Reviewerly/exHarmony huggingface dataset:
@GURUKUMRD: the Greatest Way to Learn English Vocabulary

Social media has impacted teenagers all around the world to be aware of the benefits of mobile phones for acquiring information, entertainment, and edutainment during the previous decade. 21st-century learners often favour Instagram, which is a well-liked social networking platform. The researchers seek to examine how undergraduate students in Indonesia use @gurukumrd Instagram to learn English vocabulary. This study presents a case study of how students learn about learning activities through the @gurukumrd account. Braun and Clarke (2006) obtained data through semi-structured interviews as an instrument and evaluated it qualitatively using thematic analysis. The participants were students in the sixth and eighth semesters of one university in West Java, Indonesia. The findings of this study are; Students acquire knowledge independently, select vocabulary and pronunciation resources, learn in a short period of time through @gurukumrd, memorize the vocabulary, imitate the pronunciation of the vocabulary, and engage in daily dialogue using the vocabulary. This finding indicates that students learning vocabulary through the @gurukumrd Instagram account valued time flexibility, effectiveness, independent learning, and interest-based learning.
Digital tools for successful students’ business communication in non-linguistic higher education institutions

The article demonstrates ways of digital tools application for business interaction of students studying foreign languages in non-linguistic higher education institutions. The research rested on the principles of competence-based approach and ecosystem approach. The following investigating methods were used: the theoretical analysis of literature and empirical methods such as testing, pedagogical experiment, quantitative and qualitative analyzing results of the experiment. The purpose and algorithm of the work presented were determined by the main methodological principles of teaching a foreign language and the results planned in the Basic educational program of higher education of bachelor’s degree. The purpose of the study was to identify ways and methods of effective use of digital tools for developing business communication skills of students in non-linguistic universities. The possibilities of modern digital tools to provide successful students’ business communication s in the context of a foreign language environment were analyzed. The digital tools Miro, Nearpod, and Google Calendar were chosen for the work. The following statement was formulated. Methodically competent use of the digital tools would improve students’ skills to conduct a dialogue and create a monologue in a foreign language in accordance with the goals and conditions of business communication. The presented statement had been verified by empirical methods. The experimental and control groups of first-year undergraduate students were formed according to the level of their foreign language skills determined by the entrance testing. During the experiment, students were required to prepare and hold several conferences with presentations on certain issues, followed by discussions. Criteria were developed for evaluating monologues-presentations and subsequent discussions in the form of an exchange of dialogical statements. During the preparation and holding of conferences, the students of the experimental group used selected digital tools (Miro, Nearpod, Google Calendar). The students of the control group did not use these digital tools. As a result of the experiment, significant dynamics in the level of preparing and presenting the material among the students of the experimental group was noted. The obtained results allowed the authors to develop methodological algorithm for the effective use of digital tools in business interaction of students studying in non-linguistic higher education institutions
Comparative study of Euler's method and Runge-Kutta method to solve an ordinary differential equation through a computational approach

Euler’s and Runge-Kutta's methods are used to solve ordinary differential equations. Euler’s methods become appropriate method for solving the equations. When the steps are small, they give reasonably accurate results. However, if the steps are not so small, the Runge-Kutta method is preferred to solve the problem. This paper uses the Python program to show the results of both methods. This computational approach shows that the Runge-Kutta method is better for small steps at solving differential equations than Euler’s method.
Comparative Analysis of Long Short-Term Memory Architecture for Text Classification

Text classification which is a part of NLP is a grouping of objects in the form of text based on certain characteristics that show similarities between one document and another. One of methods used in text classification is LSTM. The performance of the LSTM method itself is influenced by several things such as datasets, architecture, and tools used to classify text. On this occasion, researchers analyse the effect of the number of layers in the LSTM architecture on the performance generated by the LSTM method. This research uses IMDB movie reviews data with a total of 50,000 data. The data consists of positive, negative data and there is data that does not yet have a label. IMDB Movie Reviews data go through several stages as follows: Data collection, data pre-processing, conversion to numerical format, text embedding using the pre-trained word embedding model: Fastext, train and test classification model using LSTM, finally validate and test the model so that the results are obtained from the stages of this research. The results of this study show that the one-layer LSTM architecture has the best accuracy compared to two-layer and three-layer LSTM with training accuracy and testing accuracy of one-layer LSTM which are 0.856 and 0.867. While the training accuracy and testing accuracy on two-layer LSTM are 0.846 and 0.854, the training accuracy and testing accuracy on three layers are 0.848 and 864.
MobileNet V2 Implementation in Skin Cancer Detection

