Reviewerly/exHarmony huggingface dataset:
Effectiveness of Single and Double Exponential Smoothing: SES, ARRSES and Holt’s Linear for Time Series Data Prediction with Trend and Non-seasonal Characteristic (Covid-19 Vaccinate Case)

Purpose of this research is effectiveness the exponential smoothing for predict the time series data which has trend and non-seasonal characteristic. In this research using data case vaccinate of covid-19 1st, 2nd, 3rd and 4th. The advantage of this research is we can choose the best method for this type data. The methodology of this research is quantitative, with analyze data method is exponential smoothing (SES, ARRSES, and HOLT’S linear). The result of data analyze is the predict error for four vaccinate data are very close to zero. SES gets 0% to 0,73%, ARRSES gets 0% to 0,66% and Holt’s linear gets 0% to 0,29%. The result of this research can conclude that exponential smoothing method effective for predict the data with trend and non-seasonal data.
Stock Portfolio Optimization Using Mean-Variance and Mean Absolute Deviation Model Based On K-Medoids Clustering by Dynamic Time Warping

The tendency of investors to choose investments with maximum return and minimal risk causes the need for diversification in a portfolio to form an optimal portfolio. A lot of research on stock portfolio optimization has been conducted extensively, but not many have tried to apply machine learning concepts such as clustering analysis to accelerate the establishment of a model that can have a positive effect on the time and cost efficiency of portfolio management. However, clustering is only limited to determining the optimal stock candidate, so it is necessary to add another optimization model to calculate the portfolio weight. Based on these problems, this study carried out portfolio optimization using Mean-Variance (MV) and Mean Absolute Deviation (MAD) model based on K-Medoids Clustering by Dynamic Time Warping approach using Monte Carlo-Expected Tail Loss for risk analysis. Based on the analysis results, the MAD portfolio is more optimal than the MV portfolio by the MAD portfolio consists of five stocks, namely BMRI shares with a weight of 0.06243, UNTR shares of 0.08658, BBRI shares of 0.10285, BBCA of 0.53623, and KLBF shares of 0.21191 are the best optimal portfolios. The optimal portfolio of the MAD model has a rate of return of 87.836% in May 2017 - December 2022 with a portfolio performance of 0.03704, while the resulting risk level based on Carlo-Expected Tail Loss is 2.2416%.
Return Level Estimation in Spatial Extreme Value Modeling of Bali Sea Currents Velocity using The Smith and Brown-Resnick Max-Stable Process Approach

Bali is the world's second most popular tourist destination in 2023. One of the best tourisms is the beauty of its coasts. Even though it is the best tourism destination, it is not uncommon for disasters to occur in the coastal areas of Bali. One important factor in the occurrence of coastal disasters from waters such as tidal flooding and abrasion is ocean currents. Spatial analysis of sea currents velocity was carried out using the Smith and Brown-Resnick Max-Stable Process Approach. The purpose of this study was to determine parameter estimation and comparison of the results of Spatial Extreme Value modeling with the Smith and Brown-Resnick Max-Stable Process approach, and to determine the Return Level of Bali Sea current velocity for the same period after data testing with the best model. The data used is daily data for the period March 2, 2017 to December 30, 2020. Extreme data selection with Block Maxima uses 14 daily blocks, so there are 100 blocks for each water location. The proportion of training and testing data is 80:20. The training data follows the Generalized Extreme Value distribution and has no pattern trend (stationary). The results of the extremal coefficient measurements ranged from 1.18604 to 1.59485 indicating a fairly strong dependency between locations. The best trend surface model is a model that only has longitude coordinates on the location parameter and latitude on the scale parameter. The estimated value of the spatial parameters of the Smith model tends to be greater than that of the Brown-Resnick model. The Root Mean Square Error and Mean Absolute Percentage Error for the Smith model are 0.15503 and 7.75076%. Meanwhile, the Brown-Resnick model is 0.29576 and 14.12131%. Return Level values for the same period after data testing are classified as strong currents and are respectively 1.20586 m/s, 1.63592 m/s, 1.51322 m/s and 2.13233 m/s for Serangan, Gianyar, Nusa Dua, and Nusa Lembongan Waters. Information on estimated Return Levels is expected to be a consideration that can be used by related agencies such as the Coastal and Marine Resources Management Agency (BPSPL) and the Bali Province Regional Disaster Management Agency (BPBD) as a coastal disaster mitigation effort to make it more effective, efficient and on target.
Bilingual Hate Speech Detection on Social Media : Amharic and Afaan Oromo

