Reviewerly/exHarmony huggingface dataset:
OTOMATISASI PEKERJAAN GURU BERBASIS AUTOCRAT: MODEL PENDIDIKAN DAN PELATIHAN BERDASARKAN PENDEKATAN KONEKTIVISME

In addition to having an obligation to teach students in the classroom, teachers also has obligation to carry out administrative tasks to involve students outside the classroom. If implemented using conventional methods, this will complicate the effectiveness of the teacher's work. The purpose of this study is to develop a connectivity-based education and training model related to the automation of teacher work in the digital era through the use of the Autocrat Add-on in Google Sheets. The development of the model began by conducting a needs analysis of 59 teacher respondents from various parts of Indonesia to review the technical problems being faced, the need for problem-solving solutions, an initial understanding of the work support tools of the Autocrat Add-on and the expected output from the use of the Autocrat Add-on. The findings in this study can be used as a basis for carrying out teacher professional development activities in the digital era, especially related to the automation of teachers' work through the use of Ad-on Autocrats in order to make their work more efficient and effective.
AUTHOR’S REFLECTIONS ON PERSON AND PERSONALITY IN C.S. LEWIS’ «THAT HIDEOUS STRENGTH»

The article is dedicated to the problem of author’s reflections on person and personality in the literary texts of the famous British writer of the twentieth century C.S. Lewis. The main philological theories of the phenomenon of author’s reflections in the literary text are considered. Author’s reflections in a literary text, as a rule, are aimed at finding the causes and meaning of the events depicted, may contain a detailed author’s assessment of the character of the hero, his actions, situation, or they may prescribe the ethical necessity of the hero’s actions. The author’s reasoning about a person can model a conditional situation: describe how the hero would act under certain circumstances. The author’s reflections on anthropological themes in the fairy tale for adults “That Hideous Strength” are analyzed. C.S. Lewis’s reflections on man in “That Hideous Strength” are considered and classified from the point of view of their communicative function. Some aspects of translation of the author’s reflections in “That Hideous Strength” into Russian are discussed. The article attempts to comprehend the role of the author’s reflections on man in the literary anthropology of C.S. Lewis’s imagery.
THE STUDENT'S PERCEPTION OF REWARDS TO INCREASE THEIR MOTIVATION IN ENGLISH LEARNING IN JUNIOR HIGH SCHOOL

The reward is a form of appreciation from the student pursuer to provide an inner emotional connection. The reward is a strategy in learning that functions as a stimulus and response to students in learning English effectively. The type of reward given to students varies greatly depending on the needs of students in learning, ranging from praise, grades, and nonverbal. All the variations of rewards in learning have a common goal of increasing student motivation in learning. This study aims to determine students' perceptions of reward strategies to increase motivation to learn English in junior high school. The respondents in this study were class VIII-A students at MTsN 8 Kediri for the 2021/2022 academic year. The number of respondents was 33 students in the bilingual class. Meanwhile, the research method used is a qualitative case study using questionnaires and interviews to obtain accurate data. This study shows that students give positive perceptions about learning to use rewards in increasing learning motivation. There are four kinds of rewards often used in learning English at MTsN 8 Kediri.
Pengaruh Playstation Terhadap Prestasi Belajar Siswa Bidang Studi Aqidah Akhlak Kelas IV Di Madrasah Ibtidaiyah Muhammadiyah 03 Weru

In this study the author uses quantitative methods, while for data collection using questionnaires, documentation, observation, while the population is the entire number of fourth grade students at MI Muhammdiyah 03 Weru-Paciran, totaling 35 students and 25 being taken as samples. To analyze the final results the author uses the Product Moment formula with the formula: rxy= (∑xy)/√((∑x^2 )(∑y²)).&#x0D; In this study, the results obtained from data analysis using the Product Moment formula which the results reached 1.0208 were rounded up to 1. So the results and conclusions can be obtained that playing Playstation games is very influential on student achievement in the Aqidah Akhlak class IV study at MI Muhammdiyah 03 Weru-Paciran.
IDENTIFIKASI MASALAH PELAKSANAAN BELAJAR DARI RUMAH DI MASA PANDEMI COVID-19 PADA MATA PELAJARAN MATEMATIKA KELAS III SDN 2 BATUJAI

