Reviewerly/exHarmony huggingface dataset:
Analysis and Evaluation of Internal Control Accounting Information System for Purchase of Merchandise and Cash Disbursements at PT Bali Kulina Utama According to the COSO Framework Approach

This study aims to evaluate the internal control of the accounting information system for the purchase of merchandise inventory and cash disbursements at PT Bali Kulina Utama. The results of this study are expected to provide constructive suggestions in improving the implementation of controls that can minimize the potential for fraud and fraud in the company's activities at PT Bali Kulina Utama. This type of research is a case study using two types of data, namely primary data and secondary data. Data collection techniques used are observation, interviews and documentation. The analytical technique used is descriptive analysis technique, namely by explaining the accounting information system for the purchase of merchandise inventory and cash disbursements and comparative analysis techniques, namely by comparing the implementation of internal control accounting information systems at PT Bali Kulina Utama with the theory of internal control according to the COSO Framework approach. The results of this study indicate that the accounting information system for the purchase of merchandise inventory and cash disbursements has three activities originating from ordering, receiving, storing and disbursing cash. The results of the evaluation of the implementation of the accounting information system internal control at PT Bali Kulina Utama are not fully in accordance with the five components of the COSO Fraework internal control. Four of the five components that are not in accordance with the COSO Framework are the internal control environment, risk assessment and risk response, control and monitoring activities. An adequate component is the information and communication component.
Metode Decision Tree Dalam Pemilihan Gaya Belajar Pada Siswa Sekolah Dasar

Learning styles have an important role in the process of teaching and learning activities. By recognizing learning styles, a person will be able to determine a more effective way of learning. Learning styles themselves are divided into three types of groups, namely types of visual learning styles, types of auditory learning styles, and types of kinesthetic learning styles. Individuals at the primary education level or in this case students need guidance and supervision during the learning process when at school or home. If students have practiced habits and effective learning styles since elementary school, it will result in proud learning achievements in the future. This study uses a dataset "Data Set Of Learning Style Preference" and uses the Decision Treealgorithm. The results of this study are to identify each student's learning style and can also be useful for teachers, students, parents or school parties who are related and carry out the learning and learning process both at school and at home to choose the right learning strategy and can be used as a reference for developing Student learning styles are following the type of learning and of course, this supports the achievement of an efficient and effective learning process.
Kesehatan Perusahaan Tourism di Masa Pandemi Covid19 dengan Metode K Means Clustering

This research is aimed at seeing whether tourism companies listed on the Indonesia Stock Exchange during the Covid-19 pandemic are still classified as healthy or not. Measurement of the company's health level is carried out using the K-Means Algorithm. The results of the financial performance evaluation are grouped using the K-Means Algorithm. This algorithm will determine groups of tourism companies that are performing well (healthy) and companies that are not performing well (unhealthy) by determining groups based on the closest data distance to the centroid. From the results of the analysis of 32 tourism companies on the Indonesia Stock Exchange, data shows that there are 2 unhealthy tourism companies, namely Indonesia Pure Mas Company (MAMI) and PT. Sarimelati Kencar (PZZA) and 30 other tourism companies are in good health. Thus improvement and development steps are required for these 2 tourism companies .
Character Education as a Means of Second Language Development in Talented Children: A Study of Sociolinguistic

This study aims to describe and explain the role of character education in helping the development of a second language in gifted children based on sociolinguistic studies. This study uses a qualitative method with a descriptive analysis approach. The data in this study were obtained from the analysis of conversations or dialogues and the development of the Dini Suryani language of third-grade students at SDN 27/III Lolo Kecil with their peers, interacting in class with the teacher and while in the family environment. Researchers found that early language development is different from normal children, who tend to have a neat and well-structured use of language. Dini also uses varied and appropriate vocabulary according to specific contexts. Dini also applies the use of intonation and facial expressions in interacting in communicating. However, Dini has a personality problem that is high egoistic, such as when she wants something, she will do anything, even if she forces her will. To maximize the ability of early character education is the right supporting step or method. Thus, Dini can become a gifted child with a good personality.
An Experimental Study on State Representation Extraction for Vision-Based Deep Reinforcement Learning

