Reviewerly/exHarmony huggingface dataset:
PERCEPTIONS OF STUDENTS OF ENGLISH EDUCATION STUDY PROGRAM TOWARDS LMS IMPLEMENTATION DURING THE C0VID-19 PANDEMIC ERA

ABSTRACT&#x0D; &#x0D; This study aims to determine the perception of iEnglish Language Education students at STKIP AL MAKSUM Langkat towards online learning via Moodle during th Covid-19 pandemic. The qualitative descriptive method was carried out by distributing questionnaires using Google Form to 45 students of English Education STKIP AL MAKSUM Langkat. The questionnaire was distributed after completing the learning process for 3 months. The results of this study state that the use of the Moodle application provides a new experience in online learning activities, students can easily operate several features of the Moodle application, the learning process expects students to use the Moodle application, the Moodle application is one of e-learnin ithat can support learning activities online, the use of the Moodle application because classmates agree to use the Moodle application, and students can use the Moodle application easily in carrying out online learning activities.
THE EFFECT OF USING THIEVES AND CSR STRATEGIES TO IMPROVE READING COMPREHENSION SKILL AT EIGHTH GRADE SMP KINGSTON SCHOOL, DELI SERDANG

The purpose of this study is to determine the efficacy of applying THIEVES and CSR techniques to increase reading comprehension in Kingston School grade 8 students. The researcher devised a quasi-experimental study in order to fulfil the research's goal. The participants in this study were 108 eighth-grade Kingston School students, with 36 involved in the experimental group 1 who were taught reading comprehension using the THIEVES strategy, 36 involved in the experimental group 2 who were taught reading comprehension using the CSR strategy, and the remaining 36 involved in the control group who were taught reading comprehension using the conventional strategy. A pre-test and post-test with 40 multiple-choice items were included in the data collection instrument. The results were compared and examined to determine the efficacy of the two strategies. The experimental groups 1 and 2 scored higher than the control group, according to the results of a study. THIEVES and CSR techniques are both successful in boosting reading comprehension, according to the results. The statistics also indicate that the CSR method outperforms the THIEVES technique.&#x0D; &#x0D; Keywords: reading comprehension, THIEVES Strategy, CSR Strategy
THE EFFECT OF READING PURPOSE ON READING COMPREHENSION OF GRADE XII STUDENTS OF MADRASAH ALYAH DINIAH PUTRI RIAU

The effect of reading purpose on the reading comprehension was studied at Madrasah Alyah Diniyah Riau.The aim was to know and identify the effect of reading purpose on the reading comprehension of 12th-grade students 2021 – 2022 Academic Year at Madrasah Alyah Diniyah Riau . A mixed-method approach and quantitative and qualitative of an ex-post-facto study was applied. The 42 of 12th-grade IPA and IPS students of Madrasah Alyah Diniyah Riau 2021–2022 academic year were taken as sample by using the purposive sampling technique. The instrument of questionnaire and a test of reading comprehension were used to collect the data. Hypothesis testing (t-test) were used to analyse the collected data. From the analysis result it was found that the t-value = 277,543 &gt; t-table 1,689 with a sig. value of 0,000 &lt; 0.05,it can be interpreted that reading purpose affected the reading comprehension of Grade XII students 2021 – 2022 Academic Year of Madrasah Alyah Diniyah Riau
The Effect of Content Schemata on EFL Moroccan Learners’ Reading Comprehension: The Role of the Pre-Reading Activities

This study examined whether content schemata activation facilitate reading comprehension or not. It is motivated by two research questions: (1) does content familiarity facilitate reading comprehension? And (2) do teachers activate learners background knowledge? To examine these questions, the study used both qualitative and quantitative research methods to collect and analyse data. It was hypothesized that content schema activation facilitates reading comprehension. This article has two goals: (1) to identify the effects of pre-reading activities on Moroccan EFL students’ reading comprehension through content schema activation, and (2) to translate the research findings into suggestions and guidance for textbook designers, teachers, and policy makers. Previous research has shown that providing learners background knowledge enhances their comprehension. Our most important contribution is to provide useful guidelines for actions and practice implications related to schema activation at the pre-reading stage in Moroccan EFL context. An experimental research study was conducted to test our hypothesis using students test score data to measure and compare the performance of the treatment group and the control group. The findings from the research show that the impact of content schema activation on reading comprehension is positive as assumed. The results, implications for teachers, and future research were discussed.
Learning Asymmetric Embedding for Attributed Networks Via Convolutional Neural Network