Abstract Due to significant increases in internet penetration and the development of smartphone technology during the preceding couple of decades, many people have started using social media as a communication platform. Social media has grown to be one of the most significant components, with several benefits. However, technology also poses a number of threats, challenges, and barriers, such as hate speech, disinformation, and fake news. Hate speech detection is one of the many ways social media platforms can be accused of not doing enough to thwart hate speech on their platform. People in Bilingual and multinational societies commonly employ a code mix in both spoken and written communication. Among these, Amharic and Afaan Oromo language speakers frequently mix the two languages when conversing and posting on social media. The majority of previous study concentrated on identifying either technological favoured language or monolingual hate speech in Ethiopian languages; however, the availability of Bilingual communication in social media hampers the propagation of hate speech via social media. In this work, a Bilingual hate speech detection for Amharic and Afaan Oromo languages were conducted using four different deep learning classifiers (CNN, BiLSTM, CNN-BiLSTM, and BiGRU) and three feature extraction (Keras word embedding, word2vec, and FastText) techniques. According to the experiment, BiLSTM with FastText feature extraction is an outperforming the other algorithm by achieving a 78.05\% percent of accuracy for Bilingual Amharic Afaan Oromo hate speech detection. The FastText feature extraction overcomes the problem of out of vocabulary (OOV). Furthermore, we are working towards including others linguistic features of the languages to detect hate speech and make the resource available to facilitate further research in the area of Bilingual hate speech detection for other under-resourced Ethiopian languages.
An improved CNN‐LSTM network for modulation identification relying on periodic features of signal

Abstract Aiming at the problem that modulated signals' classification and recognition accuracy is not high in the complex and changeable electromagnetic environment, this paper improves the CNN‐LSTM network and proposes a modulation recognition method of the CNN‐LSTM network (Intra‐InterNet) based on the periodic characteristics of the signal. The method uses the feature extraction ability of the convolutional neural network to extract the correlation features within the cycle and between adjacent cycles of different kinds of modulated signals. At the same time, the relevant features of the signals of non‐adjacent cycles are extracted by using the characteristics of the LSTM. This feature extraction method effectively enhances the feature extraction capability of the network by utilizing the periodicity of the modulated signal. Through the identification and verification of various modulation signals, the experimental results show that: under the signal‐to‐noise ratio of −6 dB ∼ 6 dB, the average identification accuracy rate reaches 87.29%, which effectively improves the accuracy of signal modulation mode identification.
Carousel Greedy Algorithms for Feature Selection in Linear Regression

The carousel greedy algorithm (CG) was proposed several years ago as a generalized greedy algorithm. In this paper, we implement CG to solve linear regression problems with a cardinality constraint on the number of features. More specifically, we introduce a default version of CG that has several novel features. We compare its performance against stepwise regression and more sophisticated approaches using integer programming, and the results are encouraging. For example, CG consistently outperforms stepwise regression (from our preliminary experiments, we see that CG improves upon stepwise regression in 10 of 12 cases), but it is still computationally inexpensive. Furthermore, we show that the approach is applicable to several more general feature selection problems.
Kesiapan Masuk Sekolah Dasar dan Motivasi Berprestasi Siswa SD Negeri di Desa Trompoasri Sidoarjo

The research was conducted because of the phenomenon of the readiness of school children to enter elementary school which was carried out by grade 1 children of public elementary schools in Trompoasri village. The purpose of this study was to determine the relationship between elementary school readiness and achievement motivation of public elementary school students in Trompoasri village, Sidoarjo. The hypothesis put forward is that there is a positive relationship between children's readiness to enter elementary school and achievement motivation in grade 1 elementary school students. The sample in this study amounted to 49 students. Sampling was carried out using accidental sampling technique. Data analysis used the product moment correlation technique from Pearson with the hellp of the SPSS 26.0 program. The results of the analysis of this study show a correlation coefficients of rxy = 0.334 (significance 0.019 &lt;0.05), which means that the research hypothesis is accepted, namely that there is a positive relationship between children's readiness to enter primary school and achievement motivation in grade 1 students at SDN Trompoasri Village. This means that the higher the child's readiness to enter elementary school, the higher the achievement motivation, and vice versa, the lower the child's readiness to enter elementary school, the lower the achievement motivation.
The contribution of cyberculture to teacher training

This article presents research into the contribution of cyberculture to teacher training. The general objective of this study was to investigate how digital culture influences and redefines pedagogical practices and training, and to achieve this objective, the following specific objectives were established: to analyze the interaction between network culture and teaching competencies, to investigate the reconfiguration of teaching practices in the digital age, and to examine the ethical and social implications of integrating it into education. The methodology used to achieve these objectives included reviewing relevant literature and analyzing data from secondary sources. The research revealed that this concept has a significant impact on teacher training and practice, orienting pedagogy towards a more collaborative and integrated approach, and concluded that this integration is fundamental to contemporary education. The final considerations highlight the importance of continuing research on the subject in order to improve current knowledge and promote future advances.
Efficient Approach for the Localization of Copy-Move Forgeries Using PointRend with RegNetX

