Reviewerly/exHarmony huggingface dataset:
THE EFFECTIVENESS OF DIGITAL MEDIA IN ELEMENTARY SCHOOL DURING THE COVID-19 PANDEMIC

The Covid-19 pandemic has brought big changes to the world of education in Indonesia. Learning that should be done in person or face-to-face turns into online learning. This raises problems for the readiness of schools, teachers, and students in carrying out learning. This study aims to obtain information about the effectiveness of digital media in online learning during the Covid-19 pandemic, whether digital media has been effective and its use is optimal in learning. This research is a qualitative descriptive study using an online survey method. The results obtained were that online learning using digital media at SDN 1 Gambasan Temanggung did not increase student scores because only about 20% of student scores had increased, 50% of students also complained about the lack of facilities and infrastructure that support learning. Teachers also have not been able to use digital media in accordance with the characteristics of learning, this is evidenced by 45% of students answered disagree. Students also do not have higher motivation when using digital media, it is proven that only 5% agree. Based on the research results, it can be concluded that digital media is less effective when the supporting facilities and infrastructure are minimal.
DEVELOPMENT OF DIGITAL-BASED COVID-19 MITIGATION MEDIA (MISIMICOTIN) FOR ELEMENTARY SCHOOL

The objectives of the research are (1) the expansion of digital-based COVID-19 mitigation media literacy (Misimicotin) (2) The suitability of digital-based COVID-19 mitigation literacy media. The research method used for research is a combination of quantitative and qualitative (mixed methods) with research design and development (R D). Based on the research design that has been designed, the stages of R D research in this study are knowing the potential and problems, data collection, product design, design validation, design revision, and testing. Digital-based COVID-19 mitigation media literacy (Misimicotin) was created using Microsoft PowerPoint software which is then converted into an android application using Inspiring Suite 9 and Website 2 APK Builder. Based on the results of 3 expert validators, it was found that the media was quite feasible with an average score of 3.5, while the user response involving 30 elementary school-age respondents found that the average score of user responses was 89% agree and strongly agree so that user responses to learning media are at very good category.
Classification of crimes related to forgery of document

The article is devoted to the consideration of various grounds for classifying crimes related to forgery of documents, based on the content of the norms of the current criminal legislation on the relevant crimes. In view of the absence of a systematized conceptual apparatus, numerous legal terms and constructions for describing crimes related to forgery of documents are analyzed. The analysis of criminal law norms allows us to conclude that there is a common object of encroachment for the crimes under consideration, which is public relations arising in connection with the established procedure for the legal circulation of documents. In order to differentiate criminal liability for forgery on the basis of the presence of common and different signs of the elements of crimes, general and special forgeries should be distinguished, depending on the source of origin of forged documents and the ability of documents to consolidate various legally significant facts. The conclusion is made about the inadmissibility of using synonymous terms falsification, forgery, forgery to define one legal category. We consider it preferable to use the phrase forgery of documents as a separate, independent, and semantically most fully reflecting the essence of such crimes.
THE USE OF SNAKES AND LADDERS MATH TO THE DEVELOPMENT OF MULTIPLE INTELLEGENCES IN ELEMENTARY SCHOOL MATHEMATICS SUMMATION MATERIALS

Each student has a different level of intelligence, as a result their learning needs are also not aligned. Children have multiple intelligences so a strategy is needed to accommodate the intelligence possessed by children. This research was conducted at SD Islam Tepadu Permata Cendekia, Simalungun Regency, to be precise in third grade students. The aim is to see the effectiveness of the Multiple Intelligences Learning Strategy with the help of Snakes and Ladders Math media on addition material in Mathematics. The implementation of this research is in the form of quasi-experimental research with One group pretest posttest design. Data collection is done by applying tests as well as observations. The results of the study showed that there was an increase in the average score of students before and after being given treatment, namely (40 91). So that it can improve children's mathematical logical intelligence and verbal intelligence through tests. In addition, the physical and interpersonal intelligence of students is also seen when the traditional game media is applied. So it can be concluded that the application of Media Snakes and Ladders is effective in developing Multiple Intelligences in class III SDIT Permata Cendekia, Simalungun Regency.
DEVELOPMENT OF ANDROID APPLICATION LEARNING MEDIA USING ISPRING SUITE 9 TO INCREASE STUDENT LEARNING MOTIVATION