Recently network embedding has gained increasing attention due to its advantages in facilitating network computation tasks such as link prediction, node classification and node clustering. The objective of network embedding is to represent network nodes in a low-dimensional vector space while retaining as much information as possible from the original network including structural, relational, and semantic information. However, asymmetric nature of directed networks poses many challenges as how to best preserve edge directions during the embedding process. Here, we propose a novel deep asymmetric attributed network embedding model based on the convolutional graph neural network, called AAGCN. The main idea is to maximally preserve the asymmetric proximity and asymmetric similarity of directed attributed networks. AAGCN introduces two neighbourhood feature aggregation schemes to separately aggregate the features of a node with the features of its in- and out- neighbours. Then, it learns two embedding vectors for each node, one source embedding vector and one target embedding vector. The final representations are the results of concatenating source and target embedding vectors. We test the performance of AAGCN on three real-world networks for network reconstruction, link prediction, node classification and visualization tasks. The experimental results show the superiority of AAGCN against state-of-the-art embedding methods.
Theoretical and Experimental Study of a Complexity Measure for Analogical Transfer

Analogical transfer addresses classification and regression tasks, performing a plausible inference according to which similar instances are likely to be associated with similar labels. This paper proposes a detailed study of the ordinal implementation of this principle by the so-called $${\texttt {CoAT}}$$ algorithm, that is based on a data set complexity measure quantifying the number of inversions observed when ranking the data according to their instance or label similarities. At a theoretical level, it establishes an upper bound of the complexity measure, providing a reference value to which the observed one can be compared. At an algorithmic level, it proposes an optimization that allows decreasing the computational complexity by one order of magnitude. At an experimental level, it studies the correlation of the complexity measure with the accuracy of the conducted label inference, as well as with the classification task difficulty, as captured by the class overlapping degree.
K-NN Based Air Classification as Indicator of the Index of Air Quality in Palembang

Good air quality is something that is wanted by every human who lives in big cities. Clean air and no pollution is one of the proper environmental requirements. One of the most severe causes of air pollution is due to large-scale forest fires due to the long dry season or is carried out by irresponsible persons which they commonly refer to as land clearing in an easy and inexpensive way by utilizing the reason of the dry season. The purpose of this study is to classify air quality in Palembang using a data mining approach. Then use the results of the classification as an indicator of the level of air quality in the city of Palembang. The data mining approach that researchers use is the K-Nearest Neighbor algorithm. Based on the test results of K-NN calculations and measured using a confusion matrix produce an accuracy of 80 percent, 82.3 percent for precision, and 93.3 percent for recall. The measurement results show that the calculation using the K-NN algorithm can be used as an indicator in measuring air quality, of the 20 that have been trained and tested only 4 inaccurate data, this inaccuracy occurs because the source data has unbalanced classes such as unhealthy and very unhealthy healthy have 1 sample each. So it proves that the performance of classifiers using the K-NN algorithm relevant as an indicator of air quality levels in the city of Palembang.
The application of online practicum in assisting learning process of database courses using Waterfall method

The Open University is a State University prioritizing independent learning. Independent learning would certainly succeed if the students owned a strong determination in effectively and efficiently manage their time. In the Information Systems Study Program curriculum, there are several practical courses that must be conducted offline. In 2019 however, the phenomenon of the COVID-19 pandemic outbreak requiring all educational units to carry out learning from home had impacted on the learning process, especially the implementation of practicum at the Open University. The implementation of practicum activities usually held in collaboration with several institutions has become constrained. This triggered the possibility of not achieving student learning outcomes related to learning materials learned through practicum. Therefore, as an alternative solution, an application is required that could assist the students in carrying out practicum learning activities independently which is adapted to each teaching material. This practical application would be developed in a study using the Waterfall method consisting of analysis, design, implementation, testing and maintenance. From the results of test using the System Usability Scale (SUS), the score obtained is 64.44 (margin low). This means that the application online practicum can be used but does not meet the requirements to be implemented into existing learning systems until it gets a score of more than 80 (Acceptable).
Application of Hot Fit Model to Analyze Information Technology Ams (Academic Management System)

