Reviewerly/exHarmony huggingface dataset:
Implementation of Cluster Initial Center on K-Means Algorithm Using Coefficient of Variance and Correlation in E-Government Mapping

One of the well-known methods of data mining is clustering. Data are grouped using the clustering technique, which ensures that each cluster has data that is as comparable and diverse as possible. K-means is one of the clustering methods that is frequently utilized due to its simplicity, has a relatively quick computing time and can be utilized to manage enormous volumes of data. However, the initialization of the cluster’s initial center has a significant impact on K-means, and the cluster’s output is a local optimal solution. The first attribute is calculated using the coefficient of variance, while the second attribute is calculated using the correlation coefficient. Then calculate the average of the two attributes to be the center of the cluster. The K-means algorithm’s results when applied to the 2021 E-Government survey’s methodology
Telematics Work Field Review Text Classification Using the Naïve Bayes Method

Indonesia will benefit from a demographic boom in 2030 with a higher labor supply than in earlier decades. Then in industrial revolution 4.0 robotics and artificial intelligence will take the place of low-skilled or menial employment that don't require specialized expertise (AI). To aim research is Telematics Work Field Review Text Classification Using the Naïve Bayes Method. The method using Multinomial Naïve Bayes model which is trained to learn from patterns in training data set without being programmed explicitly. Then, based on the Term Frequency - Inverse Document Frequency, consider the weighting of the word used (TF-IDF). The text classification stage is then carried out using the multinominal nave bayes classification method with evaluation using the confusion matrix, following the acquisition of the TF-IDF value. In the study it took data with web crawling techniques on social media sites twitter. The data collected was 936 data consisting of 7,8% negative sentiments, 26,4% positive sentiments, and 65,8% neutrals. The results of accuracy testing using the Confusion Matrix. And from the results of such tests resulted in an accuracy of 66%, precision 73%, and recall 85%.
A Mapping Study Research on Blockchain Technology in Education 4.0

Decentralized transactions and transaction management in the form of the Bitcoin cryptocurrency are part of Blockchain Technology. This Blockchain idea was created in 2008 by Satoshi Nakamoto. The attributes of Blockchain contain the importance of providing security, anonymity and integrity of transactions without third-party companies, which makes an exciting area due to Blockchain’s general and decentralized nature. In this study, systematic mapping observations were used to collect all applicable studies on Blockchain generation. This goal is supported by understanding Blockchain research topics, situations that demand development, and future guidance on technicalities in the Blockchain era. The author has successfully researched forty-one major papers from several scientific paper publishers. The results of this study show that more than 80% of Blockchain papers use Bitcoin and the rest check other Blockchain attributes such as security using smart contracts. In this study, the authors specialize in reducing the limitations of using Blockchain from a privacy and security perspective. However, among the solutions presented, there is a lack of concrete evaluations of their effectiveness. This research produces a novelty in the form of suggestions for the use of Blockchain that can be used by leading to Education 4.0.
The Role of Digital Technologies in Incorporating Neoliberal Principles Into Higher Education in Indonesia

National strategies have been critical in defining how digital technologies are used in higher education (HE). This study draws on some analyses of digital teaching and learning strategies released by the Indonesian government. It exemplifies that, across the strategies, digital technologies are portrayed as instruments for progressing neoliberal principles in Indonesian higher education. Paradoxically, the strategies are also riddled with inconsistencies and ambiguity regarding the asserted connections between digital technologies, learning, and markets. This problematic portrayal of digital technologies makes them complicit in the neoliberal erosion of Indonesian HE. This study contends that the problematic representation of digital technologies contributes to their involvement in the neoliberal erosion of Indonesian HE.
Cross Department’s Elective Course Recommendation for University Students using Hybrid Filtering

The problem experienced by the average student is that it is challenging to choose the elective courses to be achieved because of the many elective courses offered. The solution that can be given is the existence of a recommendation system for choosing elective courses that can recommend classes according to the interests and abilities of the student. Each user will get different recommendations according to the profile and interests of the user. Although several studies have been conducted related to the recommendation system for electing electives, no research focuses on recommendations for taking elective courses across majors and predicting the value of the results of the procedures to be taken. This study focuses on the advice of elective classes and outcomes using Hybrid Filtering by combining collaborative filtering using the ID3 algorithm and content-based filtering Matrix. After implementation, we tested the accuracy by dividing the test data and training data differently—using academic data consisting of 1000 data. The accuracy result is 81and suggestions for further research can use evaluation of user satisfaction to see whether the results of the recommendations from the system are in the interests of students.
Design of Know Your Customer (KYC) Using Blockchain Technology