Scaling end-to-end learning to control robots with vision inputs is a challenging problem in the field of deep reinforcement learning (DRL). While achieving remarkable success in complex sequential tasks, vision-based DRL remains extremely data-inefficient, especially when dealing with high-dimensional pixels inputs. Many recent studies have tried to leverage state representation learning (SRL) to break through such a barrier. Some of them could even help the agent learn from pixels as efficiently as from states. Reproducing existing work, accurately judging the improvements offered by novel methods, and applying these approaches to new tasks are vital for sustaining this progress. However, the demands of these three aspects are seldom straightforward. Without significant criteria and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the previous methods are meaningful. For this reason, we conducted ablation studies on hyperparameters, embedding network architecture, embedded dimension, regularization methods, sample quality and SRL methods to compare and analyze their effects on representation learning and reinforcement learning systematically. Three evaluation metrics are summarized, including five baseline algorithms (including both value-based and policy-based methods) and eight tasks are adopted to avoid the particularity of each experiment setting. We highlight the variability in reported methods and suggest guidelines to make future results in SRL more reproducible and stable based on a wide number of experimental analyses. We aim to spur discussion about how to assure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted.
Detection of Acute Myeloid Leukemia based on White Blood Cell Morphological Imaging using Naïve Bayesian Algorithm

The process of diagnosing AML is based on the complete blood-count analysis of the patients.As such, it involves high energy consumption, long completion times, and is rather expensive compared to conventional medical practices.One of the methods for identifying tumor cells involves the utilization of image-processing techniques based on the morphology of white blood cells (WBCs).The principal objective of this study involves the identification of AML cells-especially of the AML M1 and AML M2 types-through morphological imaging of WBCs using the Naïve Bayes' Classifier.The Image-processing methods used in this study include YCbCr color space classification, image thresholding, morphological operations, chain code representation, and the use of bounding boxes.Regardless of the processing technique used, all identification procedures, performed in this study, were based on the Naïve Bayes' Classifier.The test process was performed on 30 images of each of the AML M1 and M2 cell types.The use of the cell identification method proposed in this study demonstrated an accuracy of 73.33%.While the accuracy of cell type identification is 54.92%.Based on the results obtained in this study, it is inferred that the Naïve Bayes' Classifier method can be employed in the process of identifying dominant AML cell types amongst AML M1 and AML M2 (myeloblast, promyelocyte, myelocyte, and metamyelocyte) based on the morphology of WBCs.
SISTEM PAKAR DIAGNOSA PENYAKIT DAN HAMA PADA TANAMAN JAGUNG MENGGUNAKAN METODE NAIVE BAYES

Corn is one of the leading agricultural commodities that can be used as a staple plant other than rice. Constraints faced by corn farmers include the lack of information about diseases that attack the corn plants, which causes less productivity. In this study a system was developed that can automatically detect disease that attacks corn plants so that preventive measures can be taken to prevent corn plants from dying. Expert systems are designed to solve certain problems by imitating the work of experts. In addition, this expert system also helps farmers who are experiencing problems regarding diseases and pests and their solutions without relying on an expert. The method used is the Naive Bayes method, Naive Bayes is a method used to predict probabilities and has several characteristics that are istitively in accordance with the way of thinking of an expert and accompanied by a strong mathematical basis. From the tests carried out with diagnoses obtained from the comparison between the results of expert diagnoses and the diagnosis of the system to diagnose diseases and pests in corn plants is 90%.
BUILDING LITERACY CULTURE THROUGH PARENT INVOLVEMENT ON EARLY AGED CHILDREN IN SUKARAPIH VILLAGE, TAMBELANG, BEKASI, WEST JAVA

To build Children habit on read to more frequently at an early age required several efforts. Study has been conducted in Sukarapih Village, Bekasi, West Java, Indonesia to examines the effect of parental involvement on children early aged and the effect of child’s own reading and other cognitive skill. Current condition in Sukarapih Village is children’s literacy skill was still low. Some factors effected this condition are (a) Parents did not encourage their child to reading more books therefore child more interest on gadgets or watching television (b) lack of facilities and infrastructure for early age children to explore book from an early age. Furthermore, Pre school and kindergarden is not available in that village. The approach carried out by activities as follow ( 1)Educated parents how importance of reading from early aged (2)Provide reading book corners so that book access easier for children in village. Evaluation activities conducted and give result children has their own as reading with a maximum frequency 3 books per month, drawing or book coloring with maximum frequency 1-2 times per month, and Learning to Write with maximum frequency activities 4 times per month
Deployment of virtual machines for tiered applications in cloud systems with optimized resource allocation based on availability SLAS