Digital images are one of the modern era's dominant sources of information and communication. However, an image can simply be altered with the existence of several tools for image editing. These altered images can transmit across platforms of social media to influence some people in society and may have both positive and negative effects. Therefore, the development of technology becomes a necessary issue to detect and localize a forgery in an image. Copy-move forgery (CMF) is one of the most popular forgeries. In CMF, the new forgery image is created by copying a part presented in an image and placing it at a different location on the same image. This paper proposes a PointRend as a technique to localize copy-move forgeries. This work also presents the PointRend framework with a lightweight backbone model RegNetX (PointRend-RegNetX) to detect such forgeries. From the comparative analysis of the proposed technique with ResNet-50 backbone on two standard datasets, it has been shown that the proposed model (PointRend-RegNetX) is superior in MICC F-220 and MICC F-2000 datasets for images that contain copy-move forgeries. In instances segmentation of forged regions, the improved model (PointRend-RegNetX) has achieved a mean average precision (mAP) of 88.5% on MICC F-220 dataset and 86.4 % on MICC F-2000 dataset.
PENERAPAN ALGORITMA NAÏVE BAYES UNTUK PREDIKSI PENJUALAN PRODUK ICONNET

ICONNET, which is a product from PT Indonesia Comnets Plus for the retail customer segment, is a new internet provider. ICONNET intends to provide the best for the Indonesian people. They need to have a strategy in order to continue to increase sales of their products. In the sales process, one way to increase product sales is to know the history of product sales to customers. Is it in accordance with the sales target or not. So if the sales target has not been met, it can be used as a performance evaluation, especially for the marketing and sales team. Therefore, the authors created a sales prediction system by implementing the Naive Bayes Classifier Algorithm which can be used to predict sales of ICONNET products based on previously recorded data. Testing the system with the black box method and performance testing. The resulting output is a web-based system. The percentage of accuracy test results are 89.189%. While the results of performance testing obtained a score with grade A (91%). It is hoped thet in the future this system can be developed with more attention to user needs.
PENERAPAN METODE NAÏVE BAYES PADA KASUS GANGGUAN PSIKOLOGIS PADA MASA PANDEMI COVID-19

The COVID-19 pandemic is a type of deadly virus that has become a new cause of stress. Physical and psychological health is the hope of every individual. But not all individuals can enjoy this health because of contracting a disease. Stress experienced by individuals can affect physiological functions, namely there are 4 diseases in psychological disorders including stress, depression, anxiety disorders and social phobia. Therefore we need a web-based system using the nave Bayes method by paying attention to the symptoms experienced which will help psychological disorders in recognizing mental health against Covid-19 transmission during the pandemic. In this study, the data in question is psychological data due to the COVID-19 pandemic. There are many modern algorithms created to diagnose symptoms due to this pandemic, but here the researcher wants to prove that Naïve Bayes can also perform the diagnosis quite well. Nave Bayes has a high accuracy value so that it can be used for various purposes, including for document classification, spam detection and other classification problems. The use of the Naïve Bayes method in this study can be rated as a material for research evaluation. With the Naive Bayes method in the rating, it can be seen from the disease. The results of this study are a desktop program for the diagnosis of stress, depression, anxiety disorders, and social phobias during the covid-19 pandemic.
Sentiment Analysis and Classifying Hashtags in Social Media Using Data Mining Techniques

Big data is one of the important topics which is still open for a wide range of applications for extracting useful information and knowledge for supporting organizations by planning and decision-making. Social media as a technology is an important resource of data, especially because it has been widely used in the last years. A Hashtag is recently one of the most popular features provided by Social media and is used by most social media users to express, share, and retrieve opinions and feelings regarding a specific theme. Hashtag features in social media are used more and more in recent years to discuss and debate important current events by public audience. This paper sheds light on how business can use such sources of information and how needed technical processes can be implemented accordingly. The paper demonstrates sentiment analysis as a scenario for such implementation. The main innovation in this paper is not limited to the technical method used, but rather to focus on the idea of using hashtags as information source in business, which is still rarely addressed in science. This paper will provide a novel model based on text mining techniques to provide a sentiment analysis for classifying business-related Hashtags posted on social media from the customers. The results will be presented and verified through samples of positive, and negative classified comments extracted from the Hashtags for supporting the organization by planning and decision making for generating completive advantages.
Pengaruh Pemahaman Kode Etik Akuntan dan Religiusitas terhadap Perilaku Etis Mahasiswa Akuntansi

