Reviewerly/exHarmony huggingface dataset:
Characterization of Pepper Ripeness in the Field Using Hyperspectral Imaging (HSI) with Back Propagation (BP) Neural Network and Kernel Based Extreme Learning Machine (KELM) Models

Due to the economic differences of chili peppers at different maturity levels and the absence of differentiation of maturity in existing harvesting processes, it is crucial to accurately identify the maturity during harvesting for improving economic benefits. This paper investigated pepper maturity recognition models using hyperspectral technology to realize intelligent pepper harvesting with high accuracy and monitor maturity. Hyperspectral data (400–1000 nm) for field line peppers were collected, preprocessed using normalization, Savitzky-Golay convolutional smoothing, and standard normal variable transformation and subsequently used to train back propagation neural network (BP) and kernel based extreme learning machine (KELM) models. Principal component analysis (PCA) was used to reduce data dimensionality and identify characteristic spectral wavelengths for pepper maturity at 862.2, 676.9, 578.1, and 980.7 nm; and BP, PCA-BP, KELM, and PCA-KELM models were subsequently established. The precision for the KELM and PCA-KELM models (99.5% and 97.3%, respectively) was superior to the other two models; however, the PCA-KELM model used only four of the feature wavelengths as inputs, hence it required only 1/44 the data compared with the KELM model. Thus, the PCA-KELM model achieved high recognition accuracy and training speed, offering an effective method to discriminate pepper ripeness based on hyperspectral features.
NFT Coin Price Prediction (Non-Fungible Token) Using K-Nearest Neighbors Method

NFT, or non-fungible tokens, are online certificates of ownership that can be traded based on data units stored in digital ledgers belonging to blockchain technology.This is non-fungible, meaning that it cannot be exchanged and is unique.NFT has been around since 2014.But now, it is increasingly being considered as a practical method for trading digital artwork or art.To buy NFT assets, you require special coins in the form of NFT coins, which consist of various types, such as mana coins, sand, axes, and other NFT coins.The NFT coins are used to process NFT purchase transactions.The movement of NFT coins over time is relatively erratic and uncertain.This NFT coin price prediction will be very useful for investors to know how the investment flow of each price works because the price of each NFT coin will change from time to time.through the literature study stage, interviews, and viewing daily NFT coin price data where the attributes used are date, open, high, low, close, and volume.The method used in this research is k-Nearest Neighbours.Dataset collection through the website www.coinmarketcap.comfor the period January 1, 2019 to December 31, 2021.Then the data processing is carried out.An accurate NFT coin price prediction model can help investors in considering transaction decisions because NFT coin prices, which tend to be non-linear, will allow investors to make predictions.This study aims to obtain the predicted value of NFT coins using the k-Nearest Neighbours algorithm.
The Blockchain Technology Architectures to Improve the Value Creation of Crop Agriculture Supply Chain in Indonesia: A Literature-Based Overview

Businesses are adapting to build supply chain resilience to ensure long-term sustainability in response to globalization, rising climate, health, and poverty issues.The recent global health crisis has forced us to contribute to the Sustainable Development Goals in the supply chain, especially Agriculture Supply Chain Management (ASCM), which is responsible for food production and distribution.Farmers have a function as producers but are frequently disadvantaged, and stakeholders often lack coordination.There is a paucity of knowledge regarding data transmitted between players and goods from the upstream to the downstream.The ASCM can consider employing blockchain technology (BT) to address these concerns.The ASCM anticipates that blockchain technology will enhance its operation and sustainability by maximizing value creation in each supply chain phase.This study aims to investigate the current state of Indonesia's ASCM, provide insight into the usage of blockchain, and propose a blockchain architectural model to enhance the value creation of ASCM in Indonesia.The researcher evaluates and assesses 15 publications, compares them to current situations, and recommends an improvement analysis for the future's system.The final analysis reveals the proposed architectural model in ASCM using blockchain technology with Ethereum, and Interplanetary File System that enables traceability, transparency, and great coordination among stakeholders.The possible created values mentioned are Business process improvements; Improving customer experience; Data security; Learning -developing of new capabilities; Coordination; and Tokenization.
Chatbot In The Selection Of Outpatient Ward In Hospital Using C4.5 Algorithm

