Reviewerly/exHarmony huggingface dataset:
The effect of monetary policy shocks on industrial output in Afghanistan

Purpose- This study examines the impact of monetary policy shocks on industrial output in Afghanistan. Quarterly secondary data were collected for the period from 2003 to 2021 from various official sources such as the Statistical Bulletin of the Central Bank of Afghanistan, the International Monetary Fund, and the World Bank. Methodology- This study used a three-stage procedure. The first stage involved testing the stability of the variables to be included in the model. The second stage involves selecting the optimal lag length using various lag length criteria. Finally, the Vector Error Correction (VECM) model was used to determine if there were any short-run correlations or dynamics among the variables. The study also conducted some post-tests to confirm the validity and robustness of the regression model. Findings- The results of the long-run vector error correction model show that there is a long-run causality running from monetary policy rate, broad money supply, inflation, exchange rate, and a commercial bank loan to the industrial sector. So, there was a speed of adjustment from the short-run to the long-run equilibrium. However, the Wald test confirmed that the short-run causality runs from the explanatory variables to the dependent variable. Commercial bank credit to the industrial sector was found to cause a change in industrial production in all six lag periods. The results were further supported by the Granger causality test. Shocks in commercial bank lending to the industrial sector were found to have no significant effect on industrial output. However, the performance of the industrial sector was the main cause of the flow of commercial banks' credit to the industrial sector. Conclusion- The study recommends that monetary policy should proceed with extreme caution in managing the exchange rate. The study also recommends that the Central Bank of Afghanistan should encourage commercial banks to offer credit to the industrial sector at low-interest rates. Keywords: Monetary policy shocks, industrial output, Vector Error Correction Model (VECM), Granger Causality Test, Afghanistan. JEL Codes: E52, E32, L60
Interactive Mobile Application in Learning Iban Language

My Iban application is an interactive mobile application for learning the Iban language. The aim of this study is to develop a mobile application that is focusing on creating alternative tools in learning Iban language where it will ease the students to learn. This application development is based on the problem that occurred when there is no suitable application in learning Iban language among the students in primary school. The methodology that has been chosen in developing the application is the Evolutionary Prototyping model. Quantitative technique is used to test and evaluate My Iban applications. Based on the findings obtained from the users through a survey, it finds out that My Iban application have gained positive feedback. The functionality and the usability of My Iban application has been tested and approved by the user. For future works, the use of interactive multimedia elements in developing mobile applications need to be improved. In conclusion, the application hopefully can encourage the students to learn the Iban language better via mobile application.
Pengaruh Model Pembelajaran Kooperatif Tipe Team Games Tournament terhadap Aktivitas Belajar Siswa pada Mata Pelajaran PAI

The Team Games Tournament type cooperative learning model implemented at Jati Agung Taman Sidoarjo High School has important value to study because it is effective, interesting and fun in learning, so that students do not get sleepy easily and do not feel bored during the learning process. Research objectives: 1) to determine the team games tournament type cooperative learning model at Jati Agung Taman Sidoarjo High School, 2) to determine student learning activities in PAI subjects at Jati Agung Taman Sidoarjo High School, 3) to determine the influence of the team games type cooperative learning model tournament for student learning activities in PAI subjects at Jati Agung Taman Sidoarjo High School. This research uses quantitative methods with percentage analysis and product moment correlation. The research population was 60 students. Data was collected through questionnaires, observation and documentation. The research results show that the use of the TGT model is considered good with a result of 61.83%. Student learning activities in PAI subjects were also assessed as good with a result of 61.33%. There is a positive influence of the TGT model on student learning activities in PAI subjects with a correlation coefficient rxy = 0.566, indicating a moderate influence.
Nearest Centroid Classifier Based on Information Value and Homogeneity

The aim of this paper is to introduce a novel classification algorithm based on distance to class centroids with weighted Euclidean distance metric. Features are weighted by their predictive powers and in-class homogeneities. For predictive power, information value metric is used. For in-class homogeneity different measures are used. The algorithm is memory based but only the centroid information needs to be stored. The experimentations are carried at 45 benchmark datasets and 5 randomly generated datasets. The results are compared against Nearest Centroid, Logistic Regression, K-Nearest Neighbors and Decision Tree algorithms. The parameters of the new algorithm and of these traditional classification algorithms are tuned before comparison. The results are promising and has potential to trigger further research.
Comparison Analysis of C4.5 Algorithm and KNN Algorithm for Predicting Data of Non-Active Students at Prima Indonesia University