Implementation of Learning From Home (BDR) is one alternative or action taken to continue to provide children's learning rights. With the implementation of learning with the BDR method, there are several problems in its implementation. The problem raised in this study is to identify the problem of implementing learning from home during the Covid-19 pandemic in Class III Mathematics at SDN 2 Batujai. The purpose of this study was to find out what are the problems with implementing learning from home during the covid-19 pandemic in class III mathematics at SDN 2 Batujai. The results of this study are expected to be used theoretically for teachers, for researchers, and for other researchers. The type of research used in this research is descriptive quantitative. The sampling technique used in this research is Nonrobability Sampling with saturated sampling type, where the sample uses all members of the population. The data collection technique in this study was the researcher using observation, interview and documentation techniques. The results of this study indicate that the withdrawal of the hypothesis based on the SPSS data table is Ha is accepted and Ho is rejected with a sig value. 0.031 &lt; 0.05. Then there is the problem of implementing learning from the Covid-19 pandemic in class III mathematics subjects at SDN 2 Batujai.
Compression and Decompression of Internet Learning Images Based on GABTC

Internet Learning Image compression is a useful approach this is inevitable to save any snapshots in compressed sample. The compressed image is reconstructed the usage of image retrieval system for any applications. This method guarantees garage performance in various content management packages. Mainly, Internet Learning resource environment deals with greater area complexity. The e-learning storage area complexity may be reduced with the assist of image compression strategies. There are extraordinary forms of image compression techniques invented for constructing compressed assets. Block Truncation Coding (BTC) is a kind of lossy compression technique for reducing grey scale quantities the usage of blocking and quantizing stages. This is powerful technique for compressing the photos. In the scope, the want for extra active and Generative image compression methods is important to enhance BTC principles. This work proposes notably complex and novel Generative Adversarial BTC (GA-BTC) compression models with more than one variances. GABTC is developed with multi-layered Deep Neural Network (DNN) structures with GA neural models. The integration of each GA fashions and BTC standards improve the best of block constructions and reconstructions notably. The proposed work evaluates the version complexity and efficiency using diverse Internet Learning images with exclusive compression fine measurements.
Naive Bayes and Support Vector Machine Algorithm for Sentiment Analysis Opensea Mobile Application Users in Indonesia

Opensea is an NFT buying and selling application-based platform that is booming in the community. One way to find out the public's perception of the Opensea application is by sentiment analysis, as done in this study. Data that is used is user review data for the Opensea application in the Indonesian play store. The sentiment analysis technique used is the Naïve Bayes Classifier and the Support Vector Machine (SVM) method. Both are used to compare public responses from sentiment analysis of reviewed data labeled as positive, negative, and neutral. Based on this study, it was found that the Naive Bayes algorithm gives the results that class precision is 87.31%, class recall is 71.02%, and accuracy is 89.81%. While the SVM algorithm gives the results that class precision is 94.23%, class recall 71.96%, and Accuracy 90.78%. It is concluded that the SVM algorithm has a better performance than the Naive Bayes algorithm.
EFEKTIVITAS PENGGUNAAN MEDIA PEMBELAJARAN BERBASIS MACROMEDIA FLASH DALAM MENINGKATKAN HASIL BELAJAR MATEMATIKA SISWA 

This study aims to determine the mathematics learning outcomes students of class X MA Pondok Pesantren Darul Qur'an material linear equations before and after learning using Macromedia Flash learning media. The sample selection was through random sampling, the instrument used was in the form of tests conducted twice, namely before learning (pre-test) and after learning (post-test). The results of the data analysis requirements test, it is known that student learning outcomes are declared to be normally distributed and have variance homogeneous. The results showed that there were differences in students' mathematics learning outcomes before using macromedia flash learning media with an average of 57.1 and after using macromedia flash learning media, which was an average of 77.3. The results of data analysis using t-test showed there was a significant difference in improving students' mathematics learning outcomes. It can be concluded that the hypothesis is accepted where Macromedia Flash learning media is effectively used to improve students' mathematics learning outcomes in class X MA Pondok Pesantren Darul Qur'an in linear equation material.
Application of machine learning algorithms in predicting pyrolytic analysis result