Deploying and managing high availability-tiered application in the cloud is challenging, as it requires providing necessary virtual machines to make the application available. An application is available if it works and responds in a timely manner for varying workloads. Application service providers need to allocate specified number of working virtual machine copies for each server with at least a given minimum computing power, to meet the response time requirement. Otherwise, we may end up with response time failures. This thesis formulates an optimization problem that determines the number and type of virtual machines needed for each server to minimize the cost and at the same time guarantees the availability SLA (Service-Level Agreement) for different workloads. The results demonstrate that a diverse approach is more cost-effective than running on a single type of virtual machine, and buying only the cheapest virtual machines for an application is not always economical.
MULTI-OBJECTIVE HYPERPARAMETER TUNING OF CLASSIFIERS FOR DISEASE DIAGNOSIS

Disease diagnosis is an arduous task due to the high cost associated with the misclassifications.The classifiers for disease diagnosis must be accurate, offer a tradeoff among sensitivity and specificity, and address the problem of class imbalance.Further, the ad-hoc hyperparameter settings deteriorate classifiers performance, specifically for disease diagnosis.For disease diagnosis systems, it is essential to address the issues of the setting of hyperparameters and class imbalance simultaneously.The objective of this paper is to propose a Multi-objective Hyperparameter Tuning approach, called MOHPT, in combination with resampling techniques.So far, the hyperparameters of classifiers for disease diagnosis have been often optimized based on a single objective criterion, i.e., accuracy.Multi-objective hyperparameter tuning in combination with resampling techniques is expected to enhance the efficacy of disease diagnosis.We have used decision trees (CART) and random forest as the classifiers.The proposed system MOHPT obtains non-dominated optimal hyperparameter configurations of these classifiers using crowding based sorting technique from multi-objective NSGA-II.The MOHPT uses sensitivity (true positive rate) and specificity (true negative rate) as the two objective functions for optimization, and each configuration in the non-dominated front of hyperparameter configurations belongs to a different tradeoff between sensitivity and specificity.The suggested approach offers a choice to medical practitioners to prefer any of the hyperparameter configurations.However, we have selected the hyperparameter configuration based on the optimum g-mean, a product of sensitivity and specificity.The resampling techniques such as undersampling, oversampling, SMOTE, RWO, MWSMOTE, and ROSE have been implemented to tackle the class imbalance.The MOHPT is tested on 17 medical datasets.Several combinations of decision tree and random forest classifiers have been implemented in combination with hyperparameter tuning and resampling techniques.The results have been validated using appropriate statistical tests.The result shows that the application of hyperparameter tuning in combination with sampling techniques significantly enhances the performance of disease diagnosis.Overall, the performance of the random forest is superior to the other classifiers.The MOHPT comparison with the related works further proves its efficacy.The suggested technique has been successful in finding the set of non-dominated set of solutions for hyperparameter configurations along with addressing the class imbalance problem.The results show a performance improvement on several performance evaluation metrics such as sensitivity, specificity, AUC etc.
Application of Internet of Things and Big Data Analysis Technology in the Dissemination and Inheritance of Light Industry Culture

Light industry is the material basis of urban economic life. After more than 50 years of development since the reform and opening up, historical changes have taken place from small to large, from weak to strong, and a light industry production system composed of multiple industries has been formed. Light industry plays an irreplaceable role in people’s living needs, and the inheritance and dissemination of light industry culture needs to be solved urgently. Based on this, the purpose of this article is to study the propagation and inheritance of light industry culture through the Internet of Things and big data analysis technology. This article first summarizes the development history of light industry, and then introduces the existing problems and threats of light industry. And combined with the core technology of big data analysis such as association rules for application research. This article uses comparative analysis methods, field survey methods and other research methods to study the theme of this article. The research shows that the light industry culture dissemination method based on big data analysis studied in this paper has higher feasibility.
Designing Stress Relieving Game Associated to Virtual and Augmented Reality for Bus Drivers﻿

Stress is a phenomenal problem that affects a large number of worker, regardless of the financial or social status, age and profession, a person exposed to stress may develop health problems that can interfere with work and his quality of life. Problems about stress should not be taken for granted as it may lead to health problems that causes sickness absenteeism. In this scenario it is important to have strategies that helps to deal with such problem. Nowadays technology has been used in different areas not only to help our day to day transactions easy but also provide assistance in coping with psychological problems like stress thru computer games, mainly those based upon Virtual and Augmented Reality (VR/AR) techniques because it offers players some experiences like: relaxation, sense of control, challenges, learning opportunities and immersion and so these characteristics can contribute to the control process of stress. In this paper, it aimed to describe a design intervention though virtual reality game controlled by breathing and simple body movements to relieve stress.
Comparison Non-Parametric Machine Learning Algorithms for Prediction of Employee Talent