Education is important nowadays because universities need to improve their students' skills so they can compete in the globalization era. Education can be obtained through both formal and informal channels, and knowledge is available everywhere, especially in today's world where information tools are rapidly evolving. Inactive students are students who do not participate in a course for a maximum of two consecutive semesters. Students who are not active have the opportunity to drop out of university studies. Students who drop out of college are usually motivated by economic factors, and the cessation of the lecture process can cause inactivity and administrative costs. Therefore, this research was conducted using the C4.5 algorithm method and the K-Nearest Neighbor (KNN) algorithm to compare and predict data on inactive students at Universitas Prima Indonesia. The research continued with the data collection and data preprocessing stages, after which the data mining process was carried out to get the final results of this research. The testing process follows the process of comparing the C4.5 algorithm and the K-Nearest Neighbor (KNN) algorithm with K-fold crossing. This evaluation step is compared by considering the comparison values of the confusion matrix (precision, precision, recall). The accuracy results obtained by each algorithm provide information about the effectiveness of using these techniques in processing the specified dataset. The accuracy of the Decision Tree C4.5 algorithm is 99.12% and the K-Nearest Neighbors algorithm is 99.14%. Based on research conducted using the K-Nearest Neighbors and C4.5 algorithms to predict inactive students, the KNN algorithm is more accurate than the C4.5 algorithm.
Comparison of RNN Architectures and Non-RNN Architectures in Sentiment Analysis

This study compares the sentiment analysis performance of multiple Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks. THE METHODS EVALUATED ARE simple Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Recurrent Neural Network, and 1D ConvNets. A dataset comprising text reviews with positive or negative sentiment labels was evaluated. All evaluated models demonstrated an extremely high accuracy, ranging from 99.81% to 99.99%. Apart from that, the loss generated by these models is also low, ranging from 0.0043 to 0.0021. However, there are minor performance differences between the evaluated architectures. The Long Short-Term Memory and Gated Recurrent Unit models mainly perform marginally better than the Simple Recurrent Neural Network, albeit with slightly lower accuracy and loss. In the meantime, the Bidirectional Recurrent Neural Network model demonstrates competitive performance, as it can effectively manage text context from both directions. Additionally, One-Dimensional Convolutional Neural Networks provide satisfactory results, indicating that convolution-based approaches are also effective in sentiment analysis. The findings of this study provide practitioners with essential insights for selecting an appropriate architecture for sentiment analysis tasks. While all models yield excellent performance, the choice of architecture can impact computational efficiency and training time. Therefore, a comprehensive comprehension of the respective characteristics of Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks is essential for making more informed decisions when constructing sentiment analysis models.
Paraphrase Generation For Reading Comprehension

Reading comprehension is an assessment that tests readers understanding of a concept from the given text. The testing process is conducted by providing questions related to the content within the context of the text. The purpose of this research is to create new question variations from existing questions, and one of the methods to achieve this is by paraphrasing questions through the task of paraphrase generation. This can help ensure that readers have fully grasped a concept of a text. This study employs a traditional approach known as the thesaurus-based approach, in which the process involves substituting synonyms using the Indonesian Thesaurus dictionary. The data used consists of a list of Indonesian language reading comprehension assessment questions ranging from elementary to high school levels. To measure the quality of the generated paraphrased questions, two evaluation processes are conducted which are automatic evaluation with the scores ranging from 0-1 and human evaluation with score ranging from 1-4. The automatic evaluation includes the BLEU-4 metric, resulting in a score of 0.044, and the ROUGE-L metric, resulting an F1-score of 0.421. As for human evaluation, the obtained relevancy score is 2.533, and the fluency score is 3.186. The results from both evaluation metrics indicate that the generated paraphrased questions exhibit diverse new word choices but tend to have slightly different meanings compared to the reference questions.
Densenet Architecture Implementation for Organic and Non-Organic Waste

