Reviewerly/exHarmony huggingface dataset:
THE USE OF AUTHENTIC ASSESSMENT IN ENHANCING STUDENTS’ READING COMPREHENSION IN AN ONLINE LEARNING OF ENGLISH LITERATURE

This study aimed at investigating the use of authentic assessment in improving students' reading comprehension in online learning for English Literature courses. This study used a qualitative approach with the type of case study. The research data were online class observations (non-participant observations) and interviews (semi-structured). The participants in this study were selected purposively consisting of lecturers of English education at a university in West Java. The results of class observations showed that lecturer used authentic assessment in online learning for English Literature courses.The lecturer use text-based teaching methods and also some references from journals; and used learning media through WhatsApp groups Zoom Meetings. It showed that the use of authentic assessment can improve students' reading comprehension through evidence of responses to students’ written literary works as a form of presentation task.Keywords: Authentic Assessment, Reading Comprehension, English Literature
Development and Assessment of Feed Forward Back Propagation Neural Network Models to Predict Sunshine Duration

The duration of sunshine is one of the important indicators and one of the variables for measuring the amount of solar radiation collected in a particular area. Duration of solar brightness has been used to study atmospheric energy balance, sustainable development, ecosystem evolution and climate change. Predicting the average values of sunshine duration (SD) for Duhok city, Iraq on a daily basis using the approach of artificial neural network (ANN) is the focus of this paper. Many different ANN models with different input variables were used in the prediction processes. The daily average of the month, average temperature, maximum temperature, minimum temperature, relative humidity, wind direction, cloud level and atmospheric pressure were used as input parameters in order to obtain the daily average of sunshine duration (SD) as the output. The eight-year data were divided into two categories. The first category covers whole years (annually) and the second category is seasonal. To recognize and assess the influence of different input parameters on sunshine duration, six models of ANN have been evolved. The findings showed that in the annual models, the outcomes of RMSE, MAE and R for the model with input parameters (Month, Cloud Level and Average Temperature) were the best results 1.82, 1.175 and 0.89, respectively. As for the season models, the outcomes of RMSE, MAE and R for the autumn season were the best results 1.450, 1.009 and 0.94, respectively. Accordingly, the performance of the artificial neural network is considerably effective in predicting the sunshine duration.
Prediksi Indeks Harga Konsumen Komoditas Makanan di Kota Surabaya menggunakan Support Vector Regression

In data mining, predictions are known to find knowledge about what will happen in the future. Predictions are usually made on time-series data. The Consumer Price Index (CPI) is an index value derived from daily consumer price data. The results of the CPI calculation are derived from observations of commodity prices at the household consumer level, which are carried out routinely on a daily, weekly, bi-weekly, and monthly basis. CPI prediction can be done using a data mining algorithm, namely Support Vector Regression (SVR). SVR is part of the Support Vector Machine algorithm that functions to solve regression cases. SVR is a reliable algorithm in the case of regression because it can handle data overfitting well. The data used as input in this paper comes from 34 food commodity prices, and the output data is obtained from the CPI value data. The food commodity price data used is from Surabaya City. The data period used is from 2014-2020. The results of the implementation of SVR with 4 kernels show that the Polynomial kernel has the best error rate with a MAPE value of 4.31%.
Penerapan Metode Extreme Gradient Boosting (XGBOOST) pada Klasifikasi Nasabah Kartu Kredit

Bad credit card is a problem of inability of credit card users to pay credit card bills that can cause losses to both parties concerned. In order to avoid losses caused by bad credit cards, the provider must conduct a careful analysis of prospective or old customers using credit cards. This study aims to classify bad credit card customers using machine learning techniques, namely classification techniques. One of the classification techniques used is the XGBoost method which is useful for regression analysis and classification based on the Gradient Boosting Decision Tree (GBDT), the XGBoost method has several hyperparameters that can be configured to improve the performance of the model. Hyperparameter tuning method used is grid search cross validation which is then validated using 10-Fold Cross Validation. XGBoost hyperparameters configured include n_estimators, max_depth, subsample, gamma, colsample_bylevel, min_child_weight and learning_rate. Based on the results of this study proves that the use of algorithms with hyperparameter tuning can improve the performance of eXtreme Gradient Boosting algorithm in the process of classification of credit card customers with an accuracy of 80.039%, precision of 81.338% and a recall value of 96.854%.&#x0D; &#x0D; Keywords: XGBoost, classification, Accuracy, Precision, Recall
Global Decision Making Over Deep Variability in Feedback-Driven Software Development

