Reviewerly/exHarmony huggingface dataset:
PERANCANGAN APLIKASI KAMUS DIGITAL BAHASA LAWANGAN – BAHASA INDONESIA

Local language is a representation of cultural identity. Apart of being used as communication tools, local language also contains valuable values and local language. Central Kalimantan has a lot of undocumented local language, etiher conventially, let alone digitally. The purposes of this research is to provide a digital vocabulaty of Dayak Lawangan in Central Kalimantan. The stages in Rapid Application Develoment (RAD) method was adopted to build the application. The method was chosen because it is suitable for the lack of development time but it still can provide a precise and complies with the stages of making an application. With this applicatioin, it is hoped that the existence of local language of Dayak Lawangan community can be well documented in order to maintain the diversity.
KOMPARASI ALGORITMA NAIVE BAYES DAN K-NEAREST NEIGHBOR PADA ANALISIS SENTIMEN TERHADAP ULASAN PENGGUNA APLIKASI TOKOPEDIA

Tokopedia is one of the leading e-commerce platforms in Indonesia. The use of e-commerce platforms has increased rapidly in recent years. This is due to technological advances, increased internet access, and consumer behavior that prefers to shop online. In today's digital era, user reviews have an increasingly important role in shaping consumer perceptions of a product or service. The purpose of this research is to conduct sentiment analysis on application performance based on user reviews of the Tokopedia application. Researchers made the decision to use sentiment analysis because it is the most suitable method for processing data sets. From 1019 Tokopedia user reviews on the Play Store that were collected, 176 positive reviews and 843 negative reviews were obtained. Then, the data is classified using the Naive Bayes and K-Nearest Neighbor algorithms, then optimized using Particle Swarm Optimization. The results of the research conducted obtained an accuracy of 76.30% for the Naive Bayes accuracy value without feature selection, 74.09% for Naive Bayes results using feature selection. Then the accuracy value obtained for K-Nearest Neighbor without feature selection is 83.10%, and with feature selection is 83.53%. From the results obtained, the effect of using Particle Swarm Optimization selection features on the two algorithms does not have a big impact, there is an insignificant change in accuracy and AUC values which in the Naïve Bayes algorithm actually decreases&#x0D;
Design and Build Learning Media to Read and Write Al-Qur'an Flash-Based

The low motivation of students/santri in learning the Koran is still one of the causes of the low quality of education, especially in the ability to read the Koran. One of the efforts to increase the motivation of students/students in learning to read the Qur'an is by using the appropriate method. Along with the development of information technology, especially in the advancement of multimedia-based teaching media, the teaching of the Qur'an can use media more effectively. The design and development of this media uses a software development model with research stages including: data collection, analysis, design, implementation and report writing. The stages of data collection were carried out using the method of literature study and interviews, while in the analysis stage, the MDLC method was used. At the design stage a structured approach is used. Process design is carried out using flow chart tools. The results of the process design are used as a basis for developing user interface designs. The design of the resulting learning media was developed using Adobe Flash CS 4 and Adobe Flash CS 6 software. With this design, it is hoped that TPA Miftakhul Jannah Mosque can increase students' learning motivation in reading the Qur'an
SISTEM PENDUKUNG KEPUTUSAN PEMBERIAN KREDIT MENGGUNAKAN METODE ELECTRE (Studi Kasus : Koperasi Kredit Immaculata)

The Immaculata Credit Cooperative is a cooperative organization whose mission is to advance the Immaculata Credit Cooperative as a reliable, independent and professional microfinance empowerment institution. One of the cooperative sectors is the credit sector because credit is a source of financing for cooperatives. Lending at the Immaculata Credit Cooperative is currently still subject to manual analysis by the credit committee, so that an inaccurate determination of credit granting can increase the number of bad or default loans. Therefore the researcher proposes to build a decision support system using the electre method where the output of the electre method calculation is in the form of ranking so that it can determine recommended prospective customers by looking at 5 assessment criteria including income, length of time to return, occupation, age, and collateral. The results of this study are in the form of desktop-based applications that can make it easier for cooperatives, especially credit committees, to determine which customers to recommend.
MODEL DETEKSI SERANGAN SSH-BRUTE FORCE BERDASARKAN DEEP BELIEF NETWORK