Introduction. Geochemical studies of organic matter in oil source rocks play an important role in assessing oil and gas accumulation in any territory. These studies play a particularly important role in forecasting unconventional resources and oil and gas reserves (so-called shale hydrocarbons). It is recommended to carry out pyrolytic studies by the Rock-Eval method for rocks saturated with organic matter on samples before and after their extraction with chloroform. However, extraction is a laborious and time-consuming process, and the load on laboratory equipment and the time required for analysis is doubled. Aim. To get a working model for predicting pyrolytic parameters of extracted samples, without carrying out extraction analysis. Materials and methods. In this paper, machine learning regression algorithms are applied for predicting one of the pyrolysis parameters of extracted samples based on the pyrolytic analysis results of the extracted and non-extracted samples. To develop the prediction model, 5 different machine learning regression algorithms were applied and compared, including multiple linear regression, polynomial regression, support vector regression, decision tree, and random forest. Results. The prediction result showcases that the relationship between the parameters before and after extraction is complex and non-linear. Some methods have shown their incompatibility with the assigned tasks, others have shown good and satisfactory results. Those algorithms can be applied to predict all geochemical parameters of extracted samples. Conclusions. The best machine learning algorithm for this task is the Random forest.
THE EFFECTIVENESS ANALYSIS OF RANDOM FOREST ALGORITHMS WITH SMOTE TECHNIQUE IN PREDICTING LUNG CANCER RISK

When compared with other types of cancer, most of the population with cancer die from lung cancer.A person needs to do a screening test through X-rays, CT scans, and MRI to detect the disease. However, before carrying out the process, the doctor will ordinarily investigate a medical history and physical examination first to study the symptoms and possible risk factors for lung cancer. The lung cancer data set has a class imbalance that affects the performance of the random forest algorithm in predicting the risk of lung cancer. This study aims to employ the SMOTE technique to the random forest algorithm to increase accuracy in predicting lung cancer risk. In this research, data processing and analysis use the Python programming language. The test results show an accuracy value of 88% with an AUC value of 0.93. When employing the random forest method to forecast lung cancer risk, the SMOTE technique is useful in dealing with class imbalances in the data set.
Video Calling With Build-In Compiler

Abstract: In today's Internet world, many applications that were previously created on a desktop computer are now being created on the web. Many applications can be accessed anytime and anywhere easily using the Internet. Due to the pandemic, the job recruitment process has moved online via Zoom, Google Meet, and other video calling apps. But during every IT interview, the person who is giving the interview faces major problems such as one must share their screen via a video calling app and open a compiler to run the program given by the interviewer. If the student doesn’t have a compiler installed on his system, then they need to open a compiler in a web browser to compile the code. The purpose of this research is to design and develop a real-time code editor application using Web socket technology to help users collaborate during tech interviews. There is a feature in this application that allows video calling with a built-in compiler. Video calling with a built-in compiler is a web application that provides a workspace to write, perform, display the results of the code through the terminal, and collaborate with other users in real-time. It provides workspaces for creating, executing, and building source code, as well as for real-time collaboration, chat, and building terminals. This application supports C, C++, Java, and Python programming languages.
Accounting and Profit Management Records in Banking Companies During the COVID-19 Pandemic

A company’s management team is responsible for measuring its financial performance and assessing the operational needs for utilizing the company resources based on its ability to generate profits. The ongoing COVID-19 pandemic made it imperative for the banking sector to implement effective profit management strategies so that their financial position remains stable, considering that there are several government policies regarding the other types of funds. This study intended to examine the accounting and profit management practices applied by the banking sector companies in Indonesia during the COVID-19 pandemic. Using a qualitative descriptive method, the researchers extracted data from the online Indonesia Stock Exchange (IDX). A sample of 13 banking companies was selected. Data analysis was done using narrative study techniques based on theoretical assumptions by analyzing existing documents. The majority of banks studied in this research applied accrual-based accounting records because of their relationship to income recognition. Profit management was implemented to maintain financial performance and tax savings.&#x0D; Keywords: accounting record-keeping, profit management
A Review of Language Testing and Assessment in Online Teaching

In line with the increased importance of language testing and assessment in online learning, the paper sets out to present an overview of the landscape of online language testing and assessment based on available scholarship in global and Vietnamese contexts. Several benefits of online language testing and assessment are reported, including promotion of learner autonomy, evaluation of students&amp;rsquo; progress, and convenience. Methods of online language assessment encompass collecting evidences of learning and using rubrics. A number of problems with current language testing and assessment practices in an online environment are delineated, including cheating/plagiarism, preference for selected response items, lack of validity and reliability, and increased workload for teachers. Recommendations for relevant stakeholders including teachers and teacher education institutions to improve the quality of online language assessment are accordingly presented.
TRUST MODEL FOR CLOUD AND GRID ENVIRONMENT USING FUZZY LOGIC AND ARTIFICIAL NEURAL NETWORK