To succeed with the development of modern software, organizations must have the agility to adapt faster to constantly evolving environments to deliver more reliable and optimized solutions that can be adapted to the needs and environments of their stakeholders including users, customers, business, development, and IT. However, stakeholders do not have sufficient automated support for global decision making, considering the increasing variability of the solution space, the frequent lack of explicit representation of its associated variability and decision points, and the uncertainty of the impact of decisions on stakeholders and the solution space. This leads to an ad-hoc decision making process that is slow, error-prone, and often favors local knowledge over global, organization-wide objectives. The Multi-Plane Models and Data (MP-MODA) framework explicitly represents and manages variability, impacts, and decision points. It enables automation and tool support in aid of a multi-criteria decision making process involving different stakeholders within a feedback-driven software development process where feedback cycles aim to reduce uncertainty. We present the conceptual structure of the framework, discuss its potential benefits, and enumerate key challenges related to tool supported automation and analysis within MP-MODA.
Penerapan Metode Forward Chaining Pada Sistem Pakar Deteksi Mutu Tepung Terigu Berbasis Web

Application of the Expert System to Maintain the Quality of wheat flour by conducting a consultation process with the application, previously this process was controlled by one person, namely the QC (Quality Control) Staff, QC (Quality Control) Staff giving instructions to their subordinates on how to maintain the quality of wheat flour, because supervision and giving orders is only carried out by people who are not experts, this creates problems, if the person in charge is not in place. For that we need an application that can overcome the problems above, because it is impossible to postpone the determination of the quality of wheat flour just because the person responsible for the process of maintaining the quality of wheat flour is not in place. This application is made using an expert system with the consideration that an expert is needed in the form of an application as an instructor on the mechanism for maintaining the quality of wheat flour, by storing the expert knowledge base into an application.&#x0D; &#x0D;
Identifikasi Senjata Tradisional Mandau Suku Dayak Menggunakan Metode Support Vector Machine

Mandau is a sharp weapon like a machete that comes from the culture of the Dayak tribe in Kalimantan. Mandau itself has many types of carvings with different motifs in each typical Dayak tribe. The diversity of Dayak clumps and sub-tribes produces various types of Mandau, which are similar. Most Indonesians cannot distinguish between the existing variety of Mandau because of the information lack about Mandau. The study aims to identify Mandau types by applying Support Vector Machine (SVM) methods according to shape and texture feature extraction of image Mandau. Process of feature extraction using eccentricity, metric, contrast, correlation, energy, and homogeneity parameters. Six parameters obtained from the image feature extraction were used for the classification process. There are four kind of Mandau that used, such as Mandau Benuaq, Mandau Kenyah, Mandau Mahah, and Mandau Tunjung, with each Mandau 24 images, and the total data is 96. The implementation of the SVM method in identifying the type of Mandau using Linear Kernel, Gaussian / RBF Kernel, and Polynomial Kernel. The results indicated that when SVMs were used to identify Mandau images in split data, 80% of the training and 20% of the test data had the highest average accuracy of 82%. Based on the type of Kernel at 80:20 data split, the identification of the Mandau image using the Polynomial Kernel has the highest accuracy rate of 95%.
Aplikasi Algoritma Kruskal dan Sollin Pada Jaringan Transmisi Nasional Provinsi Sulawesi Selatan

Graph theory is one part of the branch of mathematics that is now very famous in computer design. The substitution of a graph is an object statement referred to as a noktah, circle or point, and the object relationship is referred to as a line. Graph is applied in various scientific disciplines, for example: economics, genetic psychology, operation research and network optimization (telecommunication network, PLN transmission (electricity network, PDAM water distribution network). Kruskal and Sollin's algorithm have some similarities in their use for solving the problem of trees stretching the minimum from the smallest to the largest amount in each weight. In the article discussing the minimum range tree, especially kruskal and sollin algorithms, and applied more specifically to the national transmission network of South Sulawesi Province, then the network can be obtained optimally.
Community Centered Mitigation Based on Science Literature to Reduce The Risk of Disaster in Indonesia

Disasters are a potential threat to the lives of the Indonesian people. The trend of disasters in Indonesia is increasing regarding the number, type, impact, and complexity of the problem. All of that burdens individuals, communities, nations, and countries. Reducing this burden remains a common challenge. A simple activity to reduce the burden is to carry out risk reduction and disaster mitigation activities involving various stakeholders. Community-based mitigation activities utilizing science literacy can be an alternative model of solutions to overcome this.
Application of Online Learning Methods in Indonesian Language Subjects at SD Nurul Falah Sibolga Case Study Class V