The problem of professional ethics in accounting has become the center of public attention so that all professions are required to work professionally. This study aims to analyze the effect of understanding the accountant's code of ethics and religiosity on the ethical behavior of accounting students. This research is a causative research using a quantitative approach. The data used in this study is primary data with data collection techniques using questionnaires. The population in this study were students majoring in accounting for the 2018/2019 academic year, Faculty of Economics, Padang State University. The sample technique used in this research is purposive sampling. The number of samples in this study were 146 students from 231 accounting students at Padang State University. The hypothesis in this study was tested using multiple linear regression analysis with IBM SPSS 22. The results of this study (1) understanding of the accountant's code of ethics has a significant positive effect on ethical behavior of accounting students at Padang State University and (2) religiosity has no effect on ethical behavior of university accounting students Padang Country.
NATURAL LANGUAGE PROCESSING: HEALTHCARE ACHIEVING BENEFITS VIA NLP 

The field of Natural Language Processing (NLP) within computer science presents a complex challenge due to the wide variety of linguistic nuances across different languages. NLP involves dividing language into semantic parts like parts of speech and phrases. Its origins trace back to the early 1940s during World War 2, driven by the need for autonomous language translation machinery. NLP, a part of AI technology, employs tools that concentrate on linguistic-conceptual relationships rather than just textual analysis, structuring and extracting meaningful data from unstructured text. One significant application of NLP is the advancement of the healthcare system. Electronic Health Record (EHR) systems revolutionized medical practice, enabling efficient diagnosis, elimination of errors, and faster treatment initiation. NLP's ability to interpret unstructured data from medical records facilitated quicker and more effective analysis, improving patient care. During the COVID-19 pandemic, EHR systems played a crucial role in coordinating patient care and surveillance. NLP also supports Clinical Decision Support Systems (CDSS), aiding medical decision-making by providing tailored clinical knowledge and patient information. Knowledge-based and non-knowledge-based CDSS utilize artificial intelligence, helping prevent medication errors and improving patient safety. The adoption of Voice Recognition (VR) and speech recognition tools, such as Dragon Medical One, surged among medical professionals globally, enhancing clinical documentation quality and saving time on transcription. NLP's impact extends to clinical trial matching, automating the process of selecting suitable patients based on specific criteria, thereby increasing efficiency, accuracy, and patient safety. The Internet of Medical Things (IoMT) is an emerging technology that connects various healthcare devices and wearable, providing real-time monitoring, improved patient outcomes, and remote patient care. Recent innovations, like the AI-based vision therapy software CureSee and AI's role in detecting Alzheimer's disease, have shown great promise in revolutionizing patient care and early disease detection. To support these advancements, organizations like SyTrue use AI, machine learning, and NLP to improve payment integrity, risk adjustment, and chart review processes, leading to increased efficiency and higher ROI. Moreover, AI-powered clinical note generation using services like Amazon Transcribe simplifies the conversion of speech to text, enhancing medical documentation and facilitating data-driven decision-making. In conclusion, the diverse applications of AI and NLP in healthcare have significantly improved the industry, enabling accurate diagnostics, personalized medicine, predictive analytics, drug discovery, remote monitoring, administrative efficiency, and innovative treatment approaches. As AI continues to evolve, its impact on the healthcare system promises to be transformative, leading to better patient outcomes, reduced costs, and improved accessibility to healthcare services.
Tracking and Identification of Ice Hockey Players

Due to the rapid movement of players, ice hockey is a high-speed sport that poses significant challenges for player tracking. In this paper, we present a comprehensive framework for player identification and tracking in ice hockey games, utilising deep neural networks trained on actual gameplay data. Player detection, identification, and tracking are the three main components of our architecture. The player detection component detects individuals in an image sequence using a region proposal technique. The player identification component makes use of a text detector model that performs character recognition on regions containing text detected by a scene text recognition model, enabling us to resolve ambiguities caused by players from the same squad having similar appearances. After identifying the players, a visual multi-object tracking model is used to track their movements throughout the game. Experiments conducted with data collected from actual ice hockey games demonstrate the viability of our proposed framework for tracking and identifying players in real-world settings. Our framework achieves an average precision (AP) of 67.3 and a Multiple Object Tracking Accuracy (MOTA) of 80.2 for player detection and tracking, respectively. In addition, our team identification and player number identification accuracy is 82.39% and 87.19%, respectively. Overall, our framework is a significant advancement in the field of player tracking and identification in ice hockey, utilising cutting-edge deep learning techniques to achieve high accuracy and robustness in the face of complex and fast-paced gameplay. Our framework has the potential to be applied in a variety of applications, including sports analysis, player tracking, and team performance evaluation. Further enhancements can be made to address the challenges posed by complex and cluttered environments and enhance the system’s precision.