The C4.5 algorithm is one of many data mining classification techniques.The final result in the classification of this algorithm is the creation of a decision tree.This tree helps in making predictions and selection.The c4.5 algorithm is widely used in many cases, such as outpatient services, disease prediction, etc.It's just that for the c4.5 algorithm method in the construction of chatbots, only a few apply it.More cases of implementing chatbots using Semantic Web, NLP, etc.This study focuses on the c4.5 algorithm method and its application in chatbots because, in previous studies, it was still rare to implement chatbot services in hospitals using this algorithm.The c4.5 algorithm method in this study will be carried out on a chatbot because in predicting poly hospital services for selection, it is more suitable to use this algorithm.The data used were obtained from the Azra Hospital Bogor, totaling 3,292 data after the data cleaning process with the classification results using the c4.5 algorithm, namely getting the RMSE (Root Mean Square) / Mean value of 12.66%.This number means that the lower the average prediction error, the less.That means that from the total data used, 315 data experienced wrong predictions, and 2,977 other data were predicted correctly.Gradient Boosting was also carried out to reduce the prediction error rate by 0.66% after being tested seven times.In the future, this research is expected to improve the classification test time.
Interactive Media to Maintain Normal Sugar Levels for Diabetics By Using The Design Thinking Method

In general, learning media is widely used to share needed information, especially for the health of people with diabetes.Still, few people can understand health education for people with diabetes, and special attention is needed for sufferers, especially the elderly, to maintain a lifestyle of food, drink, and exercise.Lack of understanding in health discussions and insight often causes many people with diabetes to experience complications of other diseases, so in this study, we used interactive media to support health understanding by using design thinking approach technology that can make it easier for sufferers to understand diabetes, because of a direct approach with sufferers.The design thinking method has five stages, empathize, define, ideate, prototype, and test, which are carried out to help solve problems by adjusting to the needs of sufferers and used as a reference in application development.The final stage of this research is to test the respondents to determine whether the solutions applied can solve the patient's problems.The test was carried out using a questionnaire given to 50 respondents from the general public with complaints of diabetes symptoms, giving a calculation result of 80.2% agreeing that the respondents felt helped by the Ayasha application.Suggestions for further research are necessary additions that include the presentation of more comprehensive information about diabetes and a feedback feature for users to make it easier to understand, and further updates are needed.
Exploring Concept Drift in Technology by Tweets Mining

Over the last decade, a dramatic transform happened in information sources and their use in the digital era.Social media networks have brought a new way of expressing the sentiments of individuals.The matter went beyond being an expression of separate opinions of some individuals, as companies, official institutions and various organizations have pages on the communication sites through which they share various developments, products, opinions, and sometimes even official decisions.Social media become a medium with a huge amount of information where users can view the opinion of other users that are classified into different sentiment classes and are increasingly growing as a key factor in decision making.Twitter is a microblogging service built to describe what is happening anywhere worldwide, at any moment.It's a fascinating forum for more than 500 million messages per day from about 1.3 billion people.Twitter data is short, specific, and easily accessible, that's why it has become one of the best sources for sentimental analysis and knowledge discovery by data streams mining.One of the major issues that affect data streams mining is that the underlying distribution of data may change over time leading to the phenomenon of concept drift.Identification and handling concept-drift in Data Streams is present area of interest.In this study, we present an approach to explore and understand the concept drift occurring in Twitter data streams.Two machine learning technique Naive Bayes Classifier and Extreme Gradient Boosting (XGBoost) Classifier were applied on more than 300K tweets from International Technology Companies, and to detect / understand concept drift and specify whether concept drift in a technology area is a radical or an incremental innovation.
Habit Tracker Gamification Software Development in Community–Based Daily Personal Hygiene Using Octa lysis Framework