Bina Insan University has applied a computer-based information system. This system is named AMS (Academic Management System). The application of AMS (Academic Management System) at this time is still experiencing various obstacles and obstacles at the level of user acceptance. This study aims to analyze the results of the evaluation of the success factors for implementing AMS (Academic Management System) using the Hot-Fit Model (Human Organization Technology – Net benefits). This model was chosen because this model can provide an explanation and provide an evaluation of the factors that influence the implementation of a system at the University of Bina Insan Lubuklinggau in terms of Human (Human), Organization (Organization), Technology (Technology), and Net benefits. In addition, the success of implementing AMS (Academic Management System) at the University of Bina Insan Lubklinggau, is also influenced by the support and encouragement from universities to AMS (Academic Management System) users, as well as the availability of adequate facilities within the Bina Insan Lubuklinggau University to use AMS (Academic Management Systems). From the analysis that has been carried out on 80 respondents who have filled out the research questionnaire, the results show that to test the validity of the variables (Human), Organization (Organization), Technology (Technology), and Net benefit, it shows that each question measured on all variables is valid, which indicated by Corrected Item – Total Correlation or (rcount) the entire score of Corrected Item – Total Correlation or (rcount) greater than rtable of 0.220, and for the F test results obtained a value of F = 13.334 with a significance of 0.000. meaning that the variables of human, organization and technology together have a significant effect on net-benefit (Y).
Explanatory Data Analysis to Evaluate Keyword Searches for Educational Videos on YouTube with a Machine Learning Approach

One of the most important parts of data science is the process of explanatory data analysis. This study aims to analyze learning videos on YouTube using search keywords such as learning biology, chemistry, physics, computers, mathematics, management, accounting, citizenship, history, and culture. The method used is the explanatory data analysis technique with a Machine Learning approach. The dataset used in this study uses learning video search keywords found on the YouTube digital platform. After doing a thorough analysis of all existing variables, we found that in the context of searching for learning video keywords on YouTube, the viewing variable has a heatmap correlation of 0.97 on the likes variable, 0.97 on the subscribers variable, -0.15 on the duration variable and 0.95 on the comment variable. The duration variable negatively correlates with all variables based on the analysis using a correlation heatmap using the seaborn library. Our analysis found that the number of learning videos with the search keyword Mathematics had the highest number of views among other variables. Further research can use existing variables or also add variables and add search keywords on YouTube. The data analysis approach can also be done using SPSS, R and also a Machine Learning approach with different libraries.
Detection of Room Cleanliness Based on Digital Image Processing using SVM and NN Algorithm

A clean environment can prevent us from disease and can increase productivity. A neat and clean room arrangement can affect health, avoiding the possibility of stress, lethargy, and depression. The room recognition process based on its neatness is carried out through a process of matching and comparing the images that are used as training and testing sets. Technological developments make it possible to detect room conditions through image. Detection uses image processing by classifying images into 2 categories, clean and messy. It has been widely used in various fields, one of which is hospitality. In determining the clean room and messy room has problems due to image quality, different lighting, and image similarity. This study aims to detect clean and messy spaces by comparing the Support Vector Machine and Neural Network methods on a dataset of 199 images. Based on the test, the highest accuracy classification value was 98.0% for the Neural Network method with an AUC of 0.999
Optimization of Transaction Database Design with MySQL and MongoDB

Database is one of the most important parts in running an application. Databases can be used to store data. However, in running an application, the selection of an appropriate database needs to be considered, so that the resulting information can be in accordance with user needs. In developing applications, more people use RDBMS design which has a very structured nature, but technological developments have introduced NoSQL as a database development method. In this research, MySQL and MongoDB databases will be tested in transaction processing. The tests carried out include comparisons of database designs, comparisons of defining table structures, comparisons of running time in the insert, update, delete, search processes. The test results show that MongoDB has a simpler table description structure than MySQL. The results of the running time test show that MongoDB has a faster running time difference than MySQL. In the insert process there is a time difference of 0.005625 sec, update 0.001688 sec, delete 0.00075 sec and search 0.006875 sec.
Learning Fuzzy Neural Networks by Using Improved Conjugate Gradient Techniques

One of the optimal approaches for learning a Takagi Sugeno-based fuzzy neural network model is the conjugate gradient method proposed in this research. For the PRP and the LS approaches, a novel algorithm based on the Liu-Storey (LS) approach is created to overcome the slow convergence. The developed method becomes descent and convergence by assuming some hypothesis. The numerical results show that the developed method for classifying data is more efficient than the other methods, as shown in Table (2), where the new method outperforms the others in terms of average training time, average training accuracy, average test accuracy, average training MSE, and average test MSE.
Development of Android-Based Early Reading Learning Media

