PENGARUH KOMPENSASI, DISIPLIN KERJA, DAN MOTIVASI TERHADAP PRODUKTIVITAS KARYAWAN

Abstrak Tujuan dalam penelitian ini adalah untuk mengetahui pengaruh kompensasi, disiplin kerja, dan motivasi terhadap produktivitas karyawan di Kantor Jasa Penilai Publik (KJPP) Firmansyah & Rekan. Adapun isu – isu pokok yang akan di diungkap dalam penelitian ini adalah seberapa besar produktivitas kerja dari karyawan yang bekerja di KJPP Firmansyah & Rekan. Populasi dan sampel dalam penelitian ini adalah karyawan di Kantor Jasa Penilai Publik (KJPP) Firmansyah & Rekan. Pendekatan penelitian adalah pendekatan kuantitatif. Pengambilan sampel menggunakan teknik non-random sampling, yaitu dengan Teknik Purposive sampling. Sampel yang digunakan berjumlah 35 responden., dengan membagikan angket/ kuesioner dan teknik skala pengukuran menggunakan skala likert. Pengolahan data menggunakan SMARTPLS. Berdasarkan hasil uji hipotesis dari penelitian ini, yaitu adanya pengaruh antara kompensasi, disiplin kerja, dan motivasi terhadap produktivitas karyawan. Abstract The purpose of this study was to determine the effect of compensation, work discipline, and motivation on employee productivity in the Office of Public Appraisal Services (KJPP) Firmansyah & Partners. The main issues revealed in this research are how much work productivity of employees who work at KJPP Firmansyah & Partners. The population and sample in this study were employees at the Office of Public Appraisal Services (KJPP) Firmansyah & Partners. A research approach is a quantitative approach. Sampling used a non-random sampling technique, namely the purposive sampling technique. The sample used is 35 respondents, by distributing questionnaires/questionnaires and measuring scale techniques using a Likert scale. Data processing using SMARTPLS. Based on the results of hypothesis testing from this study, namely the influence of compensation, work discipline, and motivation on employee productivity.

An Evaluation of Supervised Dimensionality Reduction For Large Scale Data

Experimenters today frequently quantify millions or even billions of characteristics (measurements) each sample to address critical biological issues, in the hopes that machine learning tools would be able to make correct data-driven judgments. An efficient analysis requires a low-dimensional representation that preserves the differentiating features in data whose size and complexity are orders of magnitude apart (e.g., if a certain ailment is present in the person's body). While there are several systems that can handle millions of variables and yet have strong empirical and conceptual guarantees, there are few that can be clearly understood. This research presents an evaluation of supervised dimensionality reduction for large scale data. We provide a methodology for expanding Principal Component Analysis (PCA) by including category moment estimations in low-dimensional projections. Linear Optimum Low-Rank (LOLR) projection, the cheapest variant, includes the class-conditional means. We show that LOLR projections and its extensions enhance representations of data for future classifications while retaining computing flexibility and reliability using both experimental and simulated data benchmark. When it comes to accuracy, LOLR prediction outperforms other modular linear dimension reduction methods that require much longer computation times on conventional computers. LOLR uses more than 150 million attributes in brain image processing datasets, and many genome sequencing datasets have more than half a million attributes.

Selected Aspects of Carbon Stock Assessment in Aboveground Biomass

Given the significance of national carbon inventories, the importance of large-scale estimates of carbon stocks is increasing. Accurate biomass estimates are essential for tracking changes in the carbon stock through repeated assessment of carbon stock, widely used for both vegetation and soil, to estimate carbon sequestration. Objectives: The aim of our study was to determine the variability of several aspects of the carbon stock value when the input matrix was (1) expressed either as a vector or as a raster; (2) expressed as in local (1:10,000) or regional (1:100,000) scale data; and (3) rasterized with different pixel sizes of 1, 10, 100, and 1000 m. Method: The look-up table method, where expert carbon content values are attached to the mapped landscape matrix. Results: Different formats of input matrix did not show fundamental differences with exceptions of the biggest raster of size 1000 m for the local level. At the regional level, no differences were notable. Conclusions: The results contribute to the specification of best practices for the evaluation of carbon storage as a mitigation measure, as well as the implementation of national carbon inventories.

Subjective norms as a mediation of the opportunity to donate factors and agreeableness personality that can affect the intention to donate

The purpose of this study was to determine the role of subjective norms as a mediation between the relationship opportunity to donate and agreeableness personality on intention to donate. This research includes a quantitative approach with a survey method distributed to 400 participants of BPJAMSOSTEK East Java Province using a Likert scale. Data analysis using SEM using WarpPLS 6.0 software. The results of this study state that subjective norms can be a link between the opportunity to donate and in accordance with the intention to donate. This can increase the intention to donate for BPJAMSOSTEK participants. The increased intention to donate owned by participants will increase participants who donate.

Artificial Neural Network Models for Large-Scale Data

Artificial intelligence (ARTINT) and information have been famous fields for many years. A reason has been that many different areas have been promoted quickly based on the ARTINT and information, and they have created many significant values for many years. These crucial values have certainly been used more and more for many economies of the countries in the world, other sciences, companies, organizations, etc. Many massive corporations, big organizations, etc. have been established rapidly because these economies have been developed in the strongest way. Unsurprisingly, lots of information and large-scale data sets have been created clearly from these corporations, organizations, etc. This has been the major challenges for many commercial applications, studies, etc. to process and store them successfully. To handle this problem, many algorithms have been proposed for processing these big data sets.

Cumprimento das medidas de precauções-padrão por profissionais de saúde: comparação entre dois hospitais

Objective: To assess compliance with standard precautions by health professionals in two hospitals.Method: This is a descriptive study, with a quantitative approach, conducted in two hospitals in the State of Rio de Janeiro. The sample is composed of health professionals who work in health care. Study developed in the period between February 2019 and February 2020. In order to collect data, the we used: 1- Individual and professional information form; 2- Brazilian Portuguese version of the Compliance with Standard Precautions Scale. Data were analyzed using descriptive statistics and hypothesis tests.Results: The study was attended by 366 (100.0%) health professionals. The overall score of compliance with standard precautions was 13.4 (66.8%), ranging from 4 to 20. As for the average of the scores between the institutions, the professionals from hospital 1 had an average of 12.6 and those from hospital 2 showed a compliance of 13.6.Conclusions: Compliance with standard precautions among health professionals did not happen in its entirety Objetivo: Evaluar el cumplimiento de las precauciones estándar por parte de los profesionales de la salud en dos hospitales.Método: Se trata de un estudio descriptivo, con abordaje cuantitativo, realizado en dos hospitales del Estado de Rio de Janeiro. La muestra está compuesta por profesionales de la salud que trabajan en la asistencia. Estudio desarrollado en el período comprendido entre febrero de 2019 y febrero de 2020. Para la recolección de datos se utilizó lo siguiente: 1- Formulario de información individual y profesional; 2- Versión en portugués brasileño de la Escala de Cumplimiento de Precauciones Estándar. Los datos se analizaron mediante estadística descriptiva y pruebas de hipótesis.Resultados: El estudio incluyó