&#x0D; &#x0D; &#x0D; &#x0D; Deep Belief Networks are deep learning models that utilize stacks of Restricted Boltzmann Machines (RBM) or sometimes Autoencoders. Autoencoder is a neural network model that has the same input and output. The autoencoder learns the input data and attempts to reconstruct the input data. The solution in this study can provide several tests on DBN such as detecting recall accuracy and better classification precision. By using this algorithm, it is hoped that we as users can overcome problems that occur quite often such as brute force attacks in our accounts and within the company. And the results obtained from this DBN experiment are with an accuracy value of 90.27%, recall 90.27%, precession 91.67%, F1-score 90.51%. The results of this study are the data values of accuracy, recall, precession, and f1-score data used to detect brute force attacks are quite efficient using the deep model of the deep belief network. &#x0D; &#x0D; &#x0D; &#x0D;
Pengembangan Media English Vocabulary Card di UPTD SD Negeri 017107 Kisaran Naga

The background for the development of English Vocabulary Card media is to look at the state of learning media in general. In elementary schools the use of media that speaks vocabulary is still very minimal. Even though learning media is needed to make it easier for teachers to transfer knowledge and make it easier for students to understand English vocabulary. This study uses research and development methods or Research and Development (R&amp;D). After developing students' English learning with English Vocabulary Card Media Development at UPTD SD Negeri 017107 Kisaran Naga, learning proved to be more effective. Therefore, the researchers developed English Vocabulary Card media for fourth grade English subjects. This study aims to develop learning media in the form of English vocabulary cards and find out how the appropriateness of the media is so that it produces media that is valid, effective and responsive. The data collection technique in this study was to use a research and development questionnaire to produce English vocabulary card media that was feasible to use based on assessments from media experts, material experts, English subject teachers and students' responses.
Enhancing Monocular Depth Estimation Using an Enhanced Encoder–Decoder Model

&lt;p&gt;Public data was used and is referenced the manuscript.&lt;/p&gt;
A New SyncFusion Amplifier: For Security of IOT Devices

&lt;p&gt;The SyncFusion Amplifier is introduced as an innovative electronic component designed to redefine security in IoT devices. Principles inspired by quantum entanglement are leveraged to establish synchronized correlations between input signals, resulting in amplified and coherent outputs. The behavior, validation, and potential applications of the amplifier are demonstrated using QSPICE simulation and a hierarchical entry block with C++ code. Through detailed analysis, the enhancement of correlated behaviors by the SyncFusion Amplifier is showcased, with applications in cryptography and communication. By generating more robust cryptographic keys and secure communication channels, security vulnerabilities in IoT environments are addressed. The research not only unveils a groundbreaking electronic component but also proposes a paradigm shift in IoT security. As the IoT landscape continues to expand, a timely solution is presented by the SyncFusion Amplifier to fortify data protection, communication integrity, and overall device security. &lt;/p&gt;
Implementasi Game RPG Sebagai Media Edukasi Keterampilan Kewirausahaan Berbasis Mobile

Training in the field of entrepreneurship can be enhanced by using developmental games to educate students. Simulation models for entrepreneurial strategies can be developed through RPG games. The GBL (Game based learning) implementation model was used in this study to provide training for entrepreneurial practice by implementing RPG games. Entrepreneurial practices developed by preparing businesses, investing for businesses in the form of goods, main capital in businesses, determining the prices of products sold, conducting buying and selling transactions, and managing all business needs are used in conducting research using the Development or R&amp;D method. to create a multimedia that is interactive, resulting in an interactive mobile learning experience. The application of role-playing games received a score of "very good" in the entrepreneurship park at Harapan Mulya Vocational School, indicating that it was possible to provide vocational students with teachers who could provide them with information about how to become an entrepreneur.
A Study on the Self-Understanding Group Type of College Students: Focusing on students who participate in PBL classes

Objectives The purpose of this study was to classify latent groups of self-understanding among college students who participated in a PBL class using latent profile analysis (LPA) and to explore the factors that affect this classification.&#x0D; Methods To achieve these objectives, we collected samples from the ‘5th year KEEP II: Korea Education &amp; Employment Panel II’. The variables selected to investigate the factors influencing the classification were ‘PBL class satisfaction’, ‘major satisfaction’, ‘campus life satisfaction’, and ‘life satisfaction and freedom’.&#x0D; Results The results of this study are as follows: First, self-understanding types were classified into three categories: ‘low level’, ‘middle level’, and ‘high level’. Second, significant differences were found in ‘PBL class satisfaction’, ‘major satisfaction’, ‘campus life satisfaction’, and ‘life satisfaction and freedom’. The ‘high level’ group showed the highest levels in all factors. The ‘middle level’ group demonstrated overall levels above average but relatively lower levels of ‘goal execution’. Finally, the ‘low level’ group exhibited low levels in all measured variables, particularly in factors related to decision-making.&#x0D; Conclusions This study is significant in that it examines the characteristics of learners with high and low levels of self-understanding, specifically focusing on those who experienced a specific class type, PBL. Therefore, we suggest that instructors should identify and implement teaching strategies and learning activities that promote self-understanding and enhance the effectiveness of PBL.
Enough of the chit-chat: A comparative analysis of four AI chatbots for calculus and statistics