Classification of ordinal data is part of categorical data. Ordinal data consists of features with values based on order or ranking. The use of machine learning methods in Human Resources Management is intended to support decision-making based on objective data analysis, and not on subjective aspects. The purpose of this study is to analyze the relationship between features, and whether the features used as objective factors can classify, and predict certain talented employees or not. This study uses a public dataset provided by IBM analytics. Analysis of the dataset using statistical tests, and confirmatory factor analysis validity tests, intended to determine the relationship or correlation between features in formulating hypothesis testing before building a model by using a comparison of four algorithms, namely Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Artificial Neural Networks. The test results are expressed in the Confusion Matrix, and report classification of each model. The best evaluation is produced by the SVM algorithm with the same Accuracy, Precision, and Recall values, which are 94.00%, Sensitivity 93.28%, False Positive rate 4.62%, False Negative rate 6.72%, and AUC-ROC curve value 0.97 with an excellent category in performing classification of the employee talent prediction model.
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge.

Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability to support complex prediction problems using insurance claims data, such as readmission at 30 days, mainly due to data sparsity issue. Consequently, classical machine learning methods, especially those that embed domain knowledge in handcrafted features, are often on par with, and sometimes outperform, deep learning approaches. In this paper, we illustrate how the potential of deep learning can be achieved by blending domain knowledge within deep learning architectures to predict adverse events at hospital discharge, including readmissions. More specifically, we introduce a learning architecture that fuses a representation of patient data computed by a self-attention based recurrent neural network, with clinically relevant features. We conduct extensive experiments on a large claims dataset and show that the blended method outperforms the standard machine learning approaches.
Communicative tools for organizing a dialogue in network book sand bloginge

The article deals withthe communicative tools for organizing dialoguerelations in network book blogging, which is presented as an active intellectual news platform, a video hosting site that is interesting forits capabilities of educational content and technologies for promoting new products of cinema, theater and book products in the space of virtual discourse. The authors consider the problems of communicative competence of the blogger and the basics of effective communication in the context of blog discourse, present the basic requirements for organizing the news content of the recording, the way of self-presentation and the choice of tools for virtual interaction of the blogger-booktuber with subscribers. When describing the real tools of the virtual dialogue of the blogger-booktuber, the authors refer to the blog discourse of Nikolai Zharinov on the youtubchannele Artifix. On specific examples of video recording, the ways of self-presentation of the blogger, the strategy of his interaction with the target audience, his choice of style and language elements of influence are analyzed. Particular attention is paid to the methods of selection and theme of intellectual content for the programs of this author, the creative features of the individual author's commenting on information. The authors prove that online blogging is a unique communicative environment that allows the blogger to be a creative, self-sufficient competent person, successfully combine the public and intimate in a network dialogue. Each booktuber blogger strives for "effective communication", which is understood as such a dialogue interaction that allows the speaker to achieve the goal and solve a set of communicative tasks: socio-cultural education, successful self-presentation, competent promotion of an intellectual cognitive product-novelties of the book market.
The Organized Crime in Western Balkans in the Age of Globalization- Trafficking in Narcotics and Money Laundry

This research addresses the activity of transnational organized crime in the Westerns Balkans region, particularly the activity of trafficking in narcotics and money laundry. This problematic phenomenon is treated on the aspect of newly created circumstances in the age of globalization, where the entire world is facing the transformation in the political and economic aspect - developments that are impacting the field of security as well. In this period, these countries faced institutional fragility, lack of proper capacities in fight against new forms of organized criminality, lack of inter-state cooperation etc. this situation was used as an opportunity by the criminal organizations with their reach in the entire region, creating the so called Balkan Road which is used for trafficking of narcotics towards Western Europe countries. In this research is expressed the amount of narcotics trafficked and illicit profits from this activity, divided by regional countries for the period 2009-2012. This paper argues how transnational organized crime by exploiting the globalization era facilities has adapted to these newly created circumstances and is increasingly exceeding the capabilities of the countries of a traditional organizations and forms of security in responding appropriately. The interstate cooperation aspect and the use of an interactive approach at technical and operational levels is increasingly being considered as a tangible opportunity to deal with this phenomenon. The paper draws conclusions on how the organized crime threats have had influence on security approaches in the Western Balkans as well as pushing the countries towards an inter-state approach in addressing these threats. Security field contemporary literature as well as reports and assessments of various security agencies have been used in drawing research results.