Skin cancer is one of the most worrying diseases for humans. In Indonesia alone, skin cancer occupies the third position after cervical cancer and breast cancer. Currently, doctors still use the biopsy method to diagnose skin cancer. It is less effective because this method requires the performance of an experienced doctor, takes a long time, and is a painful process. Because of that, we need a way in which skin cancer can be classified using dermoscopic images to help doctors diagnose skin cancer earlier. Researchers proposed to classify skin cancer into seven classes, namely actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevus, and vascular lesions. The method used in this study is a convolutional neural network (CNN) with the MobileNet V2 architecture. The dataset used is the HAM10000 dataset, with a total of 10015 images. In this study, a comparison was made between data augmentation, learning rate, epochs, and different amounts of data. Based on the test results, the highest accuracy results were obtained, namely 79%. The best model is implemented into a mobile application.
The Application of Weighted Ranking Method Using Combination of ROC and CPI to Select Eligible Family for Keluarga Harapan Program Aids

The Keluarga Harapan Program (KHP), a financial assistance, is a program launched by the government to deal with poverty in various regions of Indonesia by conducting direct surveys and collecting data on disadvantaged families in each region. However, the issue is that many recipients do not meet the appropriate criteria or are not categorized as recipients. The Composite Performance Index and Rank Centeroid algorithms are a solution in the selection process for the recipients for the KHP by carrying out the analysis and comparison stages of whether they are categorized as KM (Disadvantage Families) through several stages. The results obtained based on analysis for recipient selection with a minimum performance index coverage value of 70% can be categorized as eligible to receiv assistance. In this study, 50 KM data samples were taken with the highest assessment value 128.41. In the top tenth ranking of the highest score from the 50 data held indicated that they were truly entitled to receive PKH KM financial assistance. Before using this method, only around 40% was eligible recipients.
Sentiment Analysis of Shopee App Reviews Using Random Forest and Support Vector Machine

During the COVID-19 outbreak, Indonesian marketplaces were significantly impacted including Shopee app. It is necessary to evaluate the features and services of the Shopee application by looking at the feedback given by the public in Google Play Store reviews. This is what prompted research to be conducted from Kaggle data in the form of Shopee reviews. From this data, sentiment analysis is carried out utilizing the Support Vector Machine (SVM) and Random Forest methods. This method are used to classify reviews based on positive and negative sentiments. The results showed that the level of classification accuracy in the Random Forest model is 82.21%. While the SVM model provides a higher level of accuracy of 84.71%. Data exploration on positive and negative sentiment classes is used to find insight into this problem. In positive sentiment, words that often appear such as “belanja”, “aplikasi”, and “barang” are found. As for the negative sentiments, namely “ongkir”, “kirim”, “aplikasi”. These words can be used to be a quality improvement or evaluation for the Shopee company.
Hubungan kemampuan berpikir kritis dan self regulated learning (srl) dengan tingkat literasi digital pada peserta didik kelas v sekolah dasar se-kecamatan laweyan

&lt;p class="Abstract"&gt;The aim of this research is to determine the relationship between critical thinking skills and Self Regulated Learning (SRL) with the digital literacy level of fifth grade elementary school students in Laweyan District. The research method used is quantitative. The sample for this research was 46 fifth grade students from 3 elementary schools in Laweyan District. The data collection techniques used are tests and scales. Data analysis uses prerequisite tests and hypothesis testing. The results of this research are that there is a simultaneous relationship between critical thinking and Self Regulated Learning (SRL) with the level of digital literacy which has a significance value of 0.000. The research explains that if critical thinking skills increase, SRL and also digital literacy levels will follow so that this research can be used as knowledge and reference for further research.&lt;/p&gt;
Analisis kesesuaian gambar ilustrasi dengan teks narasi pada buku bahasa indonesia lihatlah sekitar kelas 4 sd

