Reviewerly/exHarmony huggingface dataset:
The 2D Educational Game Prevention of Covid-19 in Indonesia

The emergence of a corona virus called Covid-19, has caused a pandemic throughout the world, especially Indonesia. Many people do not understand about this virus. The game, which was developed with 2D-based Construct 3, is expected to attract more users’ attention to learn about Covid-19, such as Symptoms, History, Health Protocols, and Vaccines. Then, along with the development of increasingly advanced technology, it will facilitate learning and socialization, namely with this Quiz Game. This study intends to create a Covid-19 quiz educational game developed with quizzes of various levels for users to achieve, with research subjects people in general. This game uses the SDLC (System Development Life Cycle) method with the waterfall method. SDLC waterfall is one of the many system development methods that is quite popular and is often used by system developers, web-based applications, information systems, but not only that SDLC waterfall can also be applied in various other software, one of which is in the development game applications, especially educational games. The test results will use blackbox in the quiz educational game, and run without any errors or defects. The results of the validity and reliability test of the 70 people’s questionnaire are respectively valid and consistent/reliable. It can be concluded that the development of this game can help to educate the Indonesian people regarding Covid-19.
Predição da Cotação Real/Bitcoin usando a Rede Neural Long Short Term Memory

Cryptocurrencies have become an important financial asset for com- panies, governments and civilians, being accepted for financial transactions in place of traditional currencies. However, cryptocurrencies, which are attractive for some reasons such as not having government control, have high volatility, making price predictions important in order to have a basis for deciding when to buy and when to sell this type of asset, in order to maximize profits. As pre- diction by people demands a specialist, which is expensive and also subject to biases and errors, several studies have investigated the application of artificial intelligence computational techniques for prediction. This study investigates the application of an LSTM neural network for predicting the price of the Bitcoin cryptocurrency in Reais, analyzing parameters of the neural network, together with the network modeling for the prediction of a single value for a given future time horizon and the prediction of multiple values for multiple future instants of time. The results obtained indicate that the single output model has a good performance for short-term loss, mainly in the prediction for the next day (horizon 1) with a mean absolute percentage error (MAPE) of 2.57% in its best configuration, however this error is not linearly scalable when the prediction horizon is increased. The multiple-output model produced higher MAPE values for short-term predictions (horizons 1, 3 and 5), however the error increase rate is lower than that of the simple horizon model.&#x0D;
Comparative Analysis of Breast Cancer Detection using Cutting-edge Machine Learning Algorithms (MLAs)

Recently, machine learning techniques have gained popularity for the medical diagnosis. Medical professionals use this approach to learn and detect the abnormalities of life-threatening chronic diseases. The increasing use of ML approaches may be due in part to better disease diagnosis enabled through improved symptom detection. The current study deployed different machine learning algorithms, including Decision Trees (DT), K-Nearest Neighbors (KNN), classifiers Multilayer Perceptron (MP), Support Vector Machines (SVM), and Random Forest (RF) for early predictions and symptoms of the disease. These models were capable of differentiating between benign and harmful cancer cells Benign tumours, which were non-cancerous and in most cases, non-lethal were mostly confined to the area from where they originated, however, it was observed that malignant cancer can start with abnormal cell growth in the human body. This abnormal cell growth can quickly spread to nearby tissues, which can cause infiltration of adjacent cells, resulting in a potentially fatal condition. Thereby, it was observed that Multilayer Perceptron (MLP) model provided the highest accuracy percentage of 86% when compared with all the other techniques in association with the accuracy rate of the models
Three Axis Kinematics Study for Motion Capture Using Augmented Reality

MoCap (Motion Capture) refers to the process of tracking, processing, recording, and translating real-worldmotions into a 3D model. MoCap provides vital realism to animal and human motion not only in entertainmentbut also in other sectors. It is also frequently used in Medical Motion Therapy, robotics, Augmented Reality(AR), and Virtual Reality (VR). In addition to the well-known optical techniques, appropriate systems based onInertial Measurement Unit (IMUs) , the rapid advancement of technology in the manufacture of such IMUsenables the development of compact sensors that may be worn on the body and send motions to a host computer.IMU can estimate the orientation of a rigid body. To compute orientation, IMU technology employs data fromthree sensors: a magnetometer, an accelerometer, and a gyroscope. The movement of any object can bemonitored multiple IMU devices. This study describes the design and execution of a real-time motion capturesystem and a six-degree-of-freedom tracking system using COTS and reduced noise. This includes the generalassembly of the hardware components and the software-side analysis of the obtained motion data and itsimplementation to a 3D model. The proposed design prioritizes flexibility and usability. The results of six DoFs,the overall noise filtration percentages of the system is 10.924 and 40.787 for Gyroscope and Accelerometerrespectively and tracking real-time motion capture reveal that our prototype is competent to collect the valuesof motion parameters and track the motion of objects that can be mated with digital characters on the virtualscreen using AR or VR. Several high-end cameras, as well as unique clothes are required. That makes mocaptechnologies prohibitively expensive. The primary objective for this effort is to investigate a more cost-effectiveand less noisy way to capture movement / postures more accessible to a broader variety of people.
Deep Learning Algorithm for Detecting and Analyzing Criminal Activity

