and cryotherapy. However, until now it is still unknown which treatment is better for patients with this wart disease. It has long been known that data mining is a method that is widely used in the health world to obtain valuable knowledge from medical data sets. Data Mining is used to classify and detect early diseases and choose the optimal type of treatment for warts. Data mining is able to find patterns and predict the trends of events. In this study four data mining methods will be used, namely the neural network, naive Bayes, SVM and K-NN. from the results of the study obtained the best method for processing the wart dataset is neural network
Identification of monogeneans parasite using gray level co-occurrence matrix and artificial neural network
Authors: Hikmatulloh  , Riana Dwiza , Hudin Jamal Maulana , Susilawati  , Wintana Dede , Hadianti Sri 
Published: November 2020

Abstract
flatworms that are parasitic and attack the skin and gills of fish are called monogenean parasites. In this study 3 monogenic parasites were used, namely grandis, liewi, and johorensis. GLCM (Grey Level Co-occurrence Matrix) used in digital image processing to obtain feature extraction on images. ANN (Artificial Neural Network) used to classification mongenean parasites based on features extracted. This research resulted overall accuracy of 86,67%. Studying monogenean parasites is needed because it is related to the health of fish that we need in our daily lives, so that this research can ease and assist the work of ecologists in classifying monogenean parasites more quickly and efficiently.
An Analysis and Measurement of Website Quality Using WebQual 4.0 and Importance Performance Analysis (IPA) Method (A Case Study of Kemiriamba Village Brebes)
Authors: Faqih Husni , Warjiyono  , Kuhon Fiola , Aji Sopian , Ardiansyah Angga , Fandhilah  
Published: November 2020

Abstract
Based on the Law of the Republic of Indonesia number 6/2016 regarding villages, it is said that regional governments are obliged to develop village information systems in implementing e-government towards good governance. In Indonesia, e-government continues to increase in number, but it is not accompanied by quality, including the Kemiriamba Village website. This study was aimed to measure the quality of the Kemiriamba Village website using the Webqual 4.0 method and Importance Performance Analysis (IPA) with 4 (four) instruments, namely Usability Quality, Information Quality, Service Interaction Quality and Visual Quality. This study is descriptive with a quantitative approach. The questionnaire data were 132 and were processed with SPSS software. The results of the analysis and measurement stated that the suitability level of the Kemiriamba Village website was 96.63%, and the average value of the gap was negative, which was -0.11, which meant that the performance level of the Kemiriamba Village website still did not meet user satisfaction and expectations, especially in Service Interaction Quality. The main priority that needs to be corrected and improved immediately is the website attribute; it should have a good reputation. Moreover, the questions, suggestions and complaints need to be processed as promised. The results of this study suggested the Kemiriamba Village website to immediately make improvements and development in order to become a qualified website towards good governance.
Causal Loop Diagram (CLD) Model In Planning A Sustainable Smart Sharia Tourism
Authors: Husain  , Zarlis Muhammad , Mawengkang Herman , Efendi Syahril 
Published: November 2020

Abstract
The development of sustainable tourism such as dynamic and complex smart sharia tourism consists of many interrelated and diverse components of stakeholders, which each has different management objectives, which can trigger unexpected conflicts among stakeholders. Regarding the dynamic and complex challenges in the field of developing sustainable tourism, the ability of identidying is really needed. For this reason, it is necessary to see further what are the factors that influence the plan for implementing sharia tourism intelligently and the strategies needed and analyze the relationship of dynamic causes of these factors using approachment methods which use the Causal Loop Diagram system ( CLD). The purpose of this research is to get a solution to overcome conflicts among stakeholders through approachment system with the Causal Loop Diagram (CLD) model. The results of this CLD Model can identify the root causes of complex problems and the effects of a sustainable tourism development system so that it can be used to develop and test alternative management policies in making smart sharia tourism planning decisions.
Usability Evaluation of the Website Services Using the WEBUSE Method (A Case Study: covid19.go.id)
Authors: Aziz Faruq , Irmawati  , Riana Dwiza , Mulyanto Joko Dwi , Nurrahman Dede , Tabrani Muhamad 
Published: November 2020