&lt;em&gt;&lt;span lang="EN-GB"&gt;This study aims to analyze and describe the alignment between illustrative images and narrative text in the Indonesian language book "Lihatlah Sekita" from the grade 4 elementary school. This research is a type of document analysis with a qualitative descriptive approach. The research data consists of an analysis of the alignment of visual aspects in illustrative images with verbal aspects in narrative text based on Allan Paivio's dual coding theory. The data analysis technique used is the interactive description analysis model by Miles and Huberman. The results of this study show that there are high percentage of alignment between illustrative images and narrative text. The percentage of alignment between illustrative images and narrative text in the book is as follows: story 1 at 100%, story 2 at 94.12%, story 3 at 95%, story 4 at 100%, story 5 at 94.74%, story 6 at 100%, story 7 at 100%, story 8 at 100%, story 9 at 100%, story 10 at 87.5%, story 11 at 100%, story 12 at 100%, and story 13 at 100%. Essentially, the alignment between illustrative images and narrative text can influence students' comprehension of the narrative text content.&lt;/span&gt;&lt;/em&gt;
A Simple Fast Resource-efficient Deep Learning for Automatic Image Colorization

Colorization of grayscale images is a severely ill-posed inverse problem among computer vision tasks. We present a novel end-to-end deep learning method for the automatic colorization of grayscale images. Past methods employ multiple deep networks, use auxiliary information, and/or are trained on massive datasets to understand the semantic transfer of colors. The proposed method is a 38-layer deep convolutional residual network that utilizes the CIELAB color space to reduce the problem's solution space. The network comprises 16 residual blocks, each with 128 convolutional filters to address the ill-posedness of colorization, followed by 4 convolutional blocks to reconstruct the image. Experiments under challenging heterogeneous scenarios and using the Imagenet, Intel, and MirFlickr datasets show significant generalization when assessed visually and against PSNR, SSIM, and PIQE. The proposed method is relatively simpler (16 million parameters), faster (15 images/sec), and resource-efficient (just 50000 training images) when compared to the state-of-the-art.
Simplifying Tone Curves for Image Enhancement

A single tone curve which is used to globally remap the brightness of each pixel in an image is one of the simplest ways to enhance an image. Tone curves might be the result of individual user edits or from algorithmic processing including in-camera processing pipelines. The precise shape of the tone curve is not strongly constrained other than it is usually limited to increasing functions of brightness. In this paper we constrain the shape further and define a simple tone adjustment, mathematically, to be a tone curve that has either no or one inflexion point. It follows that a complex tone curve is one with more than one inflexion point, visually making the curve appear 'wiggly'. Empirically, complex tone curves do not seem to be used very often. For any given tone curve we show how the closest simple approximation can be efficiently found. We apply our approximation method to the MIT-Adobe FiveK dataset which comprises 5000 images that are manually tone-edited by 5 experts. For all 25,000 edited images - where some of the tone adjustments are complex - we find that they are all well-approximated by simple tone curve adjustments.
CIECAM16-based Tone Mapping of High Dynamic Range Images

A significant challenge in tone mapping is to preserve the perceptual quality of high dynamic range (HDR) images when mapping them to standard dynamic range (SDR) displays. Most of the tone mapping operators (TMOs) compress the dynamic range without considering the surround viewing conditions such as average, dim and dark, leading to the unsatisfactory perceptual quality of the tone mapped images. To address this issue, this work focuses on utilizing CIECAM16 brightness, colorfulness, and hue perceptual correlates. The proposed model compresses the perceptual brightness and transforms the colors from HDR images using CIECAM16 color adaptations under display conditions. The brightness compression parameter was modeled via a psychophysical experiment. The proposed model was evaluated using two psychophysical experimental datasets (Rochester Institute of Technology (RIT) and Zhejiang University (ZJU) datasets).
Analysis of The Level of Satisfaction of Researchers and Devotioners with The Performance

The development of information systems and technology has certainly changed people's behavior in carrying out their activities which were initially carried out manually and can now be carried out digitally so that they are more effective and efficient. The Service Research Information System (SIPP) is a transformation carried out to digitize research and service services within the Semarang State University (UNNES). As a service provider, to find out whether the UNNES Service Research Information System (SIPP) is running as it should, an evaluation process is needed on the performance of the information system. In carrying out information system analysis and evaluation activities, the PIECES Framework analysis model is used. PIECES framework is a framework that has six categories for classification and problem solving, namely Performance, Information and Data, Economics, Control and Security, Efficiency, and Service. The use of the PIECES framework method is very suitable for analyzing each system variable studied because it can provide real results on the effectiveness of the system used by UNNES. The aim of this research is to measure the level of satisfaction, find out strengths and weaknesses, analyze what components need to be improved in the quality of service at SIPP.
Fun and Exciting Research in Competitive Research Design

