Reviewerly/exHarmony huggingface dataset:
ANALISIS k-MEDOIDS DENGAN VALIDASI INDEKS PADA IPM DAERAH 3T DI INDONESIA

Human development is a development paradigm that places humans as the main target of all development activities, namely controling over resources, improving health and improving education. The Human Development Index (HDI) in Indonesia varies in each district, especially in the 3T areas. The 3T area is an area that is classified as underdeveloped, remote and outermost in terms of economy, health, education and infrastructure. The k-Medoids method is a partitional clustering method for grouping several objects into clusters. This clustering algorithm uses the medoid as the center of the cluster, so it is robust to data containing outliers. This study aims to classify the 3T regions in Indonesia based on the Human Development Index to find out which areas require more attention from the government in optimizing the Human Development Index numbers. The size of object similarity is calculated by using the Euclidean distance and Manhattan distance, for the selection of the best number of clusters, internal cluster validation, such as Calinski – Harabasz index, Gamma Index, and Silhouette index. The result of this study showed that the best cluster were four by using Euclidean distance measurement, having Calinski – Harabasz index score of 37.15764, Gamma index score of 0.7821181, and Silhouette index score of 0.3354435.
Research on the Impact and Policies of the Central Bank's Digital Currency on Economic Development

At present, with the vigorous development of digital technology, digital technology is increasingly integrated into various fields of economic and social development, and the central bank's digital currency has emerged as the times require.The paper analyzes the contribution of the central bank's digital currency to economic growth, and also discusses the challenges that the central bank's digital currency brings to economic development.Finally, given the existing challenges, the article puts forward corresponding policy suggestions.
Model of NFT Implementation on Web SSO over OpenID Connect and Oauth 2.0 protocols

Single Sign-On (SSO) is a mechanism that allows users to access various services using a single set of login credentials. However, in SSO implementations, there are still challenges related to security and authentication management, particularly attacks targeting the Identity Provider (IDP). To address this, the use of Non-Fungible Tokens (NFTs) as proof of IDP ownership has been proposed as a solution to enhance security in the authentication mechanism. The utilization of NFTs in SSO with OpenID Connect and OAuth 2.0 has the potential to improve security and convenience in the authentication process due to the unique and non-duplicable nature of NFTs. The results of this research present a model and design of SSO with NFTs on OpenID Connect and OAuth 2.0. An SSO application with login, register, and password recovery features was also developed to provide convenience to users during the login process. The findings conclude that the utilization of NFTs in SSO with OpenID Connect and OAuth 2.0 has the potential to enhance security and convenience in the authentication mechanism. Further research is needed to explore aspects such as scalability, in-depth security analysis, testing in real-world scenarios, improvement of integration and interoperability, as well as comparative analysis with other SSO technologies.
Implementation of AHP and Topsis Algorithm on Web-based Application Design to Determine Prospective Winners of Betta Fish Contest

The procurement of beauty competitions from ornamental betta fish has been held throughout Indonesia. The concept of judging and assessment carried out in the ornamental betta fish beauty competition still uses manual bookkeeping. The assessment with the national scale (SNI) is still a concern for the competition participants because of the lack of knowledge about the standard for judging ornamental betta fish contests. DSS (Decision Support System) can be an application development solution to determine the candidate for the winner of the ornamental betta fish contest. The criteria used include color value, neatness value, proportion value, mental value, and appearance value. The calculation method used in conducting this research is AHP and TOPSIS methods. The recommendations made will go through the AHP process for weighting based on the SNI assessment standard, while TOPSIS is used to receive alternatives from users. Then the ranking is based on the preference value generated by the TOPSIS method. This study has obtained the results of the application of the AHP and TOPSIS methods to make a recommendation system for contestants for winning ornamental betta fish correctly. Testing the level of user satisfaction obtains satisfaction results measured through the EUCS (End User Computing Satisfaction) dimension with the help of a Likert Scale calculation, so that it gets a satisfaction value of 87.89%
The Level of the English Proficiency Affecting the Academic Performance of the Freshmen College Students at Gordon College, Olongapo City