This article presents a comparative analysis of four AI chatbots with potential utilization in the fields of mathematics education and statistics, namely ChatGPT, GPT-4, Bard, and LLaMA. Our objective is to evaluate and compare the features, functionalities, and potential applications of these platforms within the domains of calculus and statistics. By examining their strengths and limitations, this study aims to provide insights into the selection and implementation of AI chatbots in calculus and statistics to enhance student learning. The results of the comparative analysis reveal that, while not perfect, GPT-4 outperforms ChatGPT, Bard, and LLaMA as a learning tool in calculus and statistics. Findings also reveal that chatbots may have a positive transformational impact on higher education.
A Comparative Study of Machine Learning Classifiers Performance with Feature Extraction for Face Recognition

It is crucial to select the right machine learning classifier for image classification and face recog-nition. This study examines the effectiveness of four different face recognition classifiers - Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Neural Networks. An analysis of the Large Faces in the Wild (LFW) dataset was carried out using Principal Component Analysis (PCA). Classifiers are rigorously trained and evaluated based on the extracted features. Comparison of classifier performance is an insightful way to figure out their strengths and weaknesses. Having a visual representation of the classifier&amp;#039;s performance gives a complete understanding of its capabilities. Through the selection of the most appropriate classifier, study results contribute to advancements in image classification, recognition, and biometric identification. The comparison study demonstrated that the Neural Network classifier was exceptionally accurate and proficient in recognizing faces from the LFW dataset when used in conjunction with PCA for feature extraction. According to the comparative analysis, the Neural Network classifier proved exceptionally accurate and proficient at identifying faces from the LFW dataset when combined with PCA for feature extraction.
Image2Brain: a cross-modality model for blind stereoscopic image quality ranking

Abstract Objective. Human beings perceive stereoscopic image quality through the cerebral visual cortex, which is a complex brain activity. As a solution, the quality of stereoscopic images can be evaluated more accurately by attempting to replicate the human perception from electroencephalogram (EEG) signals on image quality in a machine, which is different from previous stereoscopic image quality assessment methods focused only on the extraction of image features. Approach. Our proposed method is based on a novel image-to-brain (I2B) cross-modality model including a spatial-temporal EEG encoder (STEE) and an I2B deep convolutional generative adversarial network (I2B-DCGAN). Specifically, the EEG representations are first learned by STEE as real samples of I2B-DCGAN, which is designed to extract both quality and semantic features from the stereoscopic images by a semantic-guided image encoder, and utilize a generator to conditionally create the corresponding EEG features for images. Finally, the generated EEG features are classified to predict the image perceptual quality level. Main results. Extensive experimental results on the collected brain-visual multimodal stereoscopic image quality ranking database, demonstrate that the proposed I2B cross-modality model can better emulate the visual perception mechanism of the human brain and outperform the other methods by achieving an average accuracy of 95.95 <?CDATA $\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> . Significance. The proposed method can convert the learned stereoscopic image features into brain representations without EEG signals during testing. Further experiments verify that the proposed method has good generalization ability on new datasets and the potential for practical applications.
Competing Liberal Approaches to Problematic Speech: South Korea

In South Korea, speech suppressions are robustly bipartisan. Politically right-leaning actors target ‘pro-communist’ (North Korea) and ‘pro-homosexual’ (LGBT) speech. Left-leaning actors target racist slurs, and especially ‘far-right’ speech that supports Japanese colonial (1910-45) or anti-communist (1948-1987) regimes. Significantly, both right- and left-leaning censors claim to protect democracy and individual rights, specifically victim-rights. To explain, proponents of liberal-democratic values (‘liberals’) diverge on procedural and substantive rights and norms (PRN and SRN). Each PRN restriction is countered by a mix of partisan opponents, who would similarly restrict their enemies, and principled, procedural-rights-norms liberals. Toleration of controversial speakers is also more likely among persons with prior ties (contacts). Korea exemplifies the polarization, backsliding (Haggard and Kaufman 2021), and regime instability (Levitsky and Way 2022) threatening mature democracies; and how shifting coalitions, led by PR liberals, push back against such polarization and corrosion of civil liberties.
E- Learning Berbasis Web dengan Penerapan Model Problem Based Learning pada Mata Pelajaran Rekayasa Perangkat Lunak