The purpose of this research is to develop an android-based learning media in thematic for grade IV elementary schools in Cikalong 1 to increase learning motivation. This research uses the type of research and development, the development model used in this research is ADDIE. The ADDIE development model has five stages namely, analysis, design, development, implementation, and evaluation. The result of the research on the development of the developed learning media obtained an average value of the level validated by material experts and media experts which was 87,05% with the valid category. The results of the questionnaire response from the teacher and 20 students obtained practicality of 91,25% and the result of the student's response questionnaire obtained a percentage of 92,12% and the result of the student motivation test percentage of 93%, meaning that the android application media attract students attention and increase students learning motivation.
Multi-view Re-weighted Sparse Subspace Clustering with Intact Low-rank Space Learning

&lt;p&gt;In this paper, we propose a new Multi-view Re-weighted Sparse Subspace Clustering with Intact Low-rank Space Learning (ILrS-MRSSC) method, trying to find a sparse representation of the complete space of information. Specifically, this method integrates the complementary information inherent in multiple angles of the data, learns a complete space of potential low-rank representation, and constructs a sparse information matrix to reconstruct the data. The correlation between multi-view learning and subspace clustering is strengthened to the greatest extent, so that the subspace representation is more intuitive and accurate. The optimal solution of the model is solved by the augmented lagrangian multiplier (ALM) method of alternating direction minimal. Experiments on multiple benchmark data sets verify the effec-tiveness of this method.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
An Improved Cuckoo Search Algorithm Based on Inertial Weight and Scaling Factor

&lt;p&gt;This paper presents a novel and an improved cuckoo search algorithm (ISCS), different from other techniques, which sets nonlinear decreasing inertia weight and adaptive scaling factor. As the iteration goes on, these two parameters are controlled by two functions to change the iterations dynamically. At the beginning of the iteration, the values of the two parameters are favorable for global search, and at the end of the iteration, their values are more favorable for local optimization. In this work, 23 classical benchmark functions are selected to improve the accuracy and convergence speed by conducting the simulation experiments on CS, ISCS and other three algorithms. The results show that the improved cuckoo algorithm enhances the accuracy of understanding and speeds up the convergence of the curves. Finally, ISCS is applied to probabilistic neural network (PPN) neural network classification and recognition technology, and the results show that the optimization of ISCS can effectively improve the classification accuracy of the test sets, and has diverse applications.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Teaching and learning inspired optimization algorithms: A review

Social Human Behaviour algorithms are the next step in nature inspired algorithms development. In the past decade these are proved to be useful for various optimisation tasks. The paper provided a global preview of existing algorithms of this kind and focused on two specific algorithms, inspired by teaching and learning process: Teaching-Learning Based Optimization and Group Teaching Optimisation algorithms. The algorithms’ structure and flow are thoroughly explained and illustrated. A preview of algorithms’ application is reported, based on the recent research. It is concluded that this kind of algorithms can be aplied in various industry areas and that further research in this field is reqired.
Development of a New Chaotic Maps Cryptosystem with Quadratic Residue Problem

A new fast public key cryptosystem is proposed, which is based on two dissimilar number-theoretic hard problems, namely the simultaneous chaotic maps (CM) problem and quadratic residue (QR) problem. The adversary has to solve the two hard problems simultaneously to recover the plaintext according to their knowledge about the public keys and the cipher-text. Cryptographic quadratic residue and chaotic system are employed to enhance the security of our cryptosystem scheme. The encryption, and decryption are discussed in details. Several security attacks are proposed to illustrate the system shield through chaotic maps and quadratic residue problems. The performance analysis of the proposed scheme show a much improved performance over existing techniques.
Segmentation of Facial Bones from Skull Point Clouds Based on Smoothed Deviation Angle