Personal hygiene is crucial for each human being as they have their way of maintaining them daily.While one person can maintain their hygiene, they are still prone to diseases that others can spread.Performing daily hygiene is mostly about habit.Habit is a settled or regular tendency or practice, especially one that is hard to give up.To change from one habit to another, we need a constant reminder.The development of information technology provides a more convenient way to do reminder activities.Apps such as Habit Tracker, Sure Wash, and Epic To-Do List are available freely in Google Play Store and Apple AppStore.Although many gamified reminder applications exist, most of them only work on one individual.While for daily hygiene, the problem is not only the individual habits but also the society.This study improved the awareness of personal hygiene by building gamification software.We use the Octa lysis framework to analyze and design the software, using three levels: data collection, calculation, and analysis from 47 respondents using the Octa lysis 8 core drive measurement.The evaluation period was carried out for 14 days using 30 respondents by analyzing Pre-test Vs.Post-test using HMSAM.Finally, this study showed a significant increase in changes in daily personal hygiene with an average of 82%, which concluded that a mobile application equipped with a reminder feature for particular groups could trigger other people to do the same.
Research on the Development of Rubrics for the Selection of Online Learning Tools in Higher Education

COVID-19 이후 대학 교육환경이 크게 변화함에 따라 Zoom, Webex 같은 온라인 학습 도구들이 다방면에 활용되고 있다. 그러나 이런 학습 도구가 가진 다양한 장점에도 불구하고 실제 교수·학습 과정에서 많은 문제들이 나타나고 있다. 본 연구의 목적은 온라인 학습 도구를 선택 할 때 적용할 수 있고 교육 프로그램이 선정된 도구를 제대로 활용하여 만들어진 것인지 평가하는 루브릭을 제시하는 것이다. 그리고 제시된 루브릭이 넓은 범위의 기준을 통합함으로써 사용자의 요구에 따라 적절하게 조정될 수 있도록 하는 것이다. 먼저 문헌 연구를 통해 온라인 학습 도구의 기본 개념과 절차를 파악하고 교육학 석사 이상의 전문가 4명이 브레인 스토밍을 통해 루브릭을 생성하였다. 이후 생성된 루브릭을 1인당 4~5개씩 압축한 뒤 반복적, 재귀적 과정을 거쳐 정리하였다. 연구 결과 총 8개의 루브릭이 제시되었고 각 루브릭에 도구가 갖추어야 할 개별적 특성을 덧붙였다. 첫째, 기능에 대한 기준으로 하위 범주에 규모, 사용의 용이성 기술 지원, 비선형적 링크 둘째, 접근성에 대한 기준으로 하위 범주는 접근성의 표준, 사용자 중심의 참여 가능 여부, 필요한 하드웨어와 소프트웨어 요구사항, 사용 비용 셋째, 기술적 전문성의 정도로 하위 범주는 LMS와 LCMS 내 통합과 임베딩, 운영 체계 및 브라우저, 추가 다운로드 넷째, 모바일 디자인 기준으로 하위 범주는 호환성, 기능, 오프라인 접근성 다섯째, 개인 정보와 자료의 보호 및 저작권 기준으로 하위 범주는 개인 정보 보호, 지적소유권, 데이터 보관 및 내보내기 여섯째, 사회적 실재감 기준으로 하위 범주는 협동적 학습 환경, 사용자 책임, 확산성 일곱째, 교육 과정에서 교육 주체들의 역할 기준으로 하위 범주는 학습 과정의 촉진, 맞춤식 교수, 학습 분석 여덟째, 학습자의 인지적 능력 기준으로 하위 범주는 인지적 작업의 질 향상, 차원적 사고, 메타인지를 포함한다. 제시된 루브릭은 확장과 변형을 통해 더욱 다양한 온라인 학습 도구의 선택과 활용에 적용될 수 있을 것이다. Since COVID-19, online learning tools such as Zoom and Webex have been applied in various fields with user-customized convenience. However, despite the various advantages of these tools, significant problems appear in the actual teaching and learning process. Representative problems include the selection and use of inappropriate learning tools, educational content designed without properly utilizing the characteristics of tools, and poor interactions in educational programs. Therefore, this study presented a rubric that could be applied when selecting an online learning tools and evaluating whether an educational program is made using the selected tool properly. And the presented rubric integrated a relatively wide range of criteria so that it can be appropriately adjusted to the user"s needs. At the final stage, the total of 8 criteria were presented. The suggested rubrics are function, accessibility, degree of skillful expertise, mobile design, personal information and data protection and copyright, social presence, role of educational subjects learners in the educational process, and learner"s cognitive ability. The characteristics that the tool should have were explained respectively. The presented rubrics can be used to select and evaluate the usability of more diverse learning tools through expansion and transformation.
4 Data Fabric Technologies and Their Innovative Applications