The objective of the study is to determine the level of proficiency in the four English language skills of freshmen students of selected courses at Gordon College and established its relationship to academic performance. The design used was descriptive method with the questionnaire as the main instrument in gathering the data. The researcher devised new research questionnaire that would suit to present study. The researcher made a 50-item English Proficiency Test which covered the four language skills in English which are reading, listening, writing, and grammar. It was concluded that: 1. Typical freshman student-respondent is a female, 17 years old, whose parent is college graduate or a college undergraduate, enrolled as BSHRM, BCSC/IT or BSBA and products of private or public high schools. 2. The level of performance of a student-respondent in reading is excellent (10.27), in listening (8.33) and writing (8.1) above average and in grammar (7.69) average. 3. The academic performance in English is above average. 4. There is significant difference in the level of proficiency of student-respondents in the four (4) language skills. 5. There is significant difference in the level of proficiency in the four (4) language skills when respondents are grouped according to age, course, parent’s highest educational attainment and type of high school graduate from but no significant difference when grouped according to sex. 6. There is significant relationship between the level of performance in the four (4) language skills and academic performance in English subjects of the student-respondents.
Students’ Topic Interest in Learning Reading Comprehension

Reading comprehension is the ability of process to comprehend the text or massage, its meaning and integrate with the knowledge readers that they have already known. When they read a text, they can obtain much information about knowledge. In fact, the students still not interest in learning reading comprehension, because it’s boring situation and they cannot understand about the text or massage. The interest topic is one of the strategies can be used by the teacher in teaching learning reading subject for students. It increases the students’ motivation and their interest in learning. Students’ interest is come from within students or from their inside. Two types of interest can influence the students’ interest: first, personal interest is the students only focus a specific activity or topic. Second, situational interest is the students’ interest are not permanent depend on the condition stimulate. In learning, the teacher is a control person to manage the students and the teacher should choice the interest topic in teaching reading comprehension.
Analysis of the Ensemble Method Classifier's Performance on Handwritten Arabic Characters Dataset

Arabic character handwriting is one of the patterns and characteristics of each person's writing. This characteristic makes Arabic writing more challenging if the letter recognition process is based on a dataset of Arabic scripts. This Arabic script has been presented in a dataset totaling 16800, each representing a class of hijaiyah letters starting from alif to yes, consisting of 600 data for each class. The accuracy of the data used can be increased using the ensemble method. By using multiple algorithms at simultaneously, the ensemble technique can raise the level or result of a score in machine learning. This study's primary goal is to evaluate the ensemble method classifier's performance on datasets of handwritten Arabic characters. The classifier uses the ensemble method by applying the proposed soft voting to provide a multiclass classification of three machine learning algorithms, namely, SVM, Random Forest, and Decision Tree for classification. This research process produces an accuracy value for the voting classifier of 0.988 and several other SVM algorithms with an accuracy of 0.103, a random forest with an accuracy of 1.0, and a decision tree with an accuracy of 0.134. The test results used the confusion matrix evaluation model, including accuracy, precision, recall, and f1-score of 0.99.
ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP

Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance. However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP. The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
Explaining Relation Classification Models with Semantic Extents

In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions. We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.
Classification of stroke patients using data mining with adaboost, decision tree and random forest models

A stroke is a fatal disease that usually occurs to the people over the age of 65. The treatment progress of the medical field is growing rapidly, especially with the technological advance, with the emergence of various medical record data sets that can be used in medical records to identify trends in these data sets using data mining. The purpose of this study was to propose a model to classify stroke survivors using data mining, by utilizing data from the kaggle sharing dataset. The models proposed in this study were AdaBoost, Decision Tree and Random Forest, evaluation results using Confusion Matrix and ROC Analysis. The results obtained were that the decision tree model was able to provide the best accuracy results compared to the other models, which was 0.953 for Number of Folds 5 and 10. From the results of this study, the decision tree model was able to provide good classification results for stroke sufferers.
Sentiment analysis of customer satisfaction levels on smartphone products using Ensemble Learning