Abstract
The purpose of this study is to analyze the site usability evaluation system using the WEBUSE method on one of the information sites about COVID-19 namely the website covid19.go.id. The basic analysis was carried out on 24 usability criteria classified into 5 usability categories, namely: content, organization, and readability, navigation and links, user interface design, performance and effectiveness, educational purpose. Each category deals with one aspect of usability. Data from respondents’ answers were analyzed using the WEBUSE method to get the usage point and usability level of the website that has been studied. This research produces a usability evaluation information system that provides an online evaluation questionnaire and this analysis is useful to present high-quality system information and user satisfaction aspects of the website covid19.go.id. The case study on the website covid19.go.id, shows that the level of usability and overall level of user satisfaction is good and acceptable for the general public from all walks of life. And in terms of satisfaction in the user interface design category, it is possible that some people are still uncomfortable with the look of the website.
E-Learning for English for Business-Based Podcast: One of Learning Solutions Amid the Pandemic of COVID-19.
Authors: Aditya Nalendra Aloysius Rangga , Rahayuningsih Retno , Rosalinah Yanti , Subroto Ibnu , Wibowo Ary Iswanto , Nelfianti Fera 
Published: November 2020

Abstract
This research used qualitative methods of descriptive where information was required directly and where time and resources were limited. It was designed to investigate whether e-learning podcast-based could be used in learning to listen in English for students of the University of Bina Sarana Informatika Management Study program. This research used samples that are 6 classes as a source of research data with the 2nd-semester students who came from Management study program. The number of 1 average class consisted of 20-25 students. Researchers used the Android-based Anchor Application as a podcast maker as well as Google Classroom as an E-Learning learning medium. To collect data, researchers used observations, interviews, and documentation. Interviews were conducted by giving 25 questions through online based on theoretical approaches related to this study with 90 sample data. Researchers used test instruments with pre-test and post-test as research instruments. This research was focused on the use of ICT in the learning process by way of e-learning Podcasts based. The novelty of this research is this study used a different target and numbers of the target from the previous studies, namely Management Program Students who only get Basic English teaching during lectures. As a result of this research was the use of podcasts more effective than e-learning without using the media podcast, characterized by a grade average result of 76.05 after Post tests were done.
Determination of the Real Contents of Olive Oil in Blend Oils by near Infrared Spectroscopy
Authors: Cai Zihao , Xu Qiang , Yang Xingxing , Hu Zhui , Zheng Xiao , Zhang Jiafan 
Published: November 2020

Abstract
Aiming at the determination of real contents of olive oil in blend oils, near infrared spectroscopy and support vector machine were combined to establish regression models for the determination of olive oil contents. The data were fused on the feature level with canonical correlation analysis to improve the prediction performance of the models. The results showed that the SVM regression models could effectively predict the real contents of olive oil in 135 samples containing 2%-20% olive oil. Among these models, the highest correlation coefficient R2 was 99.74%, and the root mean square error of the prediction set was 0.08. In addition, the prediction performances of some regression models were deteriorated after the curves were smoothed with the Savitzky-Golay method. But, the prediction performances were improved in the models established with data fused with the canonical correlation analysis approach.
Reliability Sensitivity Analysis of Vehicle CAN Bus Message Transmission Based on Second-moment Method
Authors: Liu Yu , Yin Yue 
Published: November 2020

Abstract
CAN bus, as one of the most successful on-board network systems, is widely used in modern automobiles. The main function of CAN bus is to realize timely and accurate message transmission, so higher and higher requirements are put forward for the reliability of message transmission. In this paper, a typical CAN bus message transmission model was selected for analysis, and the reliability sensitivity of the bus message transmission was analyzed and calculated based on the reliability second-moment theory. The results were consistent with the qualitative analysis, and the reliability sensitivity calculation of the second-moment method is verified. The reliability sensitivity analysis of CAN bus message transmission by this method will provide a reasonable theoretical support for the further study of message transmission reliability in CAN bus system.
Crank Call Detection Models Based on Call Data of Telephone Subscribers
Authors: Xie Wenbo , Liu Zhen , Fu Yan 
Published: November 2020