Blockchain is a technology that can solve various problems, especially problems in user privacy. This paper aims to illustrate how measured, general-purpose, and simple-to-manage field systems are ordinarily actualized on permission Blockchains, but with the impact of an open Blockchain, making KYC a reality and aiming to use Blockchain as full transparency. With the solution, the conception and execution of the KYC standard customer authorization inside a Blockchain framework in the know your customer (KYC) standardization. The method uses a decentralized scheme that aims to protect users in Blockchain companies by proposing two developed smart contracts, exploited users register and upload their KYC information on IPFS storage. The next stage is waiting for the admin's decision on whether the validity is rejected or approved, and the admin will also check the end date of the user's KYC document. leIn private KYC contract. The system will offer to find efficiency and effectiveness in operating all technology modes and components through intelligent integration and schematics. This plan will include more novelty, including arranging to prove a person's character and developing Blockchain technology for even greater schema clarity.
Digital Dashboard for College Management Business Intelligence Analysis with ROI Method

Many tertiary institutions have diverse but not integrated data sources, causing information overload. This is where we need to use the Digital Dashboard which can be applied to Higher Education management. This research will take place in the Raharja College located in the city of Tangerang-Indonesia. Digital Dashboard is needed due to problems that occur at this time in several universities, such as the absence of a system of measuring the performance of staff at universities, the need for management to get fast and accurate information in decision making, the absence of clear and integrated data management for information to be made, as well as flexible field conditions and loaded with data uncertainty, so that many intangible measures will be found. The purpose of this study is to identify the benefits achieved and carry out the process of quantification of these benefits to measure their economic value, conduct a study of the ability of the Digital Dashboard to support the performance of management and staff of Higher Education to then be developed into a real application, Generate a prototype of Digital Dashboard which is equipped with Business Intelligence which can ultimately help provide online/real time services as a reference for decision making. The method used in this research is to use the Simple-ROI calculation method of tangible benefits and the calculation of Quasi intangible benefits that can be achieved (Value Linking and Value Acceleration). This research produces a proposal that can be used as a justification as well as an illustration of the possibility of implementing and developing a Digital Dashboard system at the College of Raharja.
Deep CNN–Real ESRGAN: An Innovative Framework for Lung Disease Prediction

The advent of Deep learning models led to an unprecedented change in the field of medical image analysis. Various models like Dense net, Mobile net, and Nas net achieve an accuracy of more than 80% but the lack of clean datasets makes it difficult for these algorithms to be effective in the classification of medical images. Data preprocessing techniques based on SRGAN (Super resolution generative adversarial network) have been used to improve the resolution of the image but they are still far from addressing general real-world degraded images. Our approach proposes to use Real ESRGAN (Enhanced super resolution generative adversarial network) on a lung disease dataset that includes six different types of lung disease. When preprocessed images with actual ESRGAN (Enhanced super resolution generative adversarial network) are used in deep CNN (Convolutional neural network) models, their classification accuracy improves. In our paper, three CNN models Mobile net, Nas net, and Dense net are combined with Real ESRGAN and their classification accuracy enhances above 90% and images which were not correctly classified with base models,are now classified with near to one probability.
Single and Multi-Documents Text Summarization Technologies for Natural Language Processing: a Systematic Review on Method and Dataset

The summarization technologies have been increasingly used in recent decades. Those technologies are a very important part of emerging topics in computer science and engineering, such that Natural Language Processing (NLP). Several methods have been used for analysis to get good summary results. There are two types of document summaries: single document summaries and multi-document summaries. Single document summaries aim to extract information from a single document to get new and relevant summary information, while multi-document summaries extract information from multiple documents. This study focuses on the activities of the semantic literature in previous studies to obtain the basis of the widely used base methods and data sets used in this study. Data was collected from Scopus publication sources from 2019 until 2022 Q2 for analysis. Researchers use guidelines from the semantic literature method by using the basis of Kitchenham and Charters as a reference in its design. In this study, there were forty-eight articles obtained from the filtering results of several criteria used, including exclusion and quality assessment.
Identifying The Types and Platforms of the Metaverse – a Systematic Literature Review