When applied to an entire field, automation and autonomous systems are among the rare creative superpowers capable of catapulting progress at an exponential rate. The arrival of machine intelligence will give such automated machines the intelligence to perform their tasks with power of outcome, drastically reducing the need for human intervention in redundant processes. Large-scale technological progress can be traced back to responsibilities that are simplified and, as a result, more easily distinguished by means of automation. In accordance with these guidelines, we propose creating a product that eliminates or significantly reduces the need for human intervention in primary issue statements that can be automated and processed. The public safety infrastructure of today relies on surveillance cameras, but these devices are merely video recorders; they have no intelligence of their own. Automated video streams are now required for automatic event detection thanks to the massive amount of data produced by surveillance cameras. The project's main objective is to increase public safety through the mechanization of crime measurement and review using actual Closed-Circuit Television footage (CCTV). This is achieved by assigning the task of recognizing criminal behavior to a system that can do so automatically, allowing for more precise tracking. In this study, we present a model with a precision of 0.95 for assault and 0.97 for abuse.
Sentiment Analysis Techniques –Survey

Online social media platforms allow people to express their ideas and opinions about anything as products, education, or politics, this produce a huge data requires appropriate tools to analyze it accurately, and that’s why Sentiment Analysis (SA) has become more used in recent years. This paper review the SA and classification methods of machine learning especially supervised techniques like Support Vector Machine (SVM), Neural Networks (NN), Naïve Bayes (NB), Rough Set Theory (RST), Random Forest (RF), Decision Trees (DT), and other technology of SA is lexicon techniques and hybrid methods (which include the lexicon and machine learning techniques). Machine learning consist of two group supervised (Classification and Regression) and unsupervised (clustering and association). The SA Affected from one system to another based on its polarity as “positive” or “negative”, and the language of data that processed.
ANALISIS KEMAMPUAN DAN KESULITAN MEMBACA PERMULAAN PADA SISWA KELAS II SD NEGERI SAMBONG PERMAI

This research is motivated by the fact that there are students who are not fluent in reading, so students who are not fluent in reading experience difficulties due to a lack of students' ability to spell letters into syllables, and it is difficult to spell syllables into words and a lack of interest in reading. This study aims to describe the reading skills and reading difficulties of the 2nd graders of Sambong Permai Elementary School. The method used in this research is descriptive qualitative using data collection techniques in the form of student observation and interviews with teachers, students and parents of students, and documentation. The subjects of the study were reading skills and initial reading difficulties in grade 2, which consisted of 7 people. The research results obtained showed that in this study of the 7 students studied, 69.57% could read, 17.39% were not fluent and 13.04% could not. The form of difficulty experienced by students in beginning reading is that it is difficult to distinguish letters that are almost the same, difficulty reading syllables so that they cannot unite letters into words or sentences.
Image Encryption Using New Non-Linear Stream Cipher Cryptosystem

In this paper, we designed a new efficient stream cipher cryptosystem that depends on a chaotic map to encrypt (decrypt) different types of digital images. The designed encryption system passed all basic efficiency criteria (like Randomness, MSE, PSNR, Histogram Analysis, and Key Space) that were applied to the key extracted from the random generator as well as to the digital images after completing the encryption process.
How question type influences knowledge withholding in social Q&amp;A community

Abstract Social question‐and‐answer (Q&amp;A) communities are becoming increasingly important for knowledge acquisition. However, some users withhold knowledge, which can hinder the effectiveness of these platforms. Based on social exchange theory, the study investigates how different types of questions influence knowledge withholding, with question difficulty and user anonymity as boundary conditions. Two experiments were conducted to test hypotheses. Results indicate that informational questions are more likely to lead to knowledge withholding than conversational ones, as they elicit more fear of negative evaluation and fear of exploitation. The study also examines the interplay of question difficulty and user anonymity with question type. Overall, this study significantly extends the existing literature on counterproductive knowledge behavior by exploring the antecedents of knowledge withholding in social Q&amp;A communities.
Examples Non-Examples Technique to Improve Students’ Writing Skills at SMK Islam Kunjang