Increasingly sophisticated technological developments create new ways for people to conduct trading business. An example of this technology application is the use of e-commerce. However, there are conditions where the seller cannot measure the level of satisfaction and identify problems experienced by his customers if it is only based on the rating as the case in smartphones transactions. Therefore, a solution is needed to create a system that can filter negative and positive comments. This study offers a solution to address this issue by using machine learning employing the K-Nearest Neighbors, SVM, and Naive Bayes algorithms with hyperparameters from previous studies. This study applied the ensemble learning method with the Voting Classifier technique, which is an algorithm to combine several algorithms that have been made. From the test results, the highest accuracy was obtained by SVM with an accuracy value of 91.18% while the ensemble learning method obtained an accuracy value of 89.22%. The difference in the accuracy of training and testing for SVM and ensemble learning method is 7.1% and 4% respectively. These results indicate that the ensemble learning method can help improve the performance of sentiment analysis algorithms for comments on smartphone products.
Classification of Lombok Pearls using GLCM Feature Extraction and Artificial Neural Networks (ANN)

This study used the second-order Gray Level Co-occurrence Matrix (GLCM) and pearl image classification using the Artificial Neural Network (ANN). No previous research combines the GLCM method with artificial neural networks in pearl image classification. The number of images used in this study is 360 images with three labels, including 120 A images, 120 AA images, and 120 AAA images. The epochs used in this study were 10, 20, 30, 40, 50, 60, 70, and 80. The test results at epoch 10 got 80.00% accuracy, epoch 20 got 90.00% accuracy, epoch 30 got 93.33% accuracy, and epoch 40 got 94.44% accuracy. In comparison, epoch 50 got 95.55% accuracy, epoch 60 got 96.66% accuracy, epoch 70 got 96.66% accuracy, and epoch 80 got 95.55% accuracy. The combination of the proposed methods can produce accuracy in classifying pearl images, such as the classification test results.
Semantic segmentation of pendet dance images using multires U-Net architecture

As a cultural heritage, traditional dance must be protected and preserved. Pendet dance is a traditional dance from Bali, Indonesia. Dance recognition raises a complex problem for computer vision research because the features representing the dancer must focus on the dancer's entire body. This can be done by performing a segmentation task process. One type of segmentation task in computer vision is the semantic segmentation. Mask R-CNN and U-NET were employed in this task. Since it was first introduced in 2015, semantic segmentation using the U-Net architecture has been widely adopted, developed, and modified. One of the new architectures applied is the MultiRes UNet. This study carries out a semantic segmentation task on the Balinese Pendet dance image using the MultiRes UNet architecture by changing the value of α (alpha) to obtain the best results. This architectural is evaluated by DC score, Jaccard index, and MSE. In this dataset, the alpha value of 1.9 resulted in the best score for DC and the Jaccard index with 98.47% and 99.23% respectively. On the other hand, an alpha value of 1.8 obtained the best score of MSE with 8.20E-04.
Proposal for the complete high-dimensional Greenberger–Horne–Zeilinger state measurement

Abstract A theoretical proposal for the complete analysis of high-dimensional Greenberger–Horne–Zeilinger (GHZ) state is presented in this Letter. We first demonstrate the approach for the complete three-photon GHZ state measurement in three dimensions, and then generalize it to the situation of N -photon system in d dimensions. In our approach, the photonic hyperentanglement and quantum Fourier transform are both utilized. The presented proposal will be useful for the high-dimensional multi-photon quantum computation and quantum communication.
ANALISIS KOMPETENSI GURU WALI KELAS TERHADAP PENGGUNAAN MEDIA AUDIO VISUAL PEMBELAJARAN SD

The purpose of this study was to analyze the competence of homeroom teachers towards the use of audio-visual media for elementary school learning. The research method used is a qualitative method. The type of qualitative research used in this research is descriptive using a qualitative descriptive approach. The results of the study show that the teacher's competency in using learning media is quite good, the teacher utilizes existing learning media such as learning videos from YouTube, but the teacher has not utilized and used learning media in each teaching and learning process, there are times when the teacher only gives assignments during learning.
Arabic text steganography using lunar and solar diacritics

