Reviewerly/exHarmony huggingface dataset:
Bibliometric Computational Mapping Analysis of Graphene-Based Surfaced – Enhanced Raman Scattering (SERS) During 2012 – 2022

This study aims to examine the development of research related to Graphene-Based Surfaced-Enhanced Raman Scattering (SERS) through a bibliometric approach to computational mapping analysis using VOSviewer. The acquisition of article data was obtained from the Google Scholar database using the publish or perish reference manager application. The keywords used to guide the process of searching for the title and abstract of the article were "Graphene, SER, surface enhanced raman scattering, nanoparticle". A total of 920 articles were obtained which were considered related to the topic of this research. The study period used as study material is Google Scholar indexed articles for the last 10 years (2012 to 2022). The results showed that the Graphene-Based Surfaced-Enhanced Raman Scattering (SERS) research can be separated into 4 terms:Raman Spectroscopy, Graphene, Nanoparticle and Surface. The term “Raman Spectroscopy” is associated with 189 links with total link strength 1539 The term “Graphene” has 198 links with total link strength 2036 the term “Nanoparticle” has 199 links with total link strength in 2739 and the term “Surface” has 189 links with total link strength 1651. The results of the analysis of the development of Graphene-Based Surfaced-Enhanced Raman Scattering (SERS) in the last 10 years show an increase. However, in 2020-2021, there was a slight decrease from 136 in 2020 to 135 in 2021. The increase in research occurred from 2014 - 2020 (49, 63, 80, 99, 105,116 and 136 publications per year respectively). While the popular Graphene-Based Surfaced-Enhanced Raman Scattering (SERS) research was carried out in 2020, there were 136 studies. From the results of research on article data using VOS viewer on Graphene-Based Surfaced-Enhanced Raman Scattering (SERS) and its relationship to the problem area, the results show that there has been an increase over the last 10 years. This study can be an initial consideration for future researchers who will conduct research related to this research topic.
Decision Tree Algorithm to Measure Employee Performance Discipline

Performance appraisal is done to measure the performance of an employee on the work done. The company conducts performance appraisals on employees at least every six months, involving all employees. This study uses the Absenteeism_at_work dataset. The purpose of this research is to analyze the performance of the Decision Tree algorithm in the classification process. Classification will be grouped into two, namely: disciplined and undisciplined The classification process will be carried out using K-Nime. Algorithm performance measurement using Knime Analytics Platform is open-source software for creating data science models. Knime builds data understanding and designs data science workflows and reusable components using accuracy, recall, and precision parameters. From the research conducted, the results of the Decision Tree algorithm have an accuracy rate of 94.6% while the label No. 5.4%. Based on the nineteen attributes proposed, it can be concluded that the Decision Tree algorithm has better performance.
Review Star Hotels Using Convolutional Neural Network

Currently the Deep Learning algorithm is developing very rapidly, where its application has helped a lot in individuals and businesses. One of its uses in conducting any review can be used this method. The review used in this case is to review five-star hotels. The hotel image is used as input for a review. So from the image of the hotel, it can be immediately known the level of the hotel. Usually the review is done using good sentences with compliments that tend to be positive sentiments. Meanwhile, sentences in the form of complaints tend to have negative sentiments. This study does not use sentences in conducting a review and uses a simple method in conducting the review process. The use of images as input is classified into five classes, namely one-star hotel class, two-star hotel class, three-star hotel class, four-star hotel class and five-star hotel class. The purpose of this research is to conduct a review on five-star hotels with image as input and hotel review as the output of the Deep Learning algorithm process. Deep Learning algorithm process using Convolutional Neural Network (CNN). The datasets used are public datasets and private datasets. The use of these datasets is a way to get better training model results. So that the accuracy in reviewing the image becomes better. The results of this study resulted in an accuracy reaching 98.48%, while for Loss it reached 0.0554.
Dogs Health Care Management System

Web based applications have become an important part of daily life as it improves inefficient resources management while saving time. With the advancement of technology, every business sector tries to introduce software systems with smart features Life has become too busy to schedule and keep up with pets' necessary medical appointments and care. There are currently a smaller number of online applications for veterinary systems. So, this study designs an online system to fill up that research gap. In this project, a JavaScript based application is designed with features such as booking an appointment by checking a doctor's availability and all those details are going to store in a database. This also records all the details of the dogs that come to the hospital for daily treatments. With the help of daily clinical data, veterinary health care owners can easily identify active clients and give offers easily. Moreover, newly introduced features are caused to increase the efficiency and maintenance of the system. However, this application addresses not only the pet lovers’ difficulties regarding the veterinary health, but it also addresses management side problems as well. This system is designed and implemented not only for the pets, but also considering orphan dogs. So, this system provides volunteer service as well.
Comparison of Main Characteristics of Food Insecurity Using Classification Tree and Random Forest