This research is motivated by the low learning outcomes of the fifth grade Indonesian students of SD Nurul Falah. This is 40% of students with perfect criteria, as you can see from the daily test results of students with 60% incomplete criteria. These results reveal that the fifth grade students of SD Nurul Falah still have low learning success, especially in Indonesian subjects. Educators play a key role in determining student learning outcomes. Observations show that due to the current learning of the WhatsApp application, some students have poor learning outcomes, do not have mobile phones, some are lagging behind in learning, and others do not understand much. I don't understand the dated material sent by the educator via WhatsApp. This research is a qualitative field research that aims to explain the application of online learning methods in Indonesian class V subjects at Nurul Falah Elementary School in the 2021 academic year. The learning method here uses the whatsapp grub application, the subject of this research is class V students, totaling 20 students. Data collection techniques used are observation, interviews, and documentation. the online learning method for Indonesian subjects in class V is successful. This is evidenced by educators who motivate their students and their parents as their study partners. Exit activity in WhatsApp group. B. Publish the researched material and provide a description of the assignment assignment material collected.
Penerapan Model Klasifikasi K-Nearest Neighbor Dalam Pencarian Kesesuaian Pekerjaan

The application of job suitability is very important for companies/institutions in seeing the advantages of job applicants in accordance with their fields. then mobile-based information technology in finding job vacancies for applicants is very much needed for the required vacancies information and the suitability of the required formations. so that applicants can easily see the jobs that applicants need. The focus of this research problem is the existence of a mobile application in classifying job vacancies and the existence of a job information search menu according to the skills possessed and in accordance with the needs of mobile application-based jobs using the k-nearest neighbor (KNN) model. The purpose of this research is to apply a freelance application in aceh to help job search information according to the skills they have that match the needs of the job. The criteria available in the mobile application consist of a list of jobs, self-registration, a list of self-data weights, criteria and weights for recent education, work experience, marital status. The study was conducted with test data used 25 samples and applicants who registered consisted of 30 participants. the test results for the first registrant with the results of age 1, gender 0.5, GPA, 0.8, work experience 0.25, marital status 1 and passed with the value of the KNN calculation result is 0.406, the second applicant passed with a value of 0, 89 and the third did not pass with a value of 0.392. Job vacancies are obtained by taking 11 training data pieces that have the closest Euclidean distance to the questionnaire results. Furthermore, to test the accuracy using 30 samples with 24 accurate results and 6 inaccurate results.
Penentuan Bantuan Langsung Tunai (BLT) Dampak Covid-19 Menggunakan Metode Weighted Product

Direct Cash Assistance (BLT) is a program made by the government in the form of assistance as a form of government responsibility in reducing the burden on the community due to the impact of the COVID-19 pandemic who have lost their livelihoods. The process of determining the recipients of BLT assistance is still carried out manually so that it requires a fairly longtime span in the implementation of its determination and is not objective in determining the recipients of BLT because it is more inclined to one or more residents, which results in inaccurate BLT recipients, this problem occurs in the community the poor, Tridayasakti Village, South Tambun, Bekasi Regency, West Java Province. To overcome this problem, the researcher uses a Decision Support System to determine BLT recipients with the Weighted Product method, namely by assigning a weight value to each criterion, the calculation process and a ranking process are carried out so that the Tridayasakti Village Head can decide correctly, which residents are really entitled to get direct cash assistance (BLT). The results obtained from the data that have been collected will be processed and then processed using the Weighted Product method which aims to produce information to be used as a guide in determining BLT recipients. It is hoped that using this method in determining recipients of direct cash assistance (BLT) is more effective, efficient and accurate.
HMRFLR: A Hybrid Model for Sentiment Analysis of Social Media Surveillance on Airlines

Abstract Social Media is a major part of human life in the current era. People posts their regular activities, self-indulgent feelings, and real-life experiences on various platforms such as Twitter, Instagram, Facebook, YouTube etc. For social media surveillance, Twitter is considered to be the most widely used platform (about 64%). Twitter data is a valuable method to gather tweets and analyses of user perspectives. Along with the other industries the airline industry also wants to be up to date and keep its sectors alive having the current scenario. Airlines use traditional types of customer feedback that are very common and require a great deal of time. To minimize the problems, the analysis of feelings is considered to be a mandatory approach. After the pandemic when traveling is again resuming and flights are finally taking off, the airline industry is also giving its best to keep in touch with their customers more than ever. The dynamic evolution of the airline industry is commendable over the last decade. Millions of people share their experiences related to different airline companies every day where happy customers are posting their pictures with clouds and staff, some angry customers are complaining about bad services and difficulties they faced like missing baggage, delayed flights, changes in boarding schedules, an IT system failure, etc. This kind of real-time feedback not only helps the passengers to decide which flight they have to choose but also helps the management team and staff of airlines to analyze the situation and take immediate action regarding it to improve their services for passengers’ better experience. In the research paper, a hybrid model composed of Machine Learning algorithms including the classifiers of Random Forest and Logistic Regression named as HMRFLR is proposed to analyze the tweets of Airlines in the US for categorization of the posts according to positivity, neutrality, and negativity of the posts. For revealing the current level of customer satisfaction towards the airlines, sentiment analysis is undertaken. This hybrid model achieves a better accuracy score of 88.16%, however, the individual accuracy score of Logistic Regression is 79.1% and Random Forest is 76.87% respectively.
Impact of Covid-19 on Freedom of Expression in Morocco and Tunisia

