Reviewerly/exHarmony huggingface dataset:
Feature Selection Technique to Improve the Instances Classification Framework Performance for Quran Ontology

The Al-Quran is the sacred book of Muslims, and it provides God's word in the form of orders, instructions, and guidelines for people to follow to have happy lives both here and in the afterlife. Several earlier research has used ontologies to store the knowledge found in the Quran. The previous study focused on extracting the relationship between classes and instances or the "is-a relation" by classifying instances based on the referenced class. Based on the performance testing of the instances classification framework, the test results show that Support Vector Machine (SVM) with Term Frequency-Inverse Document Frequency (TF-IDF) and stemming operation had dropped the accuracy value to 65.41% when the test data size was increased to 30%. Likewise, with BPNN with TF-IDF and stemming operations. In the Indonesian Quran translation dataset with a test data size of 30%, the accuracy value drops to 57.86%. Instances classification based on the thematic topics of the Qur'an aims to connect verses (instances) to topics (classes) to get an overall picture of the topic and provide a better understanding to users. This study aims to apply the feature selection technique to the instances classification framework for the Al-Quran ontology and to analyze the impact of applying the feature selection technique to the framework with a small dataset and training data. The instances classification framework in this study consists of several stages: text-preprocessing, feature extraction, feature selection, and instances classification. We applied Chiq-Square as a technique to perform feature selection. SVM and BPNN as a classifier. Based on the experiment results, it can be concluded that the feature selection implementation using Chi-Square increases the value of precision, f-measure, and accuracy on the test data size from 40% to 60% in all datasets. The feature selection using Chi-Square and SVM classifier provides the highest precision value with a test data size of 60% on the Tafsir Quran dataset from the Ministry of Religious Affairs Indonesia: 64.36%. Furthermore, the feature selection implementation and BPNN classifier also increase the highest accuracy value with a test data size of 60% in the Quranic Tafsir dataset from the Ministry of Religion of the Republic of Indonesia: 63.09%.
POTENTIALS OF DEVELOPING ARTIFICIAL INTELLIGENCE TECHNOLOGY IN THE SRI LANKAN HOTEL INDUSTRY WITH SPECIAL REFERENCE TO COLOMBO DISTRICT

The intense competition and unpredictable challenges in hotel operations in Sri Lanka have led to the need to adopt new technologies to provide better services to guests and maximise efficiency and effectiveness. Therefore, the main objectives of this study were to examine various Artificial Intelligence tools used in hotels in Sri Lanka, explore managers' perceptions of Artificial Intelligence, and identify barriers to the implementation of Artificial Intelligence in hotels in Sri Lanka. Using a purposive sampling, 15 managers of star category hotels in the Colombo district were selected and interviewed using a structured interview method to explore the objectives. The findings derived from the thematic analysis showed that hotels in Sri Lanka have limited knowledge of AI applications and that the respondents' perception of AI technology is still at an early stage where they doubt and question its effectiveness compared to human resource services. The study found that the use of AI technology has significant potential, especially in new and emerging hotels, as these new hotels tend to engage with tech-savvy guests compared to existing hotels in Sri Lanka. Moreover, technological advancements in artificial intelligence will undoubtedly lead to an unimaginable hotel experience that will increase guest satisfaction in Sri Lanka's hotel industry.
Development of E-Comic-Based Learning Media Using Pixton in Theme 7 Sub-theme 2 in Class V SDN 101775 Sampali T.A 2022/2023

The purpose of this research is to produce a learning media in the form of E-Comic using Pixton which is feasible, effective and practical. The type of research used is research and development (R&amp;D) which has a 4D development model developed by Thiagarajan namely Define, Design, Develop, and Disseminate. The instruments used to collect data were teacher interviews, material and media validation questionnaires, educational practitioner (teacher) questionnaires, and test questions for students. The results of this study show that the validation of media experts gets a percentage of 97.27% after revision with the "Very Eligible" category. Material expert validation by getting a percentage of 96.47% in the "Very Eligible" category. In terms of practicality, this Pixton-based E-Comic learning media is considered practical which is assessed by educational practitioners with a percentage of 92.72% in the "Very Practical" category. Then. The level of effectiveness of this learning media can be seen from the results of the students' pre-test and post-test scores. The pre-test score showed effectiveness only at 46.23% in the "Ineffective" category while the post-test score increased to 81.61% in the "Effective" category. Based on this, it can be concluded that E-Comic based learning media using Pixton is declared feasible, practical and effective to be applied in the learning process in schools.
Two Fish Encryption Based Blockchain Technology for Secured Data Storage

