Reviewerly/exHarmony huggingface dataset:
STUDENTS’ RESPONSES TOWARDS E-LEARNING SCHOOLOGY CONTENT ON CREATIVE WRITING LEARNING DURING THE COVID-19 PANDEMIC

The era of the COVID-19 pandemic brought major changes in the world of education, not only in Indonesia, but in the world. Almost all countries feel the same way. The learning system has switched from an offline/face-to-face to an online system. This world disaster has made many new applications appear or the famous applications that support online learning. One of them is Indonesian learning which requires learning technology, especially writing skills; creative writing. Writing skills in high school are needed. There are many types of creative writing that must be mastered by students, such as writing biographies, writing short stories, writing poetry, and so on. Moreover, in the 2013 curriculum, Indonesian language learning is text-based which directs students to be able to produce text-based writings. The purpose of this study was to determine the practicality of the content of E-Learning Schoology in learning creative writing in high schools in Palembang city in terms of teachers. This research is research and development research. The object of research is the 10th grade Indonesian at SMA Palembang. The method used is the Delpi method. Data collection techniques are questionnaires and FGDs. Data were analyzed quantitatively and qualitatively. Based on the results of research and discussion, it can be concluded as follows. First, the E-Learning Schoology content for creative writing learning has been practically used based on the small group test which was participated by 10 students. This practicality can be seen from the assessments given by students on aspects of material, language, content presentation, and E-Learning which show a very decent value. Mean value which was above 4.25. Second, the students stated that the content developed has advantages such as, interesting, motivating, complete material, easy to use, inspiring, provides many learning experiences, the language is easy to understand, the features used are interesting and make it easier to learn. Third, this content has weaknesses, but these weaknesses occured because of the lack of understanding of students in operating this e-learning.
Development of Codular-Assisted Android-Based Mathematics Media to Increase Student Motivation and Learning Outcomes

Research on the development of codular-assisted android-based mathematical media was carried out to increase student motivation and learning outcomes in learning mathematics. This research refers to the ADDIE model, which consists of 5 stages: analysis, design, development, implementation, and evaluation. The research was conducted on 30 class IX students at MTS Muhammadiyah 1 Malang. The study results show that the validity value of the media is 88.4%, with a very good category. So that this android media meets the criteria and is very suitable for use in learning. As for the test results on student learning outcomes, this media can improve student learning outcomes by increasing the average test results, namely the pre-test 41.67 and the post-test 91.67, which shows that the data results make the data distribution homogeneous. The student learning motivation questionnaire as a whole with the pre-test results found 93.33% who had motivation in the good criteria, and the remaining 6.67% were in the fewer criteria. In the post-test, 83.33% had very good motivation, and the remaining 16.67% were in good criteria. This shows an increase in student motivation after using Android-based media. So it can be concluded that the use of android-based mathematics media with the material Build a Curved Side Room (Tube) developed in this study is valid and can increase motivation and student learning outcomes. This study proves that learning media can increase motivation and learning outcomes. Therefore, the results of this study can be used as an alternative to learning mathematics, especially to increase student motivation and learning outcomes.
Centroid Based Clustering Approach for Extractive Text Summarization

Abstract: Extractive text summarization is the process of identifying the most important information from a large text and presenting it in a condensed form. One popular approach to this problem is the use of centroid-based clustering algorithms, which group together similar sentences based on their content and then select representative sentences from each cluster to form a summary. In this research, we present a centroid-based clustering algorithm for email summarization that combines the use of word embeddings with a clustering algorithm. We compare our algorithm to existing summarization techniques. Our results show that our approach stands close to existing methods in terms of summary quality, while also being computationally efficient. Overall, our work demonstrates the potential of centroid-based clustering algorithms for extractive text summarization and suggests avenues for further research in this area.
ENGLISH PRESERVICE TEACHERS' PERCEPTION OF ICT: THE STUDY ON THE STUDENTS FROM RURAL AREAS

The development of technology brings a good effect on learning English. Some research supports that using technology in learning English increases students' comprehension. However, the fact in the field state that many teachers teach English using conventional methods without technology. On this basis, researchers aimed to examine the perceptions and attitudes of English preservice teachers. This research will be exciting because knowing the current perception of preservice teachers may impact their behavior with technology in the future. In conducting the study, the researchers used a descriptive qualitative method using interviews to obtain data. The criterion used is English education students who study at the higher education level in Sintang. There are 6 (six) students or preservice teachers that the researchers chose as the participants. The result of this research showed that all of the participants think that ICT is important for teaching English. Using ICT in learning can make students understand the material better, making them easier to find information from the material being taught.
A QUALITATIVE EVIDENCE SYNTHESIS OF ARTICLE ABSTRACT WRITING IN ELT AND LITERATURE JOURNALS