With the onset of the Covid-19 pandemic, many countries declared a state of emergency to cope with the crisis, including countries in the Middle East and North Africa (MENA) region. This paper explores the state of emergency’s impact on the practice of rights and freedoms in the region, with a focus on freedom of opinion and expression in two North African countries, Morocco and Tunisia. In these two countries, constitutional and rights-related developments have unfolded over the last few years, especially with the 2011 Constitution in Morocco and the 2014 Constitution in Tunisia. Constitutional and rights-related developments are evaluated within the framework of the state of emergency declared in both countries during the Covid-19 crisis. This study sought to determine the extent to which the state of emergency impacted rights and freedoms in Morocco and Tunisia, with a focus on freedom of expression. The results show that both countries are still lacking in terms of constitutional protections for free expression, and for fundamental rights and freedoms in general. The systems of governance in Morocco and Tunisia, especially their executive authorities and security apparatuses, still engage in authoritarian practices and do not shy away from exploiting any extraordinary or exceptional situation to further consolidate their power and reach.
FORMATION OF PRIMARY SCHOOL LEARNERS’ MEDIA LITERACY SKILLS IN THE LESSONS OF LANGUAGE AND LITERATURE BRANCH WHILE WORKING WITH MEDIA TEXTS

The article describes the theoretical aspects of the formation of primary school pupils’ media literacy skills, caused by the use of media in the educational space of the New Ukrainian School. The components of media literacy of primary school learners are defined and characterized: aesthetic and creative skills; interactive skills; critical analysis skills; cyber security skills. Media literacy in primary education is considered within the field of language and literature educational branch and is determined by a separate content line “Exploring media”. The authors of the syllabi (O. Savchenko and R. Shyian) reveal such a content line by acquainting junior pupils with the basics of digital literacy and forming learners’ ability to analyze, interpret, critically evaluate information in media texts, create their own media products and use them to enrich their own experience. It has been found that in the syllabi for grades 1–4 of the New Ukrainian school as the basis for the formation of primary school learners’ media literacy skills is defined the work with media texts differing from traditional texts in terms of channels of perception and their analysis through audio, visual and audiovisual media. It has been investigated that in the lessons of the language and literature educational branch the work with media texts is carried out in three ways. The first way involves the development of pupils’ speech activity (listening comprehension, reading, speaking and writing) by means of media texts. The second way covers the perception, analysis and interpretation of media texts, the development of junior pupils’ skills to find information of interest to them, describe their impressions of a media text, express their own opinion about events, phenomena and values, explain their influence on their own perception, as well as the formation of learners’ evaluative attitude towards the content and form of media texts (their real character, purpose of creation and influence on the audience). It has been proven that learners’ critical thinking skills become especially relevant when working with media texts. The third way is the creation and presentation by junior pupils of their own simple audio, visual and audiovisual media texts according to the purpose and addressee. Pedagogical conditions for the formation of primary school learners’ media literacy skills in the lessons of language and literature educational branch are set: the teacher’s readiness to introduce media education into the educational process of primary school; partnership between school and family; organization of various forms of work with media texts and their systematic use in the educational process of primary school.
Determinants to be considered while investing in cryptocurrency markets: A case of bitcoin

When Bitcoin became one of the world's most popular investment options, the cryptocurrency industry has showed potential development, and it had a similar influence on the Indian financial sector too.Aside from Bitcoin, other altcoins are gaining popularity and dominating the cryptocurrency market.As a result, the goal of this research is to identify at the macroeconomic factors that influence Bitcoin prices, such as the USD/INR exchange rate, gold prices, crude oil prices, the New York Stock Exchange Dow Jones (NYSE) price, NIFTY price, and Sensex price, as well as the prices of nine alternative cryptocurrencies in the cryptocurrency market: Binance coin, Bitcoin Cash, Bitcoin SV, Ether, Ethereum, Litecoin, Monero, Tether, and ripple.Bitcoin volume and market capitalization are additional factors, undertaken in the study, that are potential influencers of cryptocurrency pricing.The time series data, which comprises of bi-weekly data for all variables, will be used from 2015 to 2020.The OLS (ordinary least square) regression model in EVIEWS will be used in this study to conduct an empirical analysis.