Data security and sharing remains nuisance among many applications like business data, medical data, banking data etc. In this research, block chain technology is built with encryption algorithm for high level data security in cloud storage. Medical data security seems critical aspect due to sensitivity of patient’s information. Unauthorized access of medical data creates major issue to patients. This article proposed block chain with hybrid encryption technique for securing medical data stored in block chain model at cloud storage. New Two fish encryption model is implemented based on RSA Multiple Precision Arithmetic (MPA). MPA works by using library concept. The objective of using this methodology is to enhance security performance with less execution time. Patient data is processed by encryption algorithm and stored at blockchain infrastructure using encrypted key. Access permission allows user to read or write the medical data attached in block chain framework. The performance of traditional cryptographic techniques is very less in providing security infrastructure. Proposed blockchain based Two fish encryption technique provides high security in less encryption and decryption time.
A Comparative Study of Cyberbullying Detection in Social Media for the Last Five Years

The number of users of social media sites has increased nowadays, and while these sites have many benefits, they also have many damages that have grown with the increasing number of users. Among these damages that have spread in social media sites in our time is the phenomenon of cyberbullying. It has become necessary to find solutions to detect it to prevent and hold bullies accountable to reduce the phenomenon of cyberbullying, which has great health and mental effects on the victim in society. There have been many attempts to build models to detect and classify cyberbullying by using machine learning and deep learning algorithms with different sets of data that were collected from social media sites such as Twitter, YouTube, Facebook, Instagram, and others. In this work, we show a group of previous studies that used machine learning and deep learning algorithms in good attempts to detect and classify the phenomenon of cyberbullying.
Comparison of K-Medoids Method and Analytical Hierarchy Clustering on Students' Data Grouping

One sign of how successfully the educational process is carried out on campus in a university is the timely graduation of students. This study compares the Analytic Hierarchy Clustering (AHC) approach with the K-Medoids method, a data mining technique for categorizing student data based on school origin, region of origin, average math score, TOEFL, GPA, and length study. This study was carried out at University X, which contains a variety of architectural styles. The R department, the S department, the T department, and the U department make up one of them. K-Medoids and AHC techniques Utilize the number of clusters 2, 3, and 4 and the silhouette coefficient approach. The evaluation's findings indicate a value. Although there is a linear silhouette between the AHC and K-Medoids methods, the AHC approach (departments R: 0.88, S: 0.87, T: 0.88, and U: 0.88) has a more excellent Silhouette value than K-Medoids (department R: 0.35, department S: 0.65 number of cluster 2, department T: 0.67 number of cluster 2 and program Study U: 0,52). The results of the second approach, which includes the K-Medoids and AHC procedures, are determined by the data distribution to be clustered rather than by the quantity of data or clusters. Based on this methodology, University X can refer to the grouping outcomes for the four departments with two achievements to receive results on schedule.
Implementation of Virtual Fiber Optic Module Using Virtual Reality for Vocational Telecommunications Students

Virtual Reality (VR) technology is a computer technology capable of replicating a real environment into an immersive world. VR is also capable of simulating the user's physical condition so that they are able to interact. In the world of education as a support for educational activities, especially for vocational high school students who are more dominant in practicum, this study aims to show the results of the early stages of implementing Fiber Optic (FO) learning support modules. The module development process is carried out in several stages: scenario preparation, 3D asset development with low poly optimization, scenario integration with 3D assets, triggering every interaction, multiuser settings, and implementation. To build a good VR Fiber Optic application, measurements using OVR metrics are carried out. This measurement aims to determine the reliability of the application that affects the user experience. In the OVR metric measurement, the highest FPS value is 43, and the lowest FPS value is 27. The value obtained is good enough to run VR applications with low poly asset composition. Measurement of the level of user satisfaction is also carried out by using the PIECES Framework. From the measurement results, the value of each variable is in the range of 3.4 - 4.91, which means that users are satisfied with the VR FO application as a virtual learning support module. This research shows that Virtual Reality technology can provide an overview of practicum that is easy to access and not limited by space and material.
Text Classification Using Genetic Programming with Implementation of Map Reduce and Scraping

Classification of text documents on online media is a big data problem and requires automation. Text classification accuracy can decrease if there are many ambiguous terms between classes. Hadoop Map Reduce is a parallel processing framework for big data that has been widely used for text processing on big data. The study presented text classification using genetic programming by pre-processing text using Hadoop map-reduce and collecting data using web scraping. Genetic programming is used to perform association rule mining (ARM) before text classification to analyze big data patterns. The data used are articles from science-direct with the three keywords. This study aims to perform text classification with ARM-based data pattern analysis and data collection system through web-scraping, pre-processing using map-reduce, and text classification using genetic programming. Through web scraping, data has been collected by reducing duplicates as much as 17718. Map-reduce has tokenized and stopped-word removal with 36639 terms with 5189 unique terms and 31450 common terms. Evaluation of ARM with different amounts of multi-tree data can produce more and longer rules and better support. The multi-tree also produces more specific rules and better ARM performance than a single tree. Text classification evaluation shows that a single tree produces better accuracy (0.7042) than a decision tree (0.6892), and the lowest is a multi-tree(0.6754). The evaluation also shows that the ARM results are not in line with the classification results, where a multi-tree shows the best result (0.3904) from the decision tree (0.3588), and the lowest is a single tree (0.356).
Design principles for higher education teacher development