The world of education is a dimension that cannot be separated from the influence of technology. One form of technology development used in the world of education today is an electronic learning application known as e-learning. The application of learning is an important point in the application of a learning model and the learning model is one of the determinants of the success or failure of teaching and learning activities to achieve the learning objectives that have been formulated. The learning objectives are not only how students can understand the given theory but also the development of aspects such as cooperation, respect for opinions and problem solving abilities. To fulfill this development, the learning strategy should accommodate students for the development of learning in the world of education. One of the learning models that can be developed and adopted to place students at the center of learning is the application of the Problem Based Learning (PBL) model which is combined in the form of web-based e-learning. The purpose of this study is to design a web-based e-learning learning information system with the application of the PBL model. The software development method in designing this learning information system uses the SDLC (System Development Life Cycle) method using the PHP and MySQL programming languages. The research will be conducted by observing activities and obstacles in the teaching and learning process of Software Engineering subjects at SMK Maritim Nusantara Padang Pariaman. This research produces a Web-based e-learning learning information system with the application of the Problem Based Learning model. With the application of the learning model students can understand the theory and have the ability to solve problems related to these subjects. The system is supported by e-learning applications, so that it can help students learn anytime and anywhere.
Two-way quantum computers adding CPT analog of state preparation

&lt;p&gt;Standard one-way quantum computers (1WQC) combine time symmetric unitary evolution, with asymmetric treatment of boundaries: state preparation allows to enforce chosen initial state, however, for the final state measurement chooses a random value instead. As e.g. pull/push, negative/positive pressure, stimulated emission/absorption causing deexcitation/excitation are CPT analogs, and one can be used for state preparation, there should also exist its CPT analog, referred here as CPT(state preparation) - allowing for additional chosen enforcement of the final state, its more active treatment than measurement. It should act similarly to postselection, but through applied physical constraints (instead of running multiple times). Like pumped to |1&gt; prepared state vs its "unpumped" &lt;0| CPT analog, hopefully allowing to construct two-way quantum computers (2WQC) e.g. hydrodynamical, and hopefully photonic, with influenced both initial and final states. If possible, for example for an instance of 3-SAT problem on n variables, we could prepare ensemble of 2^n inputs with Hadamard gates, calculate 3-SAT alternatives for them, and use CPT(state preparation) to enforce outcomes of all these alternatives to '1'. This way hopefully restricting this ensemble to satisfying given 3-SAT problem, in theory allowing to attack NP problems by simultaneously pushing and pulling information through the system for better control.&lt;/p&gt;
Students Perception of Kahoot Media Utilization in Arabic Language Learning

Learning media is one of the most important parts that affect the success of the student learning process. The selection and utilization of appropriate and interesting media will have a positive impact on the success of student learning. However, the utilization of learning media, especially those based on information and communication technology for learning Arabic, is still not widely found and rarely done. The purpose of this study was conducted to determine the effect of using kahoot media at State Senior High School 4 Jakarta. The method used in this research is quantitative method with survey technique using questionnaire instrument, 10th grade students of State Senior High School 4 Jakarta totaling 50 people as subjects in this study. This research technique aims to determine whether kahoot media has an effect on Arabic language skills by using kahoot media through cellphones. This learning is done using a cellphone because material from printed books has been used as a source by educators. Teachers are expected to be able to utilize learning media as a means of supporting teaching and learning activities to create interactive learning for students so as to improve students' ability to understand lessons delivered by an educator which will affect learning outcomes. Based on the results of this study, it can be seen that the use of learning media games for students can be applied in the learning process and for further researchers can use this learning media in Arabic language Subjects
Probabilistic Prediction Model Using Bayesian Inference in Climate Field: A Systematic Literature

Wildfires occur repeatedly every year and have a negative impact on natural ecosystems. Anticipation of wildfires is very necessary, therefore a prediction model is needed that can produce predictions with a good level of accuracy. One approach to develop probabilistic prediction models is Bayesian inference. The purpose of this research is to review the methods that can be used in developing probabilistic prediction models using the Bayesian approach. The methodology used is Systematic Literature Review (SLR) which can be used to provide a comprehensive review of Bayesian inference research in developing probabilistic prediction models. The research strategy used was the Boolean Technique applied to database sources including Scopus, IEEE Xplore, and ArXiv. The articles used have novelty and ease of explanation of Bayesian methods, especially predictions in the field of climate so that articles are selected based on inclusion and exclusion criteria. The results show that probabilistic models can provide more accurate results than deterministic models. The Bayesian Model Averaging (BMA) method is a widely used method because it is easy to implement and develop so that the prediction results can be more accurate. The development of probabilistic prediction models with a Bayesian approach has great potential to grow as seen from the development of the number of research publications over the past 5 years. The research position of probabilistic prediction models with Bayesian approaches in the field of climate is dominated by the research community in China with the main problems related to hydrology.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//