Abstracts are essential components of academic research articles, providing a concise summary of the study's purpose, methods, results, and conclusions. This qualitative evidence synthesis examines the quality, structure, and content of abstracts in the fields of English Language Teaching (ELT) and Literature. A systematic search of relevant databases identified 15 peer-reviewed articles published in English with a focus on abstract writing. Thematic analysis was used to analyze the studies for their research findings, methodologies, and recommendations related to abstract writing. The findings reveal that abstracts tend to be descriptive, with a focus on presenting the research problem, methodology, and main findings. However, there is a lack of consistency in structure and content across journals, and the quality of abstracts varies widely. Based on the analysis, several key recommendations for improving abstract writing in ELT and Literature journals are proposed, including greater consistency and standardization across journals, and improved training for authors and reviewers. This qualitative evidence synthesis offers a thorough overview of the body of knowledge on the subject of writing effective abstracts for ELT and literature journals and makes suggestions for further study and practice in this field. The study highlights the need for greater attention to abstract writing quality, structure, and content, and provides practical recommendations for improving the quality of abstracts in ELT and Literature research articles using thematic analysis as a data analysis technique.
Benefits and Challenges of Group Discussion as Creative Learning Strategies in Speaking Class

Abstract: Creative learning strategy has turned out to be an important issue in foreign language learning. This article is aimed to investigate the benefits and challenges of group discussion as creative learning strategy to improve speaking’s proficiency. The research was conducted in an Islamic Senior High School. The research applied qualitative approach by using interviews and Focus-Group Discussions with 8 students as the source of data. Based on the analysis results, it was found that Group Discussion as creative learning strategy were beneficial during the speaking class because of it promote interaction and learning, motivation encouragement, and develop respect to another’s’ idea. Meanwhile, during the group discussion several issues arouse namely the individual learning preference, the problems with language aspects, and the students’ self-confidence. The research implies the significance of creative learning strategy to improve the English speaking successfully.
FACTORS AFFECTING STUDENTS’ FAILURES DUE TO ANXIETY IN LEARNING ENGLISH

Abstract: This study aims to determine the factors affecting students' failures due to anxiety in learning English. It was conducted at Universitas Kristen Indonesia, Jakarta, within three months. This study was a descriptive qualitative study with a survey design. The study's respondents consisted of 98 from three parallel classes at UKI. The sampling method in this study uses the cluster sampling method. In this study, a set of questionnaires was used to collect the data. It is used to measure English language anxiety. It has three constructs: communication anxiety, fear of negative assessment, and anxiety in exams, and the English learning anxiety has four constructs: personal reason, teacher attitude, test situation, and teaching procedure. The finding of this study is that English language learners have moderately high speaking skills anxiety and that the level of English language anxiety of English language learners at UKI as a whole in the two English language skills, namely listening and speaking skills, reached a medium-high level. Then it is concluded that teachers emphasize compelling and diverse strategies and techniques of teaching English for English language skills, namely listening and speaking skills, to help reduce anxiety among students applying English.
Lattice Valued Fuzzy Sets in UP (BCC)-Algebras

The aim of this paper is to apply the concept of L-fuzzy sets (LFSs) to UP (BCC)-algebras and introduce five types of LFSs in UP (BCC)-algebras: L-fuzzy UP (BCC)-subalgebras, L-fuzzy near UP (BCC)-filters, L-fuzzy UP (BCC)-filters, L-fuzzy UP (BCC)-ideals, and L-fuzzy strong UP (BCC)-ideals. Also, we study the characteristic LFSs, t-level subsets, and the Cartesian product of LFSs in UP (BCC)-algebras.
Optimal artificial neural network configurations for hourly solar irradiation estimation

&lt;span lang="EN-US"&gt;Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.&lt;/span&gt;
Breast cancer classification with histopathological image based on machine learning

&lt;span lang="EN-US"&gt;Breast cancer represents one of the most common reasons for death in the worldwide. It has a substantially higher death rate than other types of cancer. Early detection can enhance the chances of receiving proper treatment and survival. In order to address this problem, this work has provided a convolutional neural network (CNN) deep learning (DL) based model on the classification that may be used to differentiate breast cancer histopathology images as benign or malignant. Besides that, five different types of pre-trained CNN architectures have been used to investigate the performance of the model to solve this problem which are the residual neural network-50 (ResNet-50), visual geometry group-19 (VGG-19), Inception-V3, and AlexNet while the ResNet-50 is also functions as a feature extractor to retrieve information from images and passed them to machine learning algorithms, in this case, a random forest (RF) and k-nearest neighbors (KNN) are employed for classification. In this paper, experiments are done using the BreakHis public dataset. As a result, the ResNet-50 network has the highest test accuracy of 97% to classify breast cancer images.&lt;/span&gt;
Concept drift and machine learning model for detecting fraudulent transactions in streaming environment