This study moves from the research question: how to design a professional course/concept for the development of higher education teachers’ digital competences? It presents Teknosofikum, a project funded by the Danish Ministry of Higher Education and Science between 2020 and 2023. The outcome of the project is a course for higher education teachers with a focus on digital competence. Grounded in sociomaterial theories, digital competence refers here to the capacity to develop agency towards professional changes driven by technology. It comprises teachers’ abilities to evaluate why, when, how and with what effects to include digital technologies in teaching. The study draws on qualitative and quantitative data gathered through design-based research along the first three course iterations,with a total of 64 participants. The findings show the emergence of three design principles: (i) relational approach to technology; (ii) praxis; and (iii)organized non-linearity. These principles will guide the final format of Teknosofikum course/concept.
COMPARISON OF MACHINE LEARNING ALGORITHMS FOR LAND USE AND LAND COVER ANALYSIS USING GOOGLE EARTH ENGINE (CASE STUDY: WANGGU WATERSHED)

Human population growth and land use and land cover (LULC) change have always developed side by side. Considering selection of a good Machine Learning (ML) classifier algorithm is needed considering the high estimation of LULC maps based on remote sensing. This study aims to produce a LULC classification of Landsat-8 and Sentinel-2 images by comparing the accuracy performance of three ML algorithms, namely: Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM). Dataset comparison ratios were also explored to find the LULC classification results with the best accuracy. Sentinel-2 is better than Landsat-8 regarding Overall Accuracy (OA) and Coefficient Kappa. The comparison ratio of the training and testing datasets with a good level of accuracy is 70:30 on both images with the average OA Landsat-8 and Sentinel-2 being 92.09% and 94.21%, respectively. The RF algorithm outperforms CART and SVM in both types of satellite imagery. The mean OA of the CART, RF, and SVM classifiers was 92.03%, 94.74%, 83.54% on Landsat-8, 93.14%, 96.15%, and 93.34% on Sentinel-2, respectively.
Cultural and Cybernetic Control Matter Corporate Sustainability: The Role of Strategy

This research study explores the relationship between cultural and cybernetic control, cost leadership strategy, differentiation strategy, and corporate sustainability. Utilizing a quantitative methodology, data is collected from a diverse sample of companies representing different industries in Indonesia. Validated questionnaires are used to measure cultural control, cybernetic control, cost leadership strategy, differentiation strategy, and corporate sustainability performance. The collected data is analyzed using statistical techniques such as regression analysis or correlation tests to examine the connections between the variables. The findings of this study will enhance our understanding of how cultural and cybernetic control mechanisms, in conjunction with cost leadership and differentiation strategies, influence corporate sustainability. The outcomes will provide valuable insights and recommendations for organizations aiming to improve their sustainability practices and optimize their competitive strategies.
Customer behavior-based fraud detection of credit card using a random forest algorithm

Credit card use has become necessary due to the rapid growth of e-commerce and the Internet. Because of the growing use of credit cards, the number of scams related to them has also grown. Such issues may be addressed through data science, which, when combined with machine learning, cannot be underestimated. This goal, “Credit Card Fraud Detection,” aims to uncover the structure of a data set using ML (machine learning). There are a variety of strategies that may be used to identify fraudulent activities. The primary objectives of this approach are to achieve the highest possible degree of precision, a high rate of successfully detecting fraudulent activity, and a low number of false positives. Customer behaviors have been included in this proposed work to identify fraudulent activities. The Random Forest Algorithm has the highest accuracy and MCC scores of all the algorithms. It has been found that the random forest algorithm has the greatest accuracy (94.4 percent) in detecting fraudulent credit card activity. Kaggle provided the dataset that was used in the analysis of credit card fraud
VMLHST: Development of an Efficient Novel Virtual Reality ML Framework with Haptic Feedbacks for Improving Sports Training Scenarios