SMK Islam Kunjang is one of vocational high schools in Kunjang district. SMK Islam Kunjang is a relatively developing school in any things; such as the system of administration, relation, or teaching methodology or technique, because it runs for about 14 years old. SMK Islam Kunjang is located suburbs. Absolutely, most students were less in spirit to study more over English. The teacher has been trying to determine the technique in teaching especially teaching writing. The students’ writing was poor. One of the problems was the technique used. Examples Non-Examples technique is one of the cooperative learning strategies that employ images and videos connected to fundamental skills and encourages students to be more creative and engaged in developing their own ideas about the subject that they are observing. In this study, eleventh-grade students at SMK Islam Kunjang in the academic year 2022–2023 were asked to write descriptive texts with examples and without examples to see if this improved their writing abilities. Action research in the classroom is the methodology (CAR). The tools were an observational study, a questionnaire, and a test. This study focused on TKJ 1's eleventh grade, which had 45 pupils. The results showed that the average student in cycle one was 70.80%, whereas the average student in cycle two was 81.66. According to the means of cycles one and two, there was a considerable improvement from 77.78 to 81.66 and 70.80% to 89.68% of students passing the indicator of success, respectively. Additionally, pupils showed higher interest in learning how to write descriptive texts. Therefore, it is advised that students use the examples-not-examples strategy to improve their writing, particularly in descriptive texts.
Kamusku Application as a Media in Reading Online English News

This study aims to find out the students’ perceptions in using the application of the English Dictionary "Kamusku" as a media in reading online English news can make them feel more easier in learning vocabulary and understand the English online news well. This study uses a qualitative study with open ended questionnaires, The instruments used in this method are questionnaires question. This research was conducted on first semester students of English Literature at Wijaya Putra University. The results of the study obtained that the application of the English Dictionary "Kamusku" as a media in reading Online English News had a positive and significant effect on increasing students' English vocabulary to comprehend in reading the online English News.
Communicative Language Teaching for an Enhanced Speaking Competence of Grade 11 Students in Sta. Lucia National High School

This study aimed to determine the effectiveness of communicative language teaching, such as content-based and task-Based instruction, in enhancing the speaking competence of grade 11 students in terms of fluency, vocabulary, grammatical accuracy, and pronunciation. The researcher hopes that the problems encountered in speaking might be alleviated through the study's outcomes and further promotes the use of the approaches used in this study. The use of these approaches paved the way to enhance further speaking competence in fluency, vocabulary, grammatical accuracy, and pronunciation. This might open doors to those students who are hesitant to express their ideas in speaking not only in English subjects but also in other disciplines. Through the study's findings, the possible approaches in further promoting these approaches will be clear and hopefully effective. Meanwhile, the study's findings on enhancing the students' speaking competence using communicative language teaching resulted in significant differences in fluency and grammatical accuracy. Whereas in terms of vocabulary and pronunciation, no significant difference was found. The results revealed that in terms of speaking competence, students have improved. However, the results also show that vocabulary and pronunciation skills are the competence found to have found the least mastered by the students since they are still in the adjustment period from the pandemic. They needed to be given more exposure to the vocabulary and practiced their pronunciation skills.
Feature Weighting for Parkinson's Identification using Single Hidden Layer Neural Network

The diagnosis of Parkinson has become easier with the existence of machine learning. It includes using existing features from the biometric dataset generated by the person to identify whether he has Parkinson or not. The features differ in their discrimination capability and they suffer from redundancy. Hence, researchers have recommended using feature selection for Parkinson's identification. The feature selection aims at finding the most important and relevant features to produce an efficient and effective model. In this article, we present entropy-based Parkinson classification. The goal is to select only 50% of the most relevant features for Parkinson prediction. Two variants of neural networks are used for evaluation, the first one is a feed-forward Extreme Learning Machine ELM and the second one is Fast Learning Machine FLN. Also, the K-Nearest Neighbor KNN algorithm is used for evaluation. The results show the superiority of ELM and FLN when the model of feature selection is used with an accuracy of 80% compared with only 78% when the model is not used.
PENINGKATAN MOTIVASI BELAJAR SISWA KELAS 5 SDN KAWEDANAN 3 PADA PEMBELAJARAN TEMATIK MELALUI PENERAPAN MEDIA POP UP BOOK

This study aims to increase student motivation in thematic learning through the application of pop-up book media. The type of research used is Class Action Research (PTK). The research subjects were 5th-grade students at SDN Kawedanan 3, which consisted of 9 students consisting of 4 male students and 5 female students. The technique used in this study is by using 4 stages, namely the stages of planning, implementation, observation, and reflection. This study aims to increase student learning motivation through pop-up book learning media. The test results obtained were in the form of an average pre-cycle class score of 32.1% to 81.48% in the results of the first cycle, and 97.2% in the second cycle. The conclusion that can be drawn is that pop-up book media is effective for increasing learning motivation in grade 5 students at SDN Kawedanan 3.
An Imperceptible Semi-blind Color Image Watermarking Using RDWT and SVD