&lt;span lang="EN-US"&gt;In a streaming environment, data is continuously generated and processed in an ongoing manner, and it is necessary to detect fraudulent transactions quickly to prevent significant financial losses. Hence, this paper proposes a machine learning-based approach for detecting fraudulent transactions in a streaming environment, with a focus on addressing concept drift. The approach utilizes the extreme gradient boosting (XGBoost) algorithm. Additionally, the approach employs four algorithms for detecting continuous stream drift. To evaluate the effectiveness of the approach, two datasets are used: a credit card dataset and a Twitter dataset containing financial fraud-related social media data. The approach is evaluated using cross-validation and the results demonstrate that it outperforms traditional machine learning models in terms of accuracy, precision, and recall, and is more robust to concept drift. The proposed approach can be utilized as a real-time fraud detection system in various industries, including finance, insurance, and &lt;br /&gt; e-commerce.&lt;/span&gt;
THE DEVELOPMENT OF INQUIRY METHOD BASED EXPLANATORY TEXT WITH PISA FRAMEWORK FOR JUNIOR HIGH SCHOOL

Research and development of a module for reading explanatory texts based on inquiry methods and PISA standardized questions has been carried out in class VIII SMP N 52 Palembang. This study aimed to produce reading modules that suit the needs of teachers and students. The research method used was development research with the steps of research on the development of the Jolly and Bolitho and Dick, Carey Carey models which include identification of needs, exploration of material needs, contextual realization, production of materials, expert validation, one to one evaluation, small group and field tests. Data was collected through questionnaires, interviews, validation sheets, and tests. The results showed that the feasibility aspect of content and presentation obtained a percentage of 84% in the very good category, linguistic feasibility obtained a percentage of 90% and was categorized as very good, graphics obtained a percentage of 76% in the good category, validation questions received a percentage of 85% in the very good category. Practicality in the one-to-one evaluation stage and the small group gets a percentage of 80% in the practical category. The effectiveness measured in the field test is 0.49 and is in the medium category. Thus, the product in the form of a module for reading explanatory texts based on inquiry methods and PISA standardized questions can be used in learning explanatory texts.
Application of Markov Chain to Prediction Poverty in Banten Province

The number of poor people in Banten Province is in the third lowest position in Java Island under the Special Region Province of the Capital Jakarta and Yogyakarta Special Region Province in 2018-2020 until finally it is in the second lowest position in 2021. However, this does not mean that the problem of poverty is no longer a top priority. This study aims to apply the Markov chain in predicting poverty in Banten Province. According to Marli et al. (2018) The Markov chain is a method that studies the properties of a variable in the present based on its past properties to estimate the properties of these variables in the future. In this research, the type of research used is applied research and used secondary data sourced from the Central Statistics Agency (BPS) Banten Province. The poverty prediction results for Pandeglang Regency in 2022, 2023 and 2024-2025 will increase by 2%, 0.46%, and 0.02%, respectively. Lebak Regency in 2022 will increase by 2%, in 2023 and in 2024-2025 it will decrease by 0.66% and 0.01%, respectively. Tangerang Regency in 2022 will decrease by 4%, in 2023 it will increase by 0.99%, and will fall back in 2024-2025 by 0.01%. Serang Regency in 2022 will increase by 1%, in 2023-2025 it will decrease by 0.83%. Tangerang City in 2022 remains, in 2023 and 2024-2025 it will increase by 0.53% and 0.01%, respectively. The city of Cilegon in 2022 remains, in 2023 it will increase by 0.18% and 2024-2025 will decrease by 0.01%. Serang City in 2022 remains, in 2023-2025 it will decrease by 0.71%. South Tangerang City in 2022 will decrease by 1%, in 2023-2025 it will increase by 0.04%. The steady state probability of Pandeglang Regency is 17.48%, Lebak Regency is 17.33%, Tangerang Regency is 27.98%, Serang Regency is 10.17%, Tangerang City is 15.54%, Cilegon City is 2.17%, Serang City is 5.29% and South Tangerang City is 4.04%..
The impact of changing environment on undergraduate mathematics students’ status