This paper presents the development of a novel virtual reality (VR) machine learning (ML) framework that incorporates haptic feedback to improve sports training scenarios. The framework uses You Look Only Once (YoLo) for object detection, and combines it with ensemble learning to analyze the performance of athletes in a simulated environment and provide real-time feedbacks. The system includes haptic feedback devices that are controlled via Grey Wolf Optimization (GWO) to simulate the physical sensation of a real-world sports scenario, allowing athletes to experience the sensation of force, impact, and movements. The proposed system was tested using a group of professional athletes who participated in various sports, including football, basketball, and tennis. The athletes were asked to perform various training scenarios in the virtual environment, and their performance was compared with their real-world performance levels. The results showed that the proposed system improved the athletes' performance significantly, as they were able to receive immediate and accurate feedback on their movements, and the haptic feedback provided a realistic sensory experience that enhanced their trainings. The proposed research has the potential to revolutionize sports training by providing athletes with an efficient and effective way to improve their performance in a set of safe and controlled environments. The system can be customized to suit various sports and training scenarios, and the ML algorithms can be trained on large datasets to improve their accuracy and effectiveness. The incorporation of haptic feedback provides a unique and realistic experience, making the training more engaging and effective under real-time scenarios. The proposed system showcased an accuracy 93.5%, with 3.5% higher precision, and 4.9% higher recall than existing models, which has the potential to enhance athletic performance and revolutionize the way athletes train for different sports.
Formation of Professional Competence of a Future Music Teacher in the Process of Studying the Discipline «The Main Musical Instrument is the Piano»

The purpose of our research is to consider the technology of forming a professional competence approach of a future music teacher in the discipline of the musical instrument, namely in the classroom-piano. We applied a set of methods of scientific cognition, namely: analysis of philosophical, psychological, musical and pedagogical literature, content analysis of the basic concepts of research, observation, introspection, self-assessment, conversations. In the course of the research, a terminological analysis of the concept of “technology for the formation of professional competence” was carried out, criteria and components were identified. Based on the data obtained, the author's approach to the interpretation of this definition is formulated. In the process of considering the features of the organization of the learning environment as an effective technology for the formation of professional competence of a future music teacher in the discipline “The musical instrument is the piano”, the essential characteristics of this discipline were identified, we tried to reveal the mechanisms of action of the technology of professorial competence, namely the mastery, implementation and development of professional musical competencies of students in the piano discipline. Based on the analysis of various points of view on the activity and competence of the individual, we give an explanation of the concept of professional competence of the individual in training. The professional competence and preparedness of future music teachers is manifested in their ability to master and study their profession, to master skills and abilities in organizing the process of musical and pedagogical activity at school.
Sombor Energy of a Graph with Self-Loops

This study aims to extend the notion of degree-based topological index, associated adjacency-type matrix and its energy from a simple graph to a graph with self-loops.Let GS be a graph with k self-loops obtained from a simple graph G, we define Sombor index for GS as SO(GS)where S ⊆ V (G) having self-loop to each of its vertices in S. In addition we investigate some fundamental properties of Sombor eigenvalues, McClelland and Koolen-Moulton-type bound for Sombor energy of GS.Also explores the correlation between Sombor energy of GS and the total π-electron energies associated with the corresponding hetero-molecular systems.
Sentiment Analysis on Movie Review from Rotten Tomatoes Using Logistic Regression and Information Gain Feature Selection

The advancement and development of technology today can have a positive influence on the use of the internet and also on the dissemination of information it contains, including information about the world of cinema. With this convenience, there are many movie reviews that can be obtained easily. Movie reviews are very influential in the various ways movies are available. Thanks to the ease of various information on the internet, the number of movie reviews has become diverse. Therefore, it is necessary to do a sentiment analysis. In this research, the classification method used is Logistic Regression. The method was chosen because it has accurate classification accuracy. In this study, Information Gain was also chosen as a feature selection because it is good enough to do a filter approach in classification. Furthermore, for feature extraction, TF-IDF was chosen because it can overcome data imbalance in the dataset. The best model resulting from this research is a model built without using stemming in the preprocessing stage, without using information gain feature selection, and using parameters in Logistic Regression which produces an f1-score of 76.50%.
The Trends of Online Reference Choice in Postgraduate Students of FKIP UNIB as a Source of Reference in Writing Scientific Works

This study aims to reveal the tendency of online reference choices used by Postgraduate students of FKIP UNIB in writing scientific papers. The research method used is the descriptive method. The reachable sample in this study totaled 221 people consisting of 38 third-graduate students and 183 second-graduate students. The collection was carried out using an online questionnaire with Google Forms media and interviews. The results of the study show that in general online journals occupy a very dominant position as references in writing scientific papers. The tendency of its use as a reference is more as a theoretical reference than as a fact, supporting or strengthening research results written in scientific papers. From a language standpoint, journals in Indonesian (L1) tend to be the preferred reference source in writing various types of scientific papers. The driving factors for this are limitations in mastering foreign languages and the desire to quickly complete studies.