Cloud Computing is a technology which offers various on-demand services such as software, platform, and infrastructure required by the clients.Grid Computing is a process of allotting the resources required to solve complex problems.In cloud or grid environment the service providers provides the services/resources to the clients.In this regard the service providers need to maintain and manage trust for further proceedings.This research article proposed a trust model using Fuzzy Logic and Artificial Neural Network (ANN) applicable for both cloud and grid environment.The Fuzzy Logic is used to remove the impreciseness of the data.The decision derived by using Fuzzy Logic lies between zero (0) to one (1) that represents the continuous values, where 0 is the lowest and 1 is the highest value in decision making.In this article Fuzzy Logic is used to generate the trust value based on the fuzzy rules by accepting the given inputs of six parameters namely reliability, response time, fault tolerance, security, elasticity, and service level agreement (SLA).Then based on the generated trust value the service provider is allotted to the clients.Artificial Neural Network is a prediction model used to predict the trust value of the service provider based on the same six parameters mentioned as input for Fuzzy Logic.Based on the predicted trust value the service provider is allotted to the client.This article shows the experiments and results based on the Fuzzy Logic Model and Artificial Neural Netwok Model for the process of service provider allottment.The experiments and results worked on the dataset of 20000 instances collected from the authenticated source.
TUSK: Task-Agnostic Unsupervised Keypoints

Existing unsupervised methods for keypoint learning rely heavily on the assumption that a specific keypoint type (e.g. elbow, digit, abstract geometric shape) appears only once in an image. This greatly limits their applicability, as each instance must be isolated before applying the method-an issue that is never discussed or evaluated. We thus propose a novel method to learn Task-agnostic, UnSupervised Keypoints (TUSK) which can deal with multiple instances. To achieve this, instead of the commonly-used strategy of detecting multiple heatmaps, each dedicated to a specific keypoint type, we use a single heatmap for detection, and enable unsupervised learning of keypoint types through clustering. Specifically, we encode semantics into the keypoints by teaching them to reconstruct images from a sparse set of keypoints and their descriptors, where the descriptors are forced to form distinct clusters in feature space around learned prototypes. This makes our approach amenable to a wider range of tasks than any previous unsupervised keypoint method: we show experiments on multiple-instance detection and classification, object discovery, and landmark detection-all unsupervised-with performance on par with the state of the art, while also being able to deal with multiple instances.
The Highest Retail Price Checker for Web Based Medical Equipment and Necessities

In the business world that is in medical equipment and staples for customers, the availability and also the need for data is also very important. As time goes on, the data is getting more diverse, which makes various products that are uniform but have significant price differences. This triggers confusion for customers to choose a product, so a price checking application is needed. The purpose of this report is to create a price comparison website application that displays the best prices for medical equipment products and staples. The data collection methodology uses Ideation which collects related journals, books, and credible sources. The application design methodology uses the Waterfall method for web development and maintenance. Product data retrieval is done by using the HTML Web Scraping method. How to test the application is done by Alpha Testing and Beta Testing and also Checking Input Output with BlackBox Testing. From the results of data collection and Web Development PriceCheck provides benefits where customers can find out the best price for the product they are looking for which is also available at the right store.
PERANCANGAN SISTEM DIAGNOSIS GETARAN MOTOR MENGGUNAKAN JARINGAN SARAF TIRUAN PROPAGASI MUNDUR

Expert system design is an effective and sophisticated way of diagnosing a fault in a 12 kW DC Motor. This study aims to design an ANN system to determine damage to the motor. The research method uses spectrum data from the vibration analyzer which is collected based on different types of damage. The training data patterns from the spectrum characteristics to be used in the system, the goal is that the systems can recognize the patterns that have been made. The training data patterns that have been successfully recognized by the system are then tested. The results of training and ANN testing are quite good, with the greatest Cross-Entropy value of 9.94, having 0% error value, the largest Mean Square Error value 8.33e-6 and the smallest regression 0.998. A testing of 8 new spectrums resulted in accurate predictions.
Procedures for Assessing the Quality of Electronic Learning Resources Using Petal Diagrams

The concept of visualization of the results of expert evaluation of the quality of electronic learning resources is considered. Much attention is paid to petal diagrams and their use in the visualization process. The algorithm for calculating the area of the petal diagram and the influence of the order of parameters on the area of each petal are described. The criteria for assessing the quality of e-learning resources and their weights for each of the experts are presented. The roles of experts with weighting factors are shown. Complex indicators of quality of electronic educational resources for each expert are defined and the complex indicator for all experts is generalized. An algorithm for calculating the areas of sector petals, which can be used to calculate and evaluate the relative quality of ELR according to the relevant criteria is given. GeoGebra dynamic mathematics system was used to implement the method of determining a comprehensive assessment of the quality of ELR. The process of construction of the petal diagram in the system of dynamic mathematics GeoGebra with the given instructions is shown.