The human skull was the subject of study in various fields. Segmentation could be a basic tool for better understanding the skull. One of the most challenging tasks was facial bone segmentation. Our previous study had succeeded in segmenting facial bones from skull point clouds, however the quality of the results needed to be improved. In this paper, we proposed a new method to improve the results of facial bone segmentation from skull point clouds. The method consists of three stages: deviation angle extraction, smoothing, and thresholding. Each point in the point cloud was assigned a value based on the deviation angle. These values then went through a smoothing process to clarify the differences between the facial bone region and other regions. Next, thresholding was performed to divide the skull into two regions, namely facial bone and non-facial bone. The proposed method had succeeded in improving the quality of the segmentation results by achieving precision=0.931, recall=0.9854, and F=0.9573.
Mass Classification of Breast Cancer Using CNN and Faster R-CNN Model Comparison

Threat of breast cancer is a frightening type and threatens the female population worldwide. Early detection is preventive solution to determine cancer diagnosis or tumors in the female breast area. Today, machine learning technology in managing medical images has become an innovative trend in the health sector. This technology can accelerate diagnosing disease based on the acquisition of accuracy values. The primary purpose of this research is to innovate by comparing two deep learning models to build a prediction system for early-stage breast cancer. This research utilizes Convolutional Neural Network (CNN) sequential models and Faster Region-based Convolutional Neural Network (R-CNN) models that can determine the classification of normal or abnormal breast image data, which can determine the normal or abnormal classification of breast image. The dataset's source in this study came from the Mammographic Image Analysis Society (MIAS). This dataset consists of 322 mammogram data with 123 abnormal and 199 normal classes. The experimental results of this study show that the accuracy of the CNN and R-CNN models in image classification are 91.26% and 63.89%, respectively. Based on these results, the CNN sequential model has better accuracy than the Faster R-CNN model, because it does not require unique characteristics to detect breast cancer.
Fuzzy Type-2 Trapezoid Methods for Decision Making Salt Farmer Mapping

The need for domestic salt every year has increased, both for consumption and industrial salt. Some of the fisheries service programs include providing assistance to people's businesses, providing geomembrane, and online marketing training. A large number of salt farmers and official work programs have caused the implementation of the program to be less than optimal, resulting in low salt production. This study uses a type-2 fuzzy method by integrating two methods, namely type-2 Fuzzy Analytical Hierarchy Process AHP (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Fuzzy type-2 has higher accuracy than fuzzy type-1 and is more efficient and more flexible in determining the linguistic scale for criteria. The Fuzzy Analytical Hierarchy Process AHP (FAHP) interval is used to determine the weight of the salt farmer mapping criteria. Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), used to determine. The findings of this study are that the indicators that most influence the mapping of salt farmers are land area, marketing, and market. The results of the mapping of salt farmers are the classification of salt farmer class groups and recommendations for improvement for each salt farmer. Hybrid type-2 Fuzzy Analytical Hierarchy Process AHP (FAHP) method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), can be used for mapping salt farmers based on the consistency ratio value below 10 percent, 37 percent enter high class, 28 percent enter the middle class and 35 percent enter low class
Predictions of Indonesia Economic Phenomena Based on Online News Using Random Forest

Economic growth in the first quarter of 2021 based on YoY (Year on Year) is around -0.74%. This figure caused the Indonesian economy to recession after contracting four times since the second quarter of 2020. With positive and negative growth in the value of GDP for each category based on the business sector each quarter, can do future economic growth modelling. The prediction results can be used as an early warning for the government on factors that can maximize and factors that must improve. This study aims to predict the state of economic growth in the next quarter using Random Forest classification. Random Forest combines tree classification and bagging by resampling the data, which reduces the variance of the final model, which is for low variance overfitting. The data used in this study was scrapped from January 2021 to March 2021 on 5 Indonesian online news portals, namely Kompas, Antara, Okezone, Detik, and Bisnis. The independent variable is online news based on GDP category. The dependent variable results from data labelling on each news, up or down, carried out by the Directorate of Balance Sheet of BPS. Based on the calculations with cross-validation of 10, the modelling results obtained 96.51% accuracy, 97% precision, and 97% recall. The random forest method is good for predicting economic growth in the next quarter, namely the second quarter of 2021. Incorrectly predicted only three categories of GDP were: the construction category, the transportation and warehousing category, and the company service category
Gesture Recognition using Conditional Generative Adversarial Networks

