Reviewerly/exHarmony huggingface dataset:
PENGARUH PEMBERIAN REINFORCEMENT DAN SELF-EFFICACY SISWA DALAM MENINGKATKAN PRESTASI BELAJAR SISWA

The objectives of this study are 1). To determine how the effect of giving reinforcement in improving student learning achievement, 2). To determine how the effect of providing student self-efficacy in improving student learning achievement, 3). To find out how the effect of giving reinforcement and student self-efficacy in improving student learning achievement. The data collection method used is quantitative research method with survey research type. The population in this study were 40 students of class X SMTK Mardhi Wacana Alor Fanating and the sample used in this study were all grade X students of SMTK Mardhi Wacana Alor Fanating. The results of this study are (1). The results of the analysis of this study show that the acquisition of the R value is 0.096 and the R Squared value is in the range of 0.009. This shows that the reinforcement variable (X1) has a very low influence on the student learning achievement variable (Y). meaning that if the reinforcement value decreases, it will be followed by a decrease in student learning achievement. The R Squared value which is in the range of 0.009 is equivalent to 0.9%. This means that there is a very low influence between reinforcement (X1) on student learning achievement (Y) of 0.9%. (2). The result of this research analysis shows that the acquisition of R value is 0.413 and R Squared value is in the range of 0.170. This shows that the self-efficacy variable (X2) has a negative influence on the student learning achievement variable (Y). meaning that if the self-efficacy value increases, it will be followed by an increase in student learning achievement. The R Squared value which is in the range of 0.170 is equivalent to 17.0%. This means that there is an influence between self-efficacy (X2) on student learning achievement (Y) of 17.0%. (3). The results of the analysis of this study indicate that the reinforcement variable X1 and self-efficacy X2 have a negative influence on the student learning achievement variable (Y). meaning that if the value of reinforcement and self-efficacy increases, it will be followed by an increase in student learning achievement. The R Squared value which is in the range of 0.175 is equivalent to 17.5%. So that the higher the provision of reinforcement and student self-efficacy given, the more student learning achievement will increase.
ANALISIS KESALAHAN LEKSIKAL DAN SINTAKSIS DALAM MENULIS TEKS EKSPOSISI PADA SISWA KELAS X

This study aims to obtain an overview of the lexical and syntactic errors made by class X students of SMA Negeri 1 Bandar Baru Pidie Jaya in writing exposition texts. The method used in this research is descriptive analysis method. The population in this study were all students of class X SMA Negeri 1 Bandar Baru Pidie Jaya, totaling 272 students and who would be determined as the sample in this study amounted to 41 students. The data collection technique used in this study is a test analysis technique in the form of interviews and documentation. Meanwhile, the data analysis technique used in this research is essay which is read data, errors are identified, errors are described, errors are classified, and then the error percentage is calculated. The results of this study indicate that the level of lexical and syntactic errors in class X students of SMA Negeri 1 Bandar Baru is 3.69% for lexical errors and 42.39% for syntactic errors. Therefore, it can be said that class X students of SMA Negeri 1 Bandar Baru can already write expository essays well. This can be said to be good based on the results of research in which there are very low category of lexical errors and low category of syntactic errors
Identifikasi Nilai Keekonomian Survey Hidrografi-Geofisika menggunakan Peralatan Multibeam Echosounder (MBES)

The Multibeam Echosounder is a tool for determining the depth of water by sweeping large areas of the seabed. The operating principle of this tool in general is based on the emission of pulses that are emitted directly towards the seabed and after that acoustic energy is reflected back from the seabed (seabel), several sound beams (beams) are electronically generated using signal processing techniques so that the beam angle is known. . Multibeam echosounder can produce high-resolution bathymetry data. The Multibeam Echosounder is characterized by the following parameters Frequency in the range 12 to 500 kHz. Sweep angle range is between +75 ͦto 85 ͦ, Accuracy generally decreases as swath width increases. The width of the sweep is 4x to 5x the water depth and the addition of the swath width depends on the depth of the survey. Using Multibeam Echosounder (MBES) acoustic equipment can improve data quality and the survey area coverage is able to obtain data coverage with a wider area, more detailed data as well as time efficiency and cost effectiveness..
Quantifying quantitative correlation of provider selection influences cloud security