A semi-blind color image watermarking scheme using redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) is proposed in this paper. Red, green, and blue channels are extracted from the colored cover image, and 1-level RDWT is applied to decompose it into four sub-bands. LL sub-band is used to embed color watermark by modifying their singular value. At the extraction end, the reverse process of embedding algorithm is followed. Various test images and fundus images are used to evaluate the performance of the proposed technique. Performance parameters peak signal–to-noise ratio (PSNR), structural similarity (SSIM), and normalized correlation coefficient (NC) are measured. Simulation results show the imperceptibility and robustness analysis. Results are also compared with existing techniques, and the proposed method outperforms these techniques.
Influencing factors for water use behaviors of population groups in hospitals based on multi-layer perceptron model

As a public institution with high water consumption, hospitals play an important role in the process of urban water conservation.In this paper, we analyzed the characteristics of daily water use behavior of different hospital populations in different regions and types based on actual research data, and used the multi-layer perceptron (MLP) neural network model to further explore the predictive discrimination and relative importance ranking of gender, age, region and personnel classification on the water consumption behavior of different hospital populations.The results showed that the daily water use behaviors of different regions and types of people were somewhat different; the MLP neural network model had a reference value: different factors influenced each water use behavior to different degrees, with the occupation of hospital personnel influencing the water use behaviors of different people to the greatest extent.By analyzing the daily water use behavior of different groups of people in hospitals, this study can effectively analyze their water use habits and thus reflect their awareness of water conservation, provide a theoretical basis for improving water conservation management in hospital logistics or other departments, and lay the foundation for studying the water use efficiency of people's social activities.
Impact of Boosting Techniques in AI-Based Credit Card Fraud Detection Classifier

In this modern era, with online shopping on the rise, many e-commerce websites and a myriad of different online websites have increased online payment methods and modes, unfortunately increasing the risk of credit card fraud. Hundreds of thousands of people fall victim, each year. This will be susceptible to increase. To avoid massive losses, financial institutions must strengthen their detection and privacy measures. This paper aims to provide a novel approach to recognizing credit card fraud transactions by using machine learning techniques like kNN, decision tree classifier, Random Forest regressor, and further training datasets with AdaBoost and Bagging classifier which increases the accuracy for identifying the fraudulent transaction. Experimental analysis of the proposed approach and findings are based on a legitimate dataset, containing information on European cardholders.
Saraiki Language Hybrid Stemmer Using Rule-Based and LSTM-Based Sequence-To-Sequence Model Approach

Converting a word to its original form, is called stemming, which is extremely important in the field of Natural language processing (NLP). It’s an integral part of the linguistic pre-processing of every Natural language processing application. Stemming converts inflectional word forms into their root word. Much work has been done for stemming in different national and regional languages like English, French, Arabic, German, Urdu, and Hindi. Many regional languages still need work to build digital resources using Natural language processing. Saraiki is one of the widely spoken regional languages in Pakistan. Almost eighty million people use this language for communication. There are very limited digital resources using the Saraiki language available to support advancement in Natural language processing technologies. The current research aims to propose a hybrid stemmer to stem Saraiki Work. The hybrid stemmer contains two hundred prefix and postfix rules and Long short-term memory based sequence-to-sequence model for converting Saraiki words into the stem. Firstly, Saraiki text * Corresponding Author: mubasher@isp.edu.pk was pre-processed, and a rule set was implemented. Secondly, the Long short-term memory based sequence-to-sequence model was deployed to stem the Saraiki word correctly. In the last step, The Saraiki Stemmer performance was evaluated by accurately finding stem word accuracy using a rule-set and Long short-term memory sequence to sequence model. After experiments, using the rule set correctly, stem word accuracy was 68.53%, while the Long short-term memory based sequence-to-sequence model produced 93.0% accuracy of correctly stem words. This work contributes significantly to the regional linguistic field by introducing stemmer for the Saraiki language.
The Analysis of Using Verbs in Descriptive Paragraph in Semester I STBA JIA in Academic Year 2021/2022

This research aimed to describe how the students use verbs in their writing descriptive paragraph. The sample uses are the students of semester 1 class B with the total number of students are 21 students. The instrument uses in this research are test and interview. After analyzing, the results shown that in test 1 students score average are 84.2. in test 2 the average of students score is 88.47. in writing descriptive paragraph, the result shown that there are still some students made mistakes in using to be present and past, I joining to be and verbs, in diction, to infinitive, tenses and missing verb. Based on interview, shown that writing is a difficult subject for some the students. Also, They ignore about grammatical rule when they are writing.