Sign language is very useful for giving signs to communication partners, in this case, signs are not only for people with disabilities but can also be used by normal people. Even in children's or adult games, sign language is used as a language or a means of communication with one another. Recognition of sign language using a computer by doing several methods, because the computer does not recognize the image of a sign with a certain meaning. Therefore, it is necessary to train computers to recognize these signs. One of the fields that discusses gesture image recognition is the field of Computer Vision. Where the science of computer vision is able to process the image. In addition, in image processing, it is necessary to carry out deep learning processes such as Convolutional Neural Networks. In the Convolutional Neural Network algorithm, there are also many methods or architectures such as VGG16, VGG19, ResNet-50, DenseNet, Inception_V3, and many more. The use of architecture is used in accordance with existing needs. Therefore, the choice of architecture will determine the model is built or not to build from scratch, only transfer learning or pre-train. Pre-training is done by using the initial model and then using it only. Or do some training. The purpose of this study was to detect sign language using the Generative Adversarial Network (GAN). Actually, the Generative Adversarial Network method is widely used in making synthetic images, but this time the Generative Adversarial Network can also detect images from sign language.
Thirty years in the service of scholarship: the Archaeolingua Foundation

The Archaeolingua Foundation is a particular charitable organisation registered in Budapest, Hungary, in 1991. It was established by Sándor Bökönyi (1926–1994, director of the former Archaeological Institute of the Hungarian Academy of Sciences), Wolfgang Meid (professor of linguistics at the University of Innsbruck, Austria), and the “Computer Applications in Archaeology” Research Team of the Hungarian Academy of Sciences represented by its dedicated members, Erzsébet Jerem and Ferenc Redő. This article offers a brief review of the Foundation’s first thirty years, based on the author’s impressions, with particular emphasis on publication output, a reliable objective measure of its various cultural activities.
Major Security Threats and Attacks that Facing Cloud Computing with the Main Defence Strategies

Cloud Computing (Cloud) enables ubiquitous network access to a pool of shared and configurable resources. It is based on shared services and infrastructure convergence. Cloud computing offers a slew of advantages, including the ability to store large amounts of data and a variety of services, as well as addressing the issue of scarce resources and lowering service costs. Regardless of its benefits,the shift from local to remote computing has created a slew of security concerns and challenges for both consumer and provider. Addressing and evaluating cloud computing challenges is critical. Thus, by discussing cloud computing challenges alongside available and potential solutions, users, developers, and businesses can identify pertinent and timely responses to specific threats, resulting in the best possible cloud computing-based services. The purpose of this article is to discuss the inherent difficulties associated with cloud computing and some critical solutions for overcoming them. This article extracted and analyzed seminal papers in order to contribute to the corpus of literature by highlighting several critical challenges in the cloud computing domain and shedding light on how these challenges affect a variety of domains, including users, sites, and business. The most frequently mentioned challenges include data loss, data breaches, account or service hijacking, insecure interfaces and APIs, malicious insiders, insufficient due diligence, abusive cloud service use, shared technology issues, unknown risk profile, identity theft, business model changes, lock-in, cryptography, cloud data recycling, malware, and untrusted computing. This paper addressed these issues by incorporating previously discovered solutions. There has been discussion of the implications for both researchers and practitioners.
Effect of Hidden Neuron Size on Different Training Algorithm in Neural Network