The cloud computing has been able to help users to access the data easily and effectively. However, cloud security is highly emphasized to cloud users to ensure data is securely stored. The cloud security can be handled well by chosen trusted cloud service provider in getting high impact on the cloud security. The relationship between cloud security with the provider selection is much needed to ensure the extent to which data is securely stored in a cloud. Therefore, in this paper the quantitative method was conducted to measure the correlation between the selected the right cloud service provider influence the cloud security. Thus, knowledgeable person in having the experiences in using the cloud service was taking from two institution of higher learning (IHL) as a respondent. In addition, variability and normality data analysis was firstly conducted to obtain the consistency of the data. Then, the correlation between cloud security factor and provider selection factor was conducted using spearman correlation matrix and scatter graph in identifying the closely and significant the value in influencing between the factors. Thus, the correlation relationship analysis result shown the selected the right cloud provider’s give higher impact to cloud security.
Social mobility and geo-context aware macroscopic routing scheme for mobile opportunistic network

Mobile opportunistic networks (MONs) are deliberated as important aspect to proliferate the wireless communications. These networks pose several challenges such as network lifetime, storage capacity, and forwarding capacity. End-to-End routing schemes are considered as promising technique solution to deal with these issues. In this domain, opportunistic routing has gained huge attention because it follows the broadcasting nature of wireless communication and focus on selection of relay node for packet transmission to ensure the better quality of service (QoS) and energy efficiency. This work focuses on optimizing the next hop selection process and introduced reinforcement learning approach which considers distance, energy and link connectivity to assign the reward for different actions to identify the suitable relay node. Moreover, geo-context and social behaviour based opportunistic routing models are used to increase the reliability of next hop selection. Similarly, social model considers social profiling, social connectivity, and social interaction model to identify the relay node. The outcome of proposed approach is compared with several existing approaches such as prophet, spray and wait, and epidemic routing in terms of packet delivery, and network overhead. The relative study shows that the proposed approach achieves the average packet delivery as 47.22% and minimizes the network overhead.
GPS-based fall detection system for old and specially-abled people

Falls are a serious public health concern for older people across the world. Modern telemedicine now depends heavily on remote monitoring of older patients and the ability to spot threats to human health. If a fall is not assisted in time, it can significantly reduce an older person's mobility, independence, and his/her quality of life. Older people who experience post-traumatic problems or mortality frequently do so because of falls. As a result, preventing falls consequences or providing essential help on time may depend on the early identification of falls. In this article, we propose an internet of things (IoT) based system that makes use of low-power wireless sensor networks, smart devices and cloud computing to detect falls and track positions for older and specially-abled people. The tracking is done by sending links of positions from the proposed system every 15 seconds to a specified google drive. On the other hand, an alert message will be delivered to the caregiver whenever a fall is happened. Thus, a MPU-6050 sensor and NEO-6M global positioning system (GPS) module are used with ESP32 microcontroller for the aforementioned purposes. A pilot study with several protocols was carried out to validate the cost-effective proposed system and achieved good results.
Kidney stones detection based on deep learning and discrete wavelet transform

&lt;p&gt;The problem of the research is to find medical images of purity, high quality and free of impurities, which contributes to enabling doctors to obtain the results of analyzing the health status of each patient according to his disease data. Therefore, it was necessary to use discrete first chebysheve wavelets transform (DFCWT) technique in order to remove the associated impurities that appear in the medical images, and then analyze the results for all of the above, the algorithm DFCWT has been combined with and linking it to a neural network based on convolutional neural network (CNN) and this contributes to obtaining the results of analyzing image data with high accuracy and speed. The new algorithm proposed in this paper is based on deep learning finding the identification of kidney stones using DFCWT and the same process can be repeated on skin cancer, bones and fractures, processing by discrete first chebyshev wavelet transformation convolution neural network (DFCWTCNN).&lt;/p&gt;
Document retrieval using term term frequency inverse sentence frequency weighting scheme

The need for an efficient method to find the furthermost appropriate document corresponding to a particular search query has become crucial due to the exponential development in the number of papers that are now readily available to us on the web. The vector space model (VSM) a perfect model used in “information retrieval”, represents these words as a vector in space and gives them weights via a popular weighting method known as term frequency inverse document frequency (TF-IDF). In this research, work has been proposed to retrieve the most relevant document focused on representing documents and queries as vectors comprising average term term frequency inverse sentence frequency (TF-ISF) weights instead of representing them as vectors of term TF-IDF weight and two basic and effective similarity measures: Cosine and Jaccard were used. Using the MS MARCO dataset, this article analyzes and assesses the retrieval effectiveness of the TF-ISF weighting scheme. The result shows that the TF-ISF model with the Cosine similarity measure retrieves more relevant documents. The model was evaluated against the conventional TF-ISF technique and shows that it performs significantly better on MS MARCO data (Microsoft-curated data of Bing queries).
Performance analysis of bitcoin forecasting using deep learning techniques