Since the emerging of big data era, the information and data are grown rapidly. It requires us to have ability to extract the knowledge and information that consisted in this explosion of the data. One of way that can be used for this purpose is by using machine learning method. One of purpose of machine learning implementation is to conduct classification analysis and to identify variable importance that contribute in the research. It’s conducted the comparative study between two machine learning classification methods named classification tree and random forest method. This study is implemented on Indonesian Socioeconomic Survey (SUSENAS) 2020 in Aceh Province. The purpose of the study is to identify the optimum method between both and to identify the characteristics of food insecure household. The optimum method obtained by comparing the AUC value. The results obtained is random forest outperformed classification tree with the AUC value of random forest method is 0,718 and classification tree method is 0,668. The rank of variable importance of the optimum method is the type of cooking fuel used in the household, the area of house floor, education level of head of household, number of savers in a household, and the type of house floor.
Implementation of Data Mining to predict sales of Bogo helmets using the Naïve Bayes algorithm

Consumer needs for safety and comfort in driving are very important, especially for two-wheeled or motorcycle riders. A good helmet is a helmet that is safe and comfortable when worn. Helmet qualifications that meet SNI standards are open helmets and closed helmets. Transactions are carried out online, because it was still in a pandemic situation when it was established. By carrying the tag line "Ride Safety With Your Own Style", trying to educate the younger generation to keep paying attention to safety when driving but also not neglecting fashion. For the type of sale of bogo and retro helmet brands, with a variety of colors and affordable prices. A common problem faced is how to predict or forecast future helmet sales based on pre-recorded data. This prediction is very influential on the decision to determine the number of helmets that must be provided, if you order helmets in sufficient quantities and it turns out that only a few helmet sales are sold and this will cause the stock of helmets to accumulate. The results of predictions for the sale of bogo helmets in Baris True Instances amounted to 16 data, which means Valid with 53% data accuracy. Meanwhile, there are 10 data classified as Instancely Classified Instance which means Invalid, with data accuracy of 46.67%. The amount of accuracy in the Weka application is the same as the amount of accuracy in Excel calculations. From the explanation above, the Naïve Bayes algorithm method is the best solution for predicting important things in a business need and others.
IMPROVING STUDENTS’ SPEAKING SKILLS WITH ACTION LEARNING STRATEGY AT EIGHTH GRADE OF JUNIOR HIGH SCHOOL (A Classroom Action Research at Eight Grade Students of SMP N 01 Kota Bengkulu)

This research aimed to see the improvement of students’ speaking skill in english lesson by using action learning strategy at eighth grade. The participants of this research were 32 students form VIII.6 class consisting on 15 male and 17 female. This research used Classroom Action Research (CAR). The data were obtained through the observation and speaking test. To determine students who are successful in improving speaking skills based on the KKM (Minimum Completeness Criteria), which is 80. To analysis the data, the researcher used two steps, namely scoring students speaking skills use quantitative data and calculating the students’ score. The results of this study showed an increase in students' speaking ability. The results of this research indicate an improve in students' speaking ability. Students who experienced an increase in speaking skills in the preliminary data was 62.5%, or 20 students, the cycle I was 75% or 24 students and the cycle II was 84.375% or 27 students. While students who did not succeed in the preliminary data was 37.5% or 12 students, the cycle I was 25% or 8 students and the cycle II was 15.675% or 5 students. Based on the results of interviews conducted by researchers to several samples of students, several factors were found that were able to improve students' speaking skills. These factors were the comfort that students get while studying, giving reward points to attract students' interest and teachers applied some learning tricks to attract students' attention while studying.
Designing a Tool for Evaluating Employing Augmented Reality Technology to Distinguish between Similar Alphabets

The study aimed to evaluate the attitudes of learning difficulties of teachers towards employing augmented reality technology in the formation of mental visualization to distinguish between similar alphabets. The study sample consisted of 153 teachers. Results identified that there is a positive attitude among teachers, as it creates a supportive environment to facilitate the learning process of people with learning difficulties, in addition to their desire to provide equipment and training programs that would help them employ Augmented Reality Technology in teaching people with learning disabilities. Furthermore, there are no statistically significant differences due to the variables of gender, academic qualification, years of experience and using Augmented Reality Technology. Moreover, there are statistically significant differences due to the variable of sub-specialty. However, teachers with other specializations associated with a diploma in learning difficulties have a more positive attitude than specialists in learning difficulties.
The Correlation Between Students’ Perception of Google Translate and Their English Writing Skill