Organizational information technology (IT) departments now often use hybrid or multicloud deployment strategies. It is now more difficult than ever to handle data in such a heterogeneous setting. NetApp’s ideal data management system is a “data fabric” that allows for the frictionless integration of various cloud deployment models (including private, public, and hybrid ones). To provide uniformity and authority over data mobility, security, visibility, protection, and access, a data fabric centralizes data management across disparate resources. We shall define the term “data fabric,” outline its design, and provide some examples in this chapter of its use, outline its deployment options, and show you the future of NetApp’s data fabric.
8 Simulation Tools for Big Data Fabric

The big data fabric architecture was developed due to the accumulation of numerous iterations of big data as well as data management institutional arrangements and architecture, which brought together a wide variety of cutting-edge technologies to facilitate the efficient management of data at massive corporations. The foundation for the most recent iteration of big-data enterprise architecture is the evolving technology components, such as big data analytics and cloud computing. Differentiating business systems and using big data analytics may provide large companies a leg up in the market. The issues with data management may be alleviated with the help of big data fabric architecture, which is geared toward commercial enterprises. Additionally, it improves the ability to generate value using efficient big data analytics, which provides valuable business insights. While considering existing instances of similar design and their associated hurdles, this article introduces big data fabric architecture’s potential. Its relative standing may be guaranteed by using a fresh way. Finally, there is a lack of development in the implementation examples and applications of big data fabric framework engineering processes due to the relative youth of this architecture. Big data fabric design research is still in its infancy. Thus, further in-depth studies into how this design might improve a company’s competitive edge are required.
Decision Support System to Help Educational Institutions Determine the Best Learning Method

The learning method is a method used to support learning activities that are tailored to the learning objectives, student perspectives, technological support, and other very diverse factors.This condition makes it difficult for teachers or educational institutions to choose and decide the best learning method that will be used to support learning activities.Decision Support System is a computer system that is used to support decision making by considering assessment criteria and alternative solutions.Thus, the Decision Support System can be used by educational institutions to determine learning methods that are suitable for school conditions.The purpose of this research is to help the best educational institutions according to the conditions of schools, teachers and students by using the Decision Support System.The research method uses a qualitative approach through observations to educational institutions about the difficulty of determining learning methods that are in accordance with school needs.Literature studies are used to strengthen problem findings and identify alternative solutions with information technology.
Recommendation of the Best Majors in Higher Education for Students Career with Big Data

Higher education is an educational institution that provides educational services and offers many courses tailored to the needs of students and industry.The large number of skilled workforces needs in the industry requires higher education to prepare these skilled workers by building and offering various majors at universities.However, many students do not know the right major that suits their needs and desires that are aligned with industry needs.On the other hand, Big Data is a technology that collects very large amounts of data in real time which is then processed to produce important information that is needed by the user.exist in universities by using Big Data.The research method uses a qualitative approach through observation related to the difficulty in determining the choice of majors at the university.Literature studies are used to find alternative solutions with an information technology approach.
The Sentiment Analysis of User Perception on The Peduli Lindungi Application Using Support Vector Machine Algorithm