The most popular cryptocurrency used worldwide is bitcoin. Many everyday folks and investors are now investing in bitcoin. However, it becomes quite difficult to evaluate or foresee the price of bitcoin. The price of bitcoin is extremely difficult to forecast due to its swings. By this point, machine learning has developed a number of models to examine the price behaviour of bitcoin using time series data. The digital money, a different type of payment developed utilising encryption methods, is difficult to forecast. By utilising encryption technology, cryptocurrencies may act as both a medium of exchange and a virtual accounting system. To estimate the values of a future time sequence, this work introduces a deep learning-based technique for time series forecasting that treats the current data as time series and extracts the key traits of the past. To overcome the shortcomings of conventional production forecasting, three algorithms-auto-regressive integrated moving averages (ARIMA), long-short-term memory (LSTM) network, and FB-prophet-were investigated and contrasted. We compared the models using historical bitcoin data of past eight years, from 2012 to 2020. The “FB-prophet” model, which is significant, catches variation that might draw attention and avert possible problems.
Conceptual framework of recommendation system with hybrid method

Recommendation system relies on information of user preference and user behavior in order to recommend the useful information. The existing recommendation systems still have problems for new users and new items. This research proposes a new hybrid method to develop the conceptual framework of recommendation system that deals with new user and new movie data. The data used consists of a data from MovieLens and the internet movie database (IMDB). This work introduces a hybrid recommendation system which based on a combination of content-based filtering (CBF) and collaborative filtering (CF). Pre-filtering data is performed by finding an optimal number of clusters by calculating the total within cluster sum of square. In order to reduce the complexity of data and increase the relevance of the user-item ratings, the fuzzy c-mean (FCM) is employed. Then the similarity is calculated by using item-based method, the K-nearest neighbors and weight sum of the rating are applied. Finally, to recommend the movies, the research found that for new user data the precision is at 85% and mean absolute eror (MAE) value 2.1011. For new item data, the result of research obtains the precision at 87% and MAE value 2.0031. In conclusion, the new hybrid method developed can recommend movie efficiently.
Implementasi Sliding Window Algotihm pada Prediksi Kurs berbasis Neural Network

&lt;p&gt;&lt;em&gt;Time series is sequential data based on time sequence.&lt;/em&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;Time series data can be used for prediction topics, one of the prediction topics that is always interesting to study is exchange rate prediction. In the case of exchange rate prediction, an appropriate data preprocessing stage is required. The success of this preprocessing stage will have a major effect on the resulting RMSE value. There is an important technique in determining the best RMSE value, especially in time series data, one of which is the windowing technique. The windowing technique is the stage of transforming time series data into cross sectional. Window size has an important role in time series data. However, there is no standard in window size. The Window size experiment starts with a small value and then increases to a larger value until it reaches a certain point with the best RMSE. In this research, an experiment will be conducted on windows size on exchange rate data based on a neural network. The purpose of this research is to optimize the RMSE of a data mining model based on windows parameters. The implementation of sliding windows is carried out in the scenarios of window sizes 4, 6, and 28. Based on the experiments conducted, the best RMSE is on windows size 6 = 0.014 +/- 0.000. With a combination of neural network parameters in the form of training cycles = 1000, learning rate = 0.1 and momentum = 0.1.&lt;/em&gt;&lt;/p&gt;
Analisis Pengelolaan Arsip Dinamis Aktif Surat di Sekolah Menengah Atas Negeri 78 Jakarta Barat