The aim of this research was to find out the correlation of students’ perception of Google Translate and their writing skill. This research was a correlational research. The population of this research was the Fourth Semester English Education Study Program Students of Universitas Bengkulu. The samples were taken by using total sampling. There were 74 students as the samples from two classes of Paragraph Writing subject. There were two research instruments used in this research, Questionnaire and Documentation of student writing score. In analysis data, the researcher used Correlation Product Moment formula developed by Carl Pearson and the researcher using statistical analysis program SPSS. Based on the data analysis, it was found that the r-count was 0.469. It means that the correlation between the students’ perception of Google Translate and their writing skill was moderate. As a result of this, the alternative hypothesis (Ha) was accepted and the null hypothesis (Ho) was rejected. Since r-count 0.469 was higher than the r-table 0.296, the correlation was significant. Based on the research finding, it can be concluded that there was a moderate correlation between students’ perception of Google Translate and their writing skill. It means that Google Translate was not a really dominant factor that affects writing skill of the fourth semester students of the English Education Study Program of Universitas Bengkulu.
Applications of Additive Manufacturing in Architecture Study 3: Assessment of the Viability of using 3d printing for the Design and Prototyping of Historical Artifacts as Replicas

3D printing is now being used in many different applications. Souvenir items and replicas of artifacts, which usually do not need to have high durability/strength, may be one of the possible applications of 3d printing. In this study, the researchers tried to manufacture keychains, refrigerator magnets, and display items from historical artifacts in the province of Bataan. Three experts (1 from the tourism industry, 1 BS Tourism Faculty, and 1 expert in 3d printing) were tapped to assess the viability of using 3d printing in the production of souvenir items. The items were particularly evaluated based on their quality, color, surface finish, cost, durability, authenticity, material, etc. Important considerations were obtained from 3d printing as well as from the insights/evaluation provided by the experts. Experts suggested modifying the thickness, color, and materials for added appeal. Reducing the price might also increase the market for souvenir items. Adding labels and descriptions has also been recommended. All these improvements will inspire different emotions, create impact and make the design more memorable.
THE GENRE APPROACHES FOR TEACHERS’ STRATEGY TO DELIVER PRODUCTIVE TEACHING-LEARNING IN ENGLISH SUBJECT AT SMAN 1 KOTA LANGSA

The article overviewed the teaching delivery approaches of TESOL in language teaching classroom research, which employed at SMAN 1 Kota Langsa, Aceh, Indonesia. It was advised Genre Approach (GA) for engaging discussion than a directive task, a genre which is pitching dialogues one to another between teacher and students, and specifying words of selective diction. The article projected the teaching delivery process which is engaging discussion and defines the teacher-students’ communication skill in English. It described the phenomenon by employing a semi-structured interview, which described to aim an increasing approximation to control of written and spoken texts and the activity, teacher role is facilitator and students are benefactors. The result concerned about GA and its applications profiled the series of classroom practices; preparation, screening, validation, and content review. Thus, eventually, GA also help the teacher to validate their own communication competence, their students’ development process and engagement as well.
The Impact of Autopilot on Tesla

As Tesla advances in technology, Tesla is expeditiously embarking on exploring an emerging field, driverless technology. Due to the current instability of driverless technology, driverless systems are not commonly used at the moment. Nevertheless, its impact on Tesla can not be neglected. Therefore, this study focuses on the impact of the emergence of autonomous driving on Tesla. Specifically, this paper explores the impact brought about by autonomous driving by collecting statistical data, gathering real-life cases, and analyzing the information. However, the research illustrates that Tesla’s Autopilot is a double-edged sword. It damages the reputation of Tesla while offering the huge potential for gaining tremendous revenue in the present and future. In the long run, the scales are tipped in favor of autonomous driving technology. Thus, persisting in exploring the field of driverless technology will speed up the promotion of Tesla.
Effectiveness of Innovative Learning Media To Improve Counting Ability In Early Childhood

The learning element that must be considered is the learning media. One way that teacher can do is to use learning media that can attract children's attention so that the ability to count will accompany it. One of the learning media that can be used is the busy book learning media. The Busy Book is made of flannel with bright and varied colors and is safe for children in daily activities. Learning media that is quite interesting for children, especially at the age of 5-6 years is busy book learning media. The purpose of this study was to determine the effectiveness of busy book learning media to improve numeracy skills in early childhood. The research was conducted through experiments to 16 students in Class B. The data was collected through an oral counting test. The data analysis technique was carried out through a difference test with the Wilcoxon Signed Rank Test. The results showed that there was a significant difference in the students' counting ability before after the implementation of learning using busy book media. These results indicate that the use of innovative learning media is effective in increasing numeracy skills at an early age. Keywords: learning media, innovative learning, counting ability
Implications of The Minimum Competency Assessment (AKM) on The Literature Motivation Of Students Of SMP PGRI 2 Wates, Blitar Regency