The Covid-19 virus has become a global pandemic, including Indonesia.Various efforts have been made by the government to reduce the negative impact of this pandemic, one of which is through the launch of the Peduli Lindungi application.This research was conducted to classify the public feedback towards the Peduli Lindungi application based on the review data on the Google Play site.The data collection period starts from July 1 st to November 30 th , 2021.This period was chosen because the government requires the public to participate in preventing the spread of Covid-19 through the Peduli Lindungi application.Initial data labeling uses the Textblob library.The sentiment analysis is classified using the Support Vector Machine algorithm based on two classes, namely positive and negative.The results showed that the classification accuracy performance reaching 81.35%.The Sentiment analysis results are sufficient to display public opinion.The association of words on negative sentiment indicates that users are less satisfied with the performance of the Peduli Lindungi application.Especially in terms of applications, vaccines, certificates, locations, check-ins, registers, and service complaints.Thus, the results of this study are expected to be able to provide information to Peduli Lindungi to focus on improving applications based on complaints written by users and it is hoped that decision makers can improve the performance of Peduli Lindungi applications based on this opinion.
Analysis of Intention Factors Joining Course Using Advertising social media

The objective of this research aimed to analyze the influence of intention factors including attitudes towards social media advertising, celebrity endorsements, the reputation of tutoring, and E-WOM on students' intentions to take tutoring in Palembang City.The method used in this study was quantitative research with a total of 100 respondents.Data was obtained by the online questionnaire method and the analytical technique used was SEM analysis with the Lisrel 8.80 program.The results of this study indicated that attitudes towards social media advertising and E-WOM had a positive effect on students' intentions.Further results also concluded that endorsing celebrities and the reputation of courses had no effect on students' intentions.It was expected for the future, businessmen of courses could use social media advertising and EWOM on TikTok to increase students' intention to join courses.
Can Teacher Wellbeing Improve Teacher’s Quality of Work Life

Teachers must be able to improve the quality of their profession to meet the demands of industry 4.0.A quality profession will not be achieved if teachers do not have quality and prosperous human beings.By utilizing technological developments, teachers can monitor and diagnose the condition of their own welfare on an ongoing basis, to optimize the quality of work life.A Qualified Teacher's Work Life is very important for the teacher himself as an educator, institution and nation and state.Quality work life can be a measure of teacher growth vertically and horizontally along with the growth of the institution.Quality of work life refers to the level of satisfaction, motivation, involvement, and commitment experienced by teachers with respect to their lives at work.The interests of teachers as individuals are still fulfilled just as they fulfil their interests in their work.Encounter Education, psychological traits with Artificial Intelligence called Positive Education is the main goal of this research.One form is self-diagnostic which is able to diagnose psychological conditions to produce optimal quality.This study aims to dig deeper through various literature studies on teacher wellbeing whether it can improve the Quality of Work Life of teachers.The results of the study show that teacher wellbeing is one of the indicators that also determines the quality of work life of teachers.
Analysis of The Influence of Job Incesurity and Work Passion on Job Satisfaction in Outsourcing Employees

Job Satisfaction has an important role in work motivation and turnover intention.At PT XYZ it was found that the value of Job Satisfaction was still low.This study aims to see whether factors such as Job Insecurity and passion have an influence on Job Satisfaction.Quantitative research was conducted on 105 outsourced employees of PT XYZ.The data analysis technique used the Partial least Square-Structural Equation Modeling approach with the SmartPLS version 3.3 program.The results showed that Job Insecurity was proven to have a negative and significant effect on Job Satisfaction and Work Passion was proven to have a positive and significant effect on Job Satisfaction.Of the two variables, Job Insecurity and Work Passion, the most dominant influence on Job Satisfaction is Work Passion because it has path coefficients (+0.582) greater than Job Insecurity (-0.452).
Study on the Application of Long-distance Education in Middle School English Reading Teaching

The important characteristic of modern distance education is to develop various kinds of teaching activities in the network environment. English is the language of the information age. It is the obligation and responsibility of English teachers to construct and perfect English teaching model of network environment by taking advantages of Internet teaching ．This thesis mainly discusses the function and characteristics of net—work teaching, the main teaching procedures of long-distance English reading education and the countermeasures that middle school English teachers must take in order to improve the quality of long-distance English reading teaching.