Records management is something that is very important and cannot be separated in the management of an organization or agency. For a school, archives are very important as proof of the results of the school's accreditation assessment of the eight components of the national education standard. Therefore, every school must be able to have good archive management to support accreditation activities. This study aims to explain the management of active mail archives at Sekolah Menengah Atas Negeri (SMAN) 78 West Jakarta which already has A Accreditation. there are some things that are still not available. There are several things that are not yet available in the management of active mail archives at SMAN 78, namely: (1) the means of storing letters that are owned are still incomplete; (2) do not have books or proof of borrowing in the use of active mail dynamic archives; (3) does not yet have an Archive Retention Schedule (JRA) and a complete destruction report. These three things happen because the management of active mail archives is still done manually and the limited ability and knowledge of Human Resources (HR) in the archives field, as well as efforts to overcome obstacles have not been carried out optimally. Based on this, the researchers recommend to SMAN 78 that: (1) the management of active mail archives is carried out electronically; (2) Adding types and units of letter storage facilities and infrastructure; (3) Adding human resources devoted to managing letters or improving the capabilities of existing human resources through training in the field of archives, especially regarding archive disposition.
Studies Advanced in Image Classification based on Deep Learning

Accurate and efficient image classification is one of the important research topics in image analysis, and it has also been a research hotspot in the computer vision community.Deep neural networks are increasingly being used for picture categorization and processing in recent years as a result of advancements in machine learning technology.In this article, we introduce the research progress of image recognition technology based on depth learning, including the design ideas, principles, structures, advantages and disadvantages of several depth neural networks. In addition, we quantitatively compared the recognition results of different methods on classic image classification datasets. Lastly, we review some current issues with picture classification and talk about the direction of deep neural network development in the use of image classification technology.
Sturdies Advanced in chatbots based on Deep learning

Chat robots have always been a hot research topic in the field of natural language processing and human-computer interaction, aiming to build models to understand human input and provide logical answers. Early chat robots were mostly based on matching or rules, but due to the lack of manual feature representation capabilities and the complexity of rules, they could not meet practical application requirements. Thanks to the rapid development of deep learning, chat robots based on convolutional neural networks have made breakthroughs in accuracy and speed in recent years. According to the different design ideas of chat robots, the existing work mainly includes two categories: retrieval type chat robots and generation type robots. Focusing on the two main frameworks mentioned above, this article introduces the latest research progress of chat robots in detail, including the basic steps, design ideas, advantages and disadvantages of representative algorithms. We also introduced common chat robot modeling data sets and evaluation indicators. Finally, we discussed the existing problems and future development directions in the field of chat robots.
A Comparative Study of Clinical Risk Prediction Using Limited Patient Electronic Health Records

Predictive modeling of clinical risk using patient electronic health records (EHRs) has the potential to enhance healthcare outcomes by enabling early detection and intervention for high-risk patients. However, dealing with sparse, irregular, and temporal EHR data presents significant challenges. This paper presents a comparative study of clinical risk prediction with limited patient electronic medical records. The related literature is categorized and compared based on research objectives, methods and experimental analysis. Additionally, potential research opportunities for future work in this area are discussed. Meta-learning-based algorithms have the ability to overcome data scarcity challenge by learning shared feature representations. Nevertheless, further research is necessary to address limitations such as the interpretability and generalizability of the model across different patient populations.
Tamil Sign Language Translation and Recognition System for Deaf-mute People Using Image Processing Techniques

This work concentrates on the device, which helps as a translation system for translating sign gestures into text. The disabled people, in particular, hearing and speech impaired people, are facing difficulties in society. Communication of disabled people becomes worse as the majority of ordinary people do not understand it. These disabled people face difficulty communicating with others; some have many problems communicating with others in sign language. It causes the communication gap between them where impaired people cannot share their views and skills with others. We headed to facilitate communication between the disabled people and the "Tamil sign language translator to solve this problem." Here, gestures are translated to Tamil language to find a localized solution. It processes 31 Tamil alphabets, 12 Vowels, 18 Consonants, and 1 Aayudha Ezhuthu. It is 32 combinations with five fingers points either up or down and mapped to decimal numbers. Here in this process, we need edge detection, which is accurately done and Processed by canny edge detection. In addition to this process, we have used two gesture recognition methods and training the input system through our mainframe algorithm called scale-invariant feature detection transform. We have developed this system, which is useful for deaf and dumb people for essential communication.
Review of Generative Models

The advancement of generative AI models has been remarkable since 2022. Several visually appealing generative AI models have been introduced to the public, including those for text and image generation. Despite being generated by large-scale neural networks and deep learning algorithms through extensive training, generative models are capable of achieving average or above-average quality and creativity in many fields, such as painting and literature. This paper will examine some of the AI models currently available, delve into their underlying principles and histories, and provide insight into what the future may hold. With the advancement of technology, we can expect to see even more innovative and creative applications in the future.