Metaverse is gaining a lot of traction lately, especially after Mark Zuckerberg’s Facebook changed its name to Meta as a preparation to enter another universe called metaverse. The eXtended Reality (XR) technology is also rapidly growing since the birth of Virtual Reality (VR), followed by Augmented Reality (AR), and Mixed Reality is the latest development of XR technology. As the XR technology is the perfect companion to support the metaverse and the understanding of the metaverse is already clear but unfortunately, there is a lot of misconception about the types of the metaverse as well as the platform for each type. This paper aims to point out the types and platforms of the metaverse. 49 articles have been collected and reviewed carefully as a quality assessment process, and 45 articles have been selected for further review. This review informs everybody that there are many types and platforms of the metaverse nowadays.
A Bitcoin Blockchain-Based Educational Digital Assets Management System

A variety of information with high research value is produced as an educational asset as a result of the use of various emerging technologies in education. Traditional methods of handling educational resources have emerged. Considering the shortcomings of conventional system centralization methods, people are starting to use decentralized blockchain technology for innovation. The research objective of blockchain 3.0 technology is to support the architecture of the educational digital asset management system also suggested in this article, which also uses bitcoin. Apart from performing essential blockchain validation and storage, it transforms the disparate data that students generate across their classroom and extracurricular learning activities into educational digital assets. To study the growth of school users and achieve educational goals of educating students according to their individual talents and peculiarities, the system can use a variety of heterogeneous data from digital assets.
Reliable Smart Grid for Blockchain Technology Transactions

One of the key advancements in the development of smart grids is the use of transactive energy, which utilizes distributed energy resources like smart grids Contributions In today’s industrial era, where technology is developing very quickly, it is essential to use electricity as needed to prevent loss, waste, and supply shortages "Smart Electric" or "Smart Grid" design is strongly advised in this situation The following advantages in terms of cost-effectiveness are anticipated: Large enough for even technical solutions to be taken into account. This will be implemented extensively by key electricity companies Utilizing transactive energy in a smart grid, however, introduces new security issues such unauthorized energy transfers Novelty This study proposes a Blockchain based smart contract architecture with fully decentralized capabilities for dependable and inexpensive transactive energy. The Blockchain, which has a high replication rate and provides stronger security against manipulation, is used to record energy transaction data These enhancements can be made via Blockchain technology Blockchain technology is confirmed as a promising option to raise the share of renewable energy after the development of renewable energy and how it interacts with Blockchain technology are examined. The various Blockchain applications in are then divided into various categories, including smart contracts and Demand Response (DR), Electric Vehicles (EV), the Internet of Things (IoT), decentralized energy management, energy trading, financial transactions, and cyber security testbeds, as well as environmental concerns The energy auction is run in accordance with transparent rules that are implemented as a smart contract, making it accessible to all parties Threats resulting from known smart meter vulnerabilities are decreased by temporarily limiting vulnerable prosumers’ access to smart meters and updating their devices as soon as security solutions are available.
Implementation Of Active Learning Using Card Game Model In Increasing Learning Motivation

This study aims to improve the motivation to learn Civics using active learning method. This research was conducted in class X.5 which amounted to 34 students. The method used in this research is a classroom action research method (PTK) which is carried out as many as three cycles. In the first cycle directly using the active learning method with the card game model in learning Civics, because it was noticed at the beginning of observation that the study of learning class X.5 is still low, after using the method of active learning student motivation questionnaire as much as 57.64%. In the second cycle of learning motivation has increased, this can be seen from the increase of students’ learning motivation, the spirit of competing in a healthy, active in answering questions and asks, cooperation among students and the increase of questionnaire results of student learning motivation in the second cycle as much as 71.76%. Civic learning activities in the third cycle by using the card game model can improve student learning motivation, this can be seen from the result of the questionnaire student learning motivation as much as 76.03%. The results of the study increase the learning motivation from the first cycle to the second cycle of 14.12%, from the second cycle to the third cycle increased by 4.27%, so the increase in learning motivation from cycle one to third cycle increased by 18.39%. Based on the results of research that has been done, it can be concluded that the application of active learning method of card game can improve student learning motivation in Civics subjects in class X.5 in SMA Negeri 1 Cikarang Utara
Enhanced Random Crop with Data Augmentation for Insulator Detection in Complex Backgrounds