Abstract
Data explosion makes our life much more convenient than ever. For example, people always get timely news from Apps, and get interesting recommendations when shopping online. However, these conveniences come by probing people’s private data, such as account information, search history, and chat records. Thus, if the management of data is not as secure as it supposes to be, many problems, e.g., crank calls, will arise and cause big troubles in people’s lives. Although many security Apps have been developed to help mobile phone users stay away from crank calls, the users need to update Apps frequently due to their user-labels-based blacklist. These feedback-based systems depend on a large number of user actions that have significant lag. In this paper, we propose two crank call detection models to help operators mark crank callers based on the call data of telephone subscribers, in which the features are organized by different ways. One is a composited classification model, and the other is a convolutional neural network model based on the time-sliced data. The experiments show that the model applying convolutional neural network gets better accuracies in detecting crank calls.
Design and Implementation of Welding Procedure Qualification Expert System
Authors: Wang Ren , He Jianying , Ma Mingju 
Published: November 2020

Abstract
Welding process evaluation is an important link to ensure the quality of welding engineering and determine the welding process specification. It is also the main procedure to evaluate the welder’s examination results. In the traditional way of processing WPS documents, the test results are usually judged manually by experts based on experience. However, the evaluation of welding process is often very complex, involving the base material, sample type, process parameters and a large number of international standards. Therefore, how to utilize the software engineering method and experience, make the welding process evaluation program accurate and efficient information, make the welding process evaluation program easier and more efficient, is very important to the whole field of welding.
Resource Allocation Based on Chaotic Ant Colony Algorithm in Self-organizing Wireless Sensor Network
Authors: Tian Min , Bai Jinbin 
Published: November 2020

Abstract
A resource allocation scheme based on chaotic ant colony algorithm in self-organizing wireless sensor network is proposed, and corresponding chaotic sequence generator is designed in this paper. Simulations are conducted to compare the proposed method with conventional genetic algorithm and dynamic programming algorithm under self-organizing wireless sensor network environment. Results show that compared with the scheme based on dynamic allocation algorithm and conventional genetic algorithm, the scheme based on chaotic ant colony algorithm has higher target detection rate and lower power consumption, which also extends the networks lifetime.
Research on Spam Filtering Technology Based on New Mutual Information Feature Selection Algorithm
Authors: Wang Kunfu , Mao Wanfeng , Feng Wei , Wang Hui 
Published: November 2020

Abstract
Aiming at the deficiency of traditional mutual information algorithm in feature selection, this paper proposes a weighted naive Bayesian algorithm based on improved mutual information, called imi-wnb algorithm. In the feature selection stage, the word frequency factor and the difference factor between classes are introduced to improve the traditional mutual information algorithm to achieve feature dimension reduction. In the process of classification, the value of IMI is introduced to weight the attributes of naive Bayes algorithm, which partly eliminates the influence of conditional independence assumption of naive Bayes algorithm on classification, and improves the efficiency and stability of spam classification.
Recommendation Algorithm of Crowdfunding Platform Based on Collaborative Filtering
Authors: Wang Bixi , Wu Wenbin , Zheng Wenfeng , Liu Yan , Yin Lirong 
Published: November 2020

Abstract
With the continuous progress of Internet technology, the crowdfunding platform has become a new way of network financing. While the generated data keeps increasing, its benefit does not increase in a proportional way, resulting in the “information overload” phenomenon. The personalized recommendation system can solve this problem by mining users’ interests and preferences from a large amount of data. It has achieved success in many fields. This paper applies machine learning algorithm to build a recommendation system based on collaborative filtering. The designed personalized recommendation algorithm can provide accurate and rapid personalized recommendation services, which is convenient for users and conducive to the development of the crowdfunding platform. In addition, this paper uses the data from the crowdfunding platform in practice to complete the performance verification of the algorithm.