We use different types of training algorithms in the neural network. But, we cannot say which kind of training algorithm is fast for a given problem. So, in this survey paper, we are trying to find which types of training algorithm are better for categorization problems. For this purpose, we used ten types of training algorithms in the pattern network in MATLAB. We used Levenberg-Marquardt (LM), Bayesian regularization backpropagation (BR), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Scaled Conjugate gradient backpropagation (SCG), Conjugate Gradient with Powell/Beale Restarts (CGB), Fletcher-Powell Conjugate Gradient (CGF), Polak-Ribiere Conjugate Gradient (GDM), One Step Secant (OSS), and Variable Learning Rate Backpropagation (GD) algorithm. In this survey paper, we also check, affects of these algorithms on neural network when we applied different types of hidden neuron size. During this survey we found some new facts. We found that RP, SCG, CGB, CGF and OSS are fastest algorithms. BFG takes more time with respect to hidden neuron size. GDM and GD take more epochs. BR algorithm is not acceptable for image categorization.
QIBLA ACCURARY OF THE MAHLIGAI AND PAPAN TINGGI TOMB COMPLEXES AT CENTRAL TAPANULI

&lt;strong&gt;Ab&lt;/strong&gt;&lt;strong&gt;stract&lt;/strong&gt;:&lt;strong&gt; &lt;/strong&gt;Several gravediggers only use predictions to determine qibla direction during burials without appropriate instruments. Therefore, this study aims to accurately determine the Mahligai and Papan Tinggi tomb complexes’ qibla direction in Barus, Central Tapanuli, using the theodolite. This is descriptive and qualitative research with data collected through interviews, observations, and documentation. The result showed that the qibla direction in the Mahligai tomb complex is deviated by 1°, 11.8&lt;sup&gt;o&lt;/sup&gt;, and 29&lt;sup&gt;o&lt;/sup&gt; to the north, west-south, and south, respectively. Meanwhile, the qibla direction at the Papan Tinggi tomb complex is deviated 13° to the north and is different from the Mahligai tomb complex.&lt;br /&gt; &lt;br /&gt;&lt;strong&gt;Keywords: &lt;/strong&gt;qibla direction, Mahligai tomb, Papan Tinggi tomb&lt;strong&gt;&lt;/strong&gt;&lt;br /&gt;&lt;strong&gt; &lt;/strong&gt;&lt;br /&gt;&lt;strong&gt;Abstrak&lt;/strong&gt;: Beberapa penggali kubur hanya menggunakan prediksi untuk menentukan arah kiblat selama penguburan tanpa instrumen yang sesuai. Oleh karena itu, penelitian ini bertujuan untuk mengetahui arah kiblat kompleks makam Mahligai dan Papan Tinggi di Barus, Tapanuli Tengah secara akurat dengan menggunakan teodolit. Jenis penelitian ini adalah deskriptif kualitatif dengan data dikumpulkan melalui wawancara, observasi, dan dokumentasi. Hasil penelitian menunjukkan bahwa arah kiblat di kompleks makam Mahligai menyimpang masing-masing sebesar 1°, 11,8°, dan 29° ke utara, barat-selatan, dan selatan. Sedangkan arah kiblat di kompleks makam Papan Tinggi menyimpang 13° ke utara dan berbeda dengan kompleks makam Mahligai.&lt;br /&gt; &lt;br /&gt;&lt;strong&gt;Kata Kunci&lt;/strong&gt;: arah kiblat, makam Mahligai, makam Papan Tinggi
Plant Breeding Biomolecular Classification in Quantum Bayesianism (QBism) Physics-Informed Neural Network Architecture

In this brief communication, biomolecular plant breeding multi-classification inference is discussed when leveraging the advantages of Physics-informed Neural Network (PiNN) architecture. Albeit, the expected utility of Partial Differential Equation (PDE) inspired neural networks resides in its performance under limited data availability; a variety of neural network configurations result from PDE inspired machine learning models. Having less emphasis on the theoretical outcomes of the PiNN while paying particular attention to the performance of the model prior to convergence. Biotechnology stands to experience myriad benefits as both plant science and machine learning evolve and cross-pollinate.