Early reading is a skill that students should have. This skill becomes a provision for the next stage of skills .Learning to recognize letters, especially early reading in Golan Kindergarten during the pandemic, experienced problems. Students feel less interested when the learning process is carried out conventionally. In addition, parents also have difficulties when it comes to delivering material to children. Through the use of android-based learning media, the learning process is expected to attract students. The method developed the Borg &amp; Gall model, which consisted of ten stages. The data collection techniques used include observation, interviews, and questionnaires. Observation is used to find out the learning conditions in schools. Interviews, conducted with teachers to find out the use value of the media created. Meanwhile, the questionnaire is used as a reference to find out the level of needs in schools. The resulting learning media is tested through media expert validation tests and practicality tests. Validation of media experts to assess whether media is suitable for use as an alternative to learning media in schools. Practicality test, used to assess the level of practicality of the resulting medium. The results of media expert validation obtained a percentage of 81.5, stating that the media was valid. The practicality test was 8,262, and the mean score of students was 77. The two results presented that the android-based learning media for early reading was feasible
Analyze application Building Management of The Bank Indonesia representative office West Java

One of the facilities and assets owned by Bank Indonesia is an official residence intended for permanent employees, office buildings, and other facilities such as borrowing rooms and goods that can be used by employees to support office activities. But in the implementation of maintenance of official houses and office buildings as well as the process of requesting room loan and goods still done manually. Therefore, a Building Management application is needed that can help the maintenance activities of official house buildings and office buildings as well as the process of requesting the loan of rooms and goods. Building Management application is a software that is used for building maintenance and management of all building needs including borrowing space and goods in an office building. This study aims to accelerate the process of requesting repairs to official office buildings and office buildings as well as borrowing rooms and goods. In addition, this application also generates automatic report recording output. The method in this study use V-Model is an extension of the waterfall model and is based on the association of the testing phase to each appropriate development phase. The result in this study is application to be built is based on the website using the CodeIgniter framework and the V Models system development method with stages arranged starting from verification which contains the needs analysis stage, design to the coding phase and also the validation process that contains testing of the application to determine application functionality and also know the level application usability for the user.
Product Recommendation System Application Using Web-Based Equivalence Class Transformation (Eclat) algorithm

The use of saved transaction data can provide a lot of knowledge that useful to the company in making policy and find the strategy in Alfamidi. In applying that goal, that is using Market Business Analysis. One of the techniques of Data Mining is Association Rule, which is the procedure of Market Basket Analysis to find the customer buying patterns. This pattern can be one of the ways in making policy and business strategy. One pattern determined by two parameters, they are support (support value) and confidence (certainly value). This analysis used algorithm Equivalence Class Transformation (ECLAT). One of the patterns resulted from analysis to the 30 transaction data with 12 category items. As an instance, if we buy strawberry jam then buy essence of bread with confidence value = 1%. The results obtained an also be used in helping the Alfamidi to help in determine the inventory decisions. So, the conclusion may be taken if consumers could buy strawberry jam then bought essence of bread simultaneously, then the Alfamidi should at least maintain the availability stock of both these items in order to remain the same.
Comparison of Drug Type Classification Performance Using KNN Algorithm

The error of decommissioning is a serious problem that is often faced in medicine. In the face of these problems, information technology has a very important role. One of the information technologies that can be used is to use the machine learning classification algorithm K-Nearest Neighbor KNN. KNN is a type of machine learning algorithm that can be applied to problems with classification and regression prediction. The classification of types of drugs for patients greatly affects the health of the patient. The patient data is processed and transformed to numbers, which are then divided into training data and test data from 90:10, 80:20, 70:30 and using the Cross Validation model. KNN works through the nearest neighboring value with a value of k = 3 calculated by the calculation of Euclidean Distance, and then evaluated using the Confusion Matrix. The performance of the KNN algorithm resulted in the highest Accuracy value of 98.33%, a Precision value of 98.8%, a Recall value of 96.2%, and an F-measure value of 97.48%. The performance is obtained from the sharing of training data and 90:10 test data. The data share results in high performance compared to other data shares, including using the Cross Validation model. And the lower the k value, the higher the value of the resulting performance. The results show that the performance of the KNN algorithm is working well.