With the development of the automatic inspection of unmanned aerial vehicles (UAVs), improving the detection accuracy of insulators will not only help further insulator state detection and fault diagnosis but also contribute to the early landing of the UAVs’ automatic inspection system. In this paper, we propose a data augmentation method based on the random crop to improve the detection accuracy of insulators. Firstly, it ensures the validity of the label by generating a patch that contains the centers of all ground truth boxes. Secondly, it achieves a balance between protecting the ground truth and random cropping by limiting the area ratio of each ground truth box before and after random cropping. We find that these two steps increase the attention of the model to the insulator. On the self-made insulator dataset, the solution achieves 91.2% and 89.3% mAP in YOLOv3 and RetinaNet respectively, which is 3% and 1.5% better than the random crop.
Predicting Tuition Fees for University of Raharja Students Using Artificial Intelligence

A university hopes that every semester students are able to pay tuition fees properly and smoothly. It is hoped that the institution will be able to maintain monthly cash flow so that operational and maintenance costs can be met. Therefore, this research was conducted to predict and fulfill institutional cash-in from the SPP payment method either in cash, installments or sometimes late payments every semester. In predicting the method of payment of tuition fees, using student profile data (name, name, study program) and grade point average for each semester for the past 5 semesters and the method of payment (cash and installments). Using the Naive Bayes (NB) algorithm, Support Vector Machine (SVM), and Deep Learning, this research aims to predict college costs. Classification Prediction Model with Naive Bayes used in the research Method, Confusion Matrix Performance NB produced by SVM and Deep Learning, Confusion Matrix Performance SVM produced by SVM and Deep Learning, and Confusion Matrix Performance Deep Learning produced by Deep Learning.
Developing Dataset of Response Variance for Automatic English Speaking Testing App

Through student speeches and other means, English teachers have already been able to assess their students' speaking skills firsthand. A huge number of pupils' speaking skills must be evaluated, which takes time. Thus, the need for an automated methodology to gauge English-speaking proficiency. It is good to examine English speaking with a computer using speech recognition technologies. However, to accurately assess the students' speaking performance automatically using computer requires a large dataset of students' various responses that are marked as appropriate responses. These responses are further marked as correct or expected responses, which later be used for automatic scoring generation and other various useful features. In this research we develop a dataset by collecting, analysing, categorising, and weighting various answer possibilities collected from students' responses to specific questions. The dataset, from 20 participants, was examined through Confusion Matrix, and it yielded accuracy score above 80%. However, this yielding result needs further testing with higher number of participants to get stronger accuracy. This dataset is expected to power the automatic English speaking testing app in assessing students' speaking performance. Eventually, the app is expected to be helpful for English teachers to efficiently score the speaking performance of huge numbers of students.
Blockchain Technology Evolution Trends Bibliometrics Analysis on Scopus Database Using VOSviewer

The emergence of the blockchain technology revolution in several industries is a privilege because it has opened many opportunities in research. Thus, blockchain has gained much hope from the research community. In recent years, the change in research on the blockchain has developed rapidly into a trend that has led to further research for a more detailed analysis of the current knowledge systems in the field. Meanwhile, there is still a lack of in-depth exploration of blockchain in bibliometric reporting. This study aims to address the gap by analyzing the scope of blockchain research activities carried out to date. This study uses bibliometric analysis using VOSviewer to analyze the Scopus database in a collection of more than 500 articles published between 2016 and 2021 containing the author, number of citations, journal source, publisher, institution, country, year of publication, category, and author keywords. Need to be analyzed. This study presents the latest trends and abstracts of Blockchain research from several bibliometric perspectives.
Comparison of Pretrained BERT Embedding and NLTK Approach for Easy Data Augmentation in Research Title Document Classification Task

Data imbalance has been a problem in classification tasks, be it a binary classification or multiclass classification. Easy data augmentation is an approach to do data augmentation in natural language processing data by substituting term with its synonym, inserting a random synonym, or deletion. This research aims to see the performance of machine learning model if term inserted or changed is using BERT rather than NLTK. This research also proposed the hybrid approach of BERT and NLTK, and proposed sentence selection methods such as whole data and random pick. This research showed that EDA using only BERT gives the best performance for all sentence selection approach. However, several classes still showed reduced performance.