The need to hide essential information has rapidly increased as mobile devices and the internet has overgrown. Steganography is a method created to create hidden communication. Recently, methods have been developed to hide important information using text steganography. This work-study takes advantage of the possibility of concealing data in all diacritics after the two letters &lt;em&gt;ال&lt;/em&gt;)&lt;em&gt;)&lt;/em&gt; in the cover text. In the presented study, we propose a new algorithm in steganography in Arabic text as a cover text. After pre-processing the cover text, the algorithm hides the elements of secret messages inside the Arabic letters by adding appropriate diacritics (like Hamzah Al-Wasl) on the extracted words beginning with (&lt;em&gt;ال&lt;/em&gt;) according to its third letter type (solar or lunar). In the proposed algorithm, the length of the secret message is determined so that the intended recipient can extract the hidden message accurately. The proposed algorithm is robust against the attack because the change in the cover text is small and imperceptible. On the other hand, since Arabic is used as a cover text, the breadth of the inclusion depends on the number of words beginning with (&lt;em&gt;ال&lt;/em&gt;) definition.
Analisis Sentimen Pengguna Twitter Terhadap Grup Musik BTS Menggunakan Algoritma Support Vector Machine

BTS is a music group from South Korea which has global popularity around the world. As a music group that has global popularity, BTS influence on social media is quite large, with this influence making many companies interested in making them Brand Ambassadors (BA). A company can do a sentiment analysis of the public figure they want to make BA, later this sentiment can be taken into consideration for them to be able to determine the BA of their product. Sentiment analysis can also be used to distinguish the attitude of customers, users or followers towards a brand, topic, or product with the help of their reviews. Based on this, this study will analyze the sentiments of Twitter users towards music group BTS, using the Knowledge Discovery Database (KDD) research methodology, with 5 stages namely Data Selection, Data Preprocessing, Data Transformation, Text Mining and Evaluation. By using the Support Vector Machine (SVM) algorithm with a linear kernel, this study will do 3 scenarios with the distribution of training data and testing data 90:10 in scenario 1, 80:20 in scenario 2, and 70:30 in scenario 3. Confusion Matrix is used to evaluate the performance of the algorithm used and the results show that the best performance of the model formed is in scenario 1 and scenario 2.
Encoding method for computer-generated hologram based on curve descriptions

Computer-Generated Hologram (CGH) can be used to detect aspheric surface with high accuracy. In order to improve the encoding efficiency of CGH, this paper proposes an encoding method to describe the engraved fringes in segments using the arc as the primitive. Firstly, the phase contour boundary is obtained, and then the boundary is discretized by Newton iteration method. Finally, the discrete points are encoded with the circular arc as the primitive based on the minimum rms criterion of residual error, and the engraved fringes are obtained. In this paper, the CGH is designed, encoded and fabricated for an aspheric surface. The encoding file is only 7M under the premise of encoding accuracy better than λ /100 rms, which proves that this method can greatly improve the encoding efficiency compared with linear encoding. Finally, all errors of CGH are analyzed and summarized, and the root-mean-square error of all errors was 0.00283λ ,which proves the feasibility of the encoding method in this paper.
PENGARUH MOTIF URBAN FARMING TERHADAP PERILAKU PENGGUNA MEDIA SOCIAL SEBAGAI CYBER EXTENSION

Urban farming is a self-sustainable solution to meet consumption needs by utilizing small plots of land in residential areas.The practice of urban farming has gained recognition with the advancement of information technology, including the utilization of social media.Social media serves as a cyber extension, enabling the exchange of agricultural information through the digital world.Cyber extension is a form of tele-education that facilitates urban farming practitioners in accessing information related to urban farming techniques via social media.This research aims to analyze the influence of urban farming motives on the behavior of urban farming practitioners in using media.The study was conducted in the city of Makassar, where questionnaires were administered to 50 respondents based on predetermined criteria.Regression analysis was employed to analyze the data and address the research objectives.The results indicate that urban farming motives have a significant relationship and influence on the behavior of online media usage in seeking information related to urban farming.The urban farming motive affects the choice of social media platforms used by respondents to gather information about urban farming.Additionally, the motive also influences the attitudes and knowledge of respondents, assisting them in determining their behavior after receiving the information.