Research involving high school students or equivalent has been in great demand in recent years. Various ministries , government institutions and universities have launched various competitive research programs and events competition based research for SMA/MA level. Students and teachers at MAN 1 Semarang City have so far experienced many difficulties in designing competitive research. The achievements made by MAN 1 Semarang City students in the field of research are still very limited. The limited research experience of accompanying teachers could also be one of the causes. Collaboration with universities as institutions that not only provide education and teaching, but also research development, is a must. Therefore, assistance in designing research that is worthy of being competed in prestigious research competitions by the Semarang State University community service team is very important. Mentoring activities are carried out in the fields of science, technology and social humanities, involving Semarang State University research experts from both fields of study. This activity involved 31 students from MAN 1 Semarang City who were members of the madrasa research program along with 9 research assistant teachers from various fields of science. Activities include assistance in several phases of competitive research design, namely creating research ideas, drafting and finalizing research designs. This activity received a very good response and was seen as being able to help students and research assistant teachers at MAN 1 Semarang City in generating brilliant research ideas with novelty or novelty and high usefulness for the advancement of science and technology. A total of three proposal titles were produced from this activity ; one title in the field of social and human affairs and two titles in the field of science and technology. The obstacles in implementing mentoring are felt by teachers and students who have to spare time between busy teaching and learning activities at madrasas. The implementing team expressed its appreciation to the research supervisors and students who had successfully prepared research proposals to the final stage.
Revitalizing Distance Learning: MOOC For Improving The Quality of Training for Civil Servants

The COVID-19 pandemic has had a widespread impact on the education and training sector. Central Java, a province with training management resources, is experiencing a crisis in training management today. The large number of training participants from the state civil apparatus (SCA) who come from various regions in Indonesia requires innovation and change despite the various limitations of managers who do not have a special education base. Training managers at BPSDMD for SCA training need to be strengthened by developing distance learning through MOOCs because it can overcome the problems of distance, time, and financing to organize training that is usually done offline. This MOOCs will also increase the trust of other government agencies by making BPSDMD a pilot local government-based MOOCs developer. This service is carried out through mentoring and training that focuses on improving the resources of training managers at BPSDMD Central Java Province. This training and mentoring activity was attended by 38 BPSDMD employees with an In-House Training scheme for two days. As a result, there was an increased understanding of MOOCs and its application, designing training programs, presenting training materials, and evaluating training programs. The skills of the trainees can be seen from the portfolios collected. This service is useful in developing BPSDMD training programs for SCA in Central Java.
Socialization of The Danger of Drug Abuse and The P4GN Movement

The aim of the activity to socialize the dangers of drug abuse at Wahidiyah Samarinda Junior High School (SMP) is so that participants can understand the impacts of drug use, both physical, psychological and socio-economic impacts, and what the P4GN Movement is. Socialization activities use participatory methods, lectures and questions and answers. The socialization activity was carried out on July 11 2023 with the number of participants taking part in the socialization activity consisting of 45 students and 2 teachers located in the Wahidiyah Samarinda Middle School hall. For this reason, through this socialization activity on the dangers of drug abuse, it is hoped that it will be able to increase the understanding of Wahidiyah Samarinda Middle School students regarding the types of narcotics that are usually used by teenagers, the impact of narcotics abuse from physical, psychological, educational, social and economic aspects, the P4GN program, as well as ways to prevent and control drug abuse among teenagers.
Improving the Computer Literacy of Teachers and Students of Semarang City Elementary School

Public spaces have been prepared for technology-literate human resources. Technology is synonymous with computerized operations, even all applications can be developed through computer devices. This ability is needed to keep pace with the development of technological innovation around the world. Central Java has great potential in contributing to the increase in computerization understanding scores for school students. The target elementary school is considered appropriate, because from the study of developmental psychology has proven that elementary school students are most appropriately taught early talents and interests. This service aims to improve the ability of teachers and elementary school students in digital literacy to master computers. The service was carried out through a zoom meeting with participants of elementary school teachers and students in the Semarang City area. Workshop activities began with analyzing training needs, determining the type of training, delivering materials, and evaluating through practice by participants. As a result, the participants' skills in using computers have improved from the previous one. The participants' understanding of computer operation, especially the use of automation in Microsoft Word, also increased.