Garbage is the result left over from the process of daily human activities and activities which are considered no longer suitable for use, ranging from household waste to large-scale industrial waste. Therefore, the classification of waste is important because the problem of waste disposal is increasing and the way of processing is wrong. This research focuses on the classification of organic and non-organic waste using the DenseNet architecture. The dataset is processed first and each image in the dataset is resized to 128x128 pixels before being used in the model. We then trained all DenseNet types namely DenseNet121, DenseNet169, DenseNet 201, and compared their performance. Based on the test results, all DenseNet models that were trained were able to produce good accuracy, precision, recall, and F1 scores in garbage classification. In particular, our designed DenseNet121 model achieves 93.1 accuracy, 94.08% precision, 94.00% recall, 94.03% F1 score and 1min 34s training time as the best among other models. These results prove that the DenseNet architecture can be used to classify organic and non-organic waste correctly.
Blockchain-Based Academic Identity and Transcript Management in University Enterprise Architecture

The present research examines the implementation of Blockchain-based Identity and Academic Transcript Management in a university's enterprise architecture. This research is motivated by the increasing demand for secure, transparent, and efficient student identity management and the need to store easily verifiable academic transcripts. Blockchain technology has been spotlighted because it provides security and data integrity solutions. This research aims to determine if integrating Blockchain into the University's Enterprise Architecture can improve the management of student identities and academic transcripts by reducing the risk of forgery and facilitating more dependable access for interested parties. Increased security and efficiency in managing student data are the practical implications of this research, which can help universities reduce the risk of data loss and increase stakeholder trust. This research method includes surveying various universities that have adopted Blockchain technology in their academic identity and transcript management. In addition, we will assess its technical implementation, evaluate its effect on efficiency, and conduct interviews with university personnel involved in the implementation process. This study's anticipated outcome is providing universities planning to adopt Blockchain technology for Enterprise Architecture with actionable guidance. This research will identify the benefits, challenges, and best practices of integrating Blockchain in academic identity and transcript management and lay the groundwork for further improvement of educational services in university settings.
Vehicle Detection and Identification Using Computer Vision Technology with the Utilization of the YOLOv8 Deep Learning Method

Vehicle identification and detection is an important part of building intelligent transportation. Various methods have been proposed in this field, but recently the YOLOv8 model has been proven to be one of the most accurate methods applied in various fields. In this study, we propose a YOLOv8 model approach for the identification and detection of 9 vehicle classes in a reprocessed image data set. The steps are carried out by adding labels to the dataset which consists of 2,042 image data for training, 204 validation images and 612 test data. From the results of the training, it produces an accuracy value of 77% with the setting of epoch = 100, batch = 8 and image size of 640. For testing, the YOLOv8 model can detect the type of vehicle on video assets recorded by vehicle activity at intersections with. However, the occlusion problem overlapping vehicle objects has a significant impact on the accuracy value, so it needs to be improved. In addition, the addition of image datasets and data augmentation processes need to be considered in the future
Comparison Of The C.45 And Naive Bayes Algorithms To Predict Diabetes

Diabetes mellitus is an urgent global health problem and has a major impact on people around the world. This disease is characterized by high levels of sugar (glucose) in the blood due to disturbances in the production or use of the hormone insulin by the body. This study aims to carry out accurate early detection of diabetics so that they can be treated as soon as possible to reduce the risk of death and to compare the two algorithms that have the best level of accuracy. The algorithms used in this study are the C4.5 and Naïve Bayes Decision Tree Algorithms. The results of the experiments carried out in this study the Decision Tree Algorithm C4.5 and Naïve Bayes can be used in modeling the early detection of diabetes. The highest average accuracy results were obtained at 90.835% using the Decision Tree C4.5 Algorithm. As for the Naïve Bayes Algorithm, an average accuracy rate of 90.745% is obtained. The pruning process was carried out using the Decision Tree Algorithm C4.5, the accuracy performance increased to 91.30%. There were 18 patterns or rules for the early detection of diabetics from the built model. The determination of attributes, the number of attribute dimensions, and the number of samples greatly affect the performance of the model built.
PENGGUNAAN METODE SAS BERBANTUAN KARTU KATA BERGAMBAR TERHADAP KEMAMPUAN MEMBACA PESERTA DIDIK KELAS I DI SEKOLAH DASAR