The application of AKM or Minimum Competency Assessment is used to measure students' abilities, one of which is in terms of literacy, which is oriented towards technology-based learning, innovation, and critical reasoning. In the aspect of literacy in the implementation of AKM, it has a contextual nature and is able to solve problems (problem solving) in providing stimulation for students to think critically or critical thinking. The study used a questionnaire quantitative research method presented on a Likert scale with five answer choices, namely strongly agree (SS); agree (S); normal (BS); disagree (TS); strongly disagree (STS). The measuring tool used in reading motivation is the Reading Motivation Questionnaire (RMQ) developed by the author into 7 dimensions, including (a) curiosity, (b) involvement, (c) academic value, (d) competition, (e) recognition. social, (f) regulation of emotions, and (g) freedom from boredom. To measure literacy motivation in junior high school students as the effect of implementing AKM on students. The source of this research is class VIII students at SMP PGRI 2 Wates who follow the AKM as the target school in the Class 4 Teaching Campus activities in 2022. The results obtained based on the measuring instrument for students' literacy motivation after carrying out the assessment on students concluded an increase in literacy motivation. students after implementing AKM on students. Keywords: literacy motivation; Minimum Competency Assessment; Reading Motivation Questionnaire
The Use of Indonesian-English Code Mixing in One of Molita Lin’s YouTube Video as Beauty Vlogger

The occurrence of code mixing phenomenon was unavoidable, moreover in the bilingual society such as Indonesia, where the speakers communicate by in other (foreign) languages besides their national or local languages. There is no exception to Molita Lin, an Indonesian beauty vlogger who produces and uploads videos about beauty-related topics, such as cosmetics, skincare products, body care routines, and make-up tutorials to the YouTube platform. Since she has the ability to speak in English, Molita Lin significantly uses English terms while explaining something in her videos. Therefore, this study was aimed to provide an important opportunity to advance the understanding the types of code mixing as well as the reasons of the occurrences of code mixing in one of Molita Lin’s YouTube video entitled “SKINCARE UNTUK PEMULA ANTI RIBET | Day Night” by using documentation method as the method of data collection. and then the data were analyzed descriptively and qualitatively by applying theories proposed by Hoffman regarding types and the reasons of code mixing. As an outcome, this study concluded that in the Molita Lin’s video transcribe, there are three types of code mixing and only four out of seven reasons of code mixing that can be observed.
Fooling the Big Picture in Classification Tasks

Minimally perturbed adversarial examples were shown to drastically reduce the performance of one-stage classifiers while being imperceptible. This paper investigates the susceptibility of hierarchical classifiers, which use fine and coarse level output categories, to adversarial attacks. We formulate a program that encodes minimax constraints to induce misclassification of the coarse class of a hierarchical classifier (e.g., changing the prediction of a 'monkey' to a 'vehicle' instead of some 'animal'). Subsequently, we develop solutions based on convex relaxations of said program. An algorithm is obtained using the alternating direction method of multipliers with competitive performance in comparison with state-of-the-art solvers. We show the ability of our approach to fool the coarse classification through a set of measures such as the relative loss in coarse classification accuracy and imperceptibility factors. In comparison with perturbations generated for one-stage classifiers, we show that fooling a classifier about the 'big picture' requires higher perturbation levels which results in lower imperceptibility. We also examine the impact of different label groupings on the performance of the proposed attacks.The online version contains supplementary material available at 10.1007/s00034-022-02226-w.
Features of the Blended Learning Technique in a General Educational Organization to Reduce Pedagogical Risks

Blended learning involves working in an electronic educational environment, which carries additional pedagogical risks for students. In this paper, we assessed the main pedagogical risks and proposed options for their elimination by improving the methodology of blended learning. The purpose of the article is to find solutions to improve the methods of blended learning to eliminate pedagogical risks that affect the health of the subjects of the educational process. Methodology. The methodological basis of this study was the assessment of the pedagogical results of the use of E-learning in the educational process, as well as the likely pedagogical risks. Methods of theoretical research, analysis of regulatory documents in force on the territory of Russia, in the field of education, as well as electromagnetic, informational, personal security were used. Conclusion: thus, when planning classes in the blended learning format, it is necessary to take into account pedagogical risks for participants in educational relations, which for the most part are not taken into account. There is a need to conduct research aimed at determining the maximum permissible level of EMF for children of different age groups and choosing the safest electronic learning tools. It is important to pay special attention to the expertise of digital educational content. Ideally, training should be carried out only on electronic educational platforms that have passed the examination of digital educational content for compliance with the requirements of the legislation of the Russian Federation.