This paper focuses on the impact of changing environment on undergraduate mathematics students’ status, described through their engagement, participation, and motivation levels. These parameters were computed through a fuzzy cognitive map, which gathered data from a situation-aware e-learning platform. The main goal is to analyze the students’ reaction to a long-term emergency caused by the COVID-19 pandemic. A mixed-methods case study was conducted at University of Salerno to evaluate how completely remote teaching for the second year influenced the student’s status. The results show that distance learning and other social factors decrease university mathematics students’ motivation, engagement, participation, and overall performance in the long term, despite the countless teaching strategies implemented, the consolidated combination of mathematics and technology, and the use of a situation-aware e-learning platform.
The Decomposition of a Finitely Generated Module over Some Special Ring

This research aims to give the decompositions of a finitely generated module over some special ring, such as the principal ideal domain and Dedekind domain. One of the main problems with module theory is to analyze the objects of the module. This research was using a literature study on finitely generated modules topics from scientific journals, especially those related to the module theory. And by selective cases we find a pattern to build a conjecture or a hypothesis, by deductive proof, we prove the conjecture and state it as a theorem. The main result in this study is the decomposition of the finitely generated module is a direct sum of the torsion submodule and torsion-free submodule. Since the torsion-free module is always a free module over a principal ideal domain, then the torsion-free submodule is a free module. Last, we generalize the ring, from a principal ideal domain, to a Dedekind domain. We found then the torsion-free submodule became a projective module. Then the decomposition of the finitely generated module is a direct sum of the torsion submodule and the projective submodule. These results should help the researchers to analyze the objects on module theory.
Ensembling techniques in solar panel quality classification

&lt;p&gt;&lt;span lang="EN-US"&gt;Solar panel quality inspection is a time consuming and costly task. This study tries to develop as reliable method for evaluating the panels quality by using ensemble technique based on three machine learning models namely logistic regression, support vector machine and artificial neural network. The data in this study came from infrared camera which were captured in dark room. The panels are supplied with direct current (DC) power while the infrared camera is located perpendicular with panel surface. Dataset is divided into four classes where each class represent for a level of damage percentage. The approach is suitable for systems which has limited resources as well as number of training images which is very popular in reality. Result shows that the proposed method performs with the accuracy is higher than 90%.&lt;/span&gt;&lt;/p&gt;
Emotion detection on social media status in Myanmar language

&lt;span lang="EN-US"&gt;Many social media emerged and provided services during these years. Most people, especially in Myanmar, use them to express their emotions or moods, learn subjects, sell products, read up-to-date news, and communicate with each other. Emotion detection on social users makes critical tasks in the opinion mining and sentiment analysis. This paper presents the emotion detection system on social media (Facebook) user status or post written in Myanmar (Burmese) language. Before the emotion detection process, the user posts are pre-processed under segmentation, stemming, part-of-speech (POS) tagging, and stop word removal. The system then uses our preconstructed Myanmar word-emotion Lexicon, M-Lexicon, to extract the emotion words from the segmented POS post. The system provides six types of emotion such as surprise, disgust, fear, anger, sadness, and happiness. The system applies naïve Bayes (NB) emotion classifier to examine the emotion in the case of more than two words with different emotion values are extracted. The classifiers also classify the emotion of the users on their posts. The experiment shows that the system can detect 85% accuracy in NB based emotion detection while 86% in recurrent neural network (RNN).&lt;/span&gt;
Formation of Non-Perfect Maze Using Prim’s Algorithm

Maze is a place that has many paths with tortuous paths that are misleading and full of dead ends and can be viewed as a grid graph. A non-perfect maze is a maze that has a cycle. This research produces an algorithm that can form a non-perfect maze with a size of m×n which has two types of bias. The first bias is the composition of the percentage of horizontal and vertical partitions. The second bias is the percentage of the number of cycles. The algorithm created in this study was generated by modifying Prim’s algorithm and the use of Fisher-Yates algorithm which is used in random selection in Prim’s algorithm. The non-perfect maze algorithm begins with the calculation of the parameter values of the two types of bias and continues with forming a perfect maze and ends with forming a non-perfect maze. The algorithm that has been designed can form a non-perfect maze with a complexity of O(|E|^2), where E is the set of edges of an m×n grid graph. Flash-based application development is also carried out in order to implement algorithms to obtain a non-perfect maze. The non-perfect maze is produced in a two-dimensional visual form in the form of an image along with its corresponding grid graph. The application is capable of displaying up to the first 20 solutions of the biased maze.