This study aims to determine the effect of using the SAS method assisted by picture word cards on the beginning reading ability of grade 1 students in elementary schools. In this study using a quantitative approach with a research design using a quasi-experimental or quasi-experimental design. The samples in this study were class I-A, which consisted of 25 students as class and class I-B, which consisted of 25 students as the control class. The data collection technique used in this study is through tests and observations. The tests used are pre-test and ¬post-test. Based on the research results, the experimental class obtained an average pretest score of 45.53 with an average posttest score of 86.12. Meanwhile, the average value of the control class pretest was 44.86 and the average posttest value of the control class was 66.96. The results of the tests carried out by researchers in the experimental class and control class, it can be concluded that the posttest score was found to be greater than the pretest, which means that students' initial reading ability increases. The researcher used an independent test sample t test to find the effect of the SAS method assisted by picture word cards on the beginning reading ability of first grade elementary school students. It is known that the calculated t value is 16.139 with a sig value of 0.000 &lt;0.05, which means that there is a significant influence on the initial reading ability of first grade elementary school students in the experimental class and the control class. Then in accordance with the decision making that has been determined then that Ho is rejected and Ha is accepted. Thus the researchers concluded that there was an effect of using the SAS method assisted by picture word cards on the initial reading ability of grade 1 students in elementary schools.
Inquiry Training Based on Science Subjects E-Module Development to Improve Hots Literacy Ability

The purpose of this study was to determine the feasibility and effectiveness of Inquiry Training-based E-Modules in science subjects to improve HOTS literacy skills for Class IV Elementary School students. This research is a type of research and development (Reasearch and Development) using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The results showed that the E-Module based on Inquiry Training in science subjects to improve HOTS literacy skills was "Very Eligible" with details of the average score given by design experts 4, 4 in the "Very Good" category with a feasibility presentation of 88 % with “Very Eligible” category. The material expert validation results were 4.56 in the "Very Good" category and the feasibility presentation rate was 91% in the "Very Eligible" category. And based on the results of the pre test and post test there was an increase in presentation of 29.3 %. Initial pre-test results were 5 4.5 % and post-test results were 8 4 %. Therefore it can be concluded that Inquiry Training-based E-Modules in science subjects to improve HOTS literacy skills are appropriate and effective for use in the learning process.
"Bullfinches of the North": children's portraits of V.A. Igoshev from the funds of the state museums of Ugra

The article is devoted to the theme of childhood – one of the main creative lines in the artistic heritage of Vladimir Alexandrovich Igoshev. The artistic development of the distant and previously little-known periphery began with the first graphic works and individual sketches in March 1954, dedicated to the little inhabitants of the harsh earth, and ended with heartfelt masterpieces created in the last years of the master's life, when all the strength was taken away by illness. Based on the art criticism analysis of V.A. Igoshev's works of art from the funds of the state museums of Ugra, the figurative and stylistic originality of the children's theme in the context of the "northern cycle" is revealed, the general evolution of portrait creativity is indicated. It is proved that as artistic tasks changed, as a result of deep creative immersion in the original and organic world of the North, the figurative interpretation of the children's theme, which acquires an aesthetically autonomous meaning, also changed.
The Effects of Using MALL Applications to Teach Vocabulary to EFL Learners in UTAS, Oman

This study aims to evaluate the effectiveness of incorporating different types of smartphone applications i.e., game-based, flashcards and self-paced applications in vocabulary teaching and learning at a public university in Oman. Past research indicated that Mobile Assisted Language Learning (MALL) applications facilitated second language learning (Stockwell, 2010). As a result, many teachers have opted to use smartphone-based applications such as Kahoot, Quizlet, and Memrise to engage their learners in the classroom and motivate them to learn lexical items. This paper investigates how deep is the influence and effectiveness of teachers’ use of mobile applications on the students' learning of Academic vocabulary and how these applications should be utilized to be as effective as possible. The study uses semi-structured interviews with 8 interviewees who are lecturers at UTAS, Oman. The interviews focused on frequency of usage, efficiency, personal experiences and ideas for effective implementation of such applications in the future. Results indicate that smartphone applications used for vocabulary learning have a positive impact on vocabulary teaching and learning, enhancement of academic learning skills and advancement of academic vocabulary retention levels. The study also provides recommendations for effective use of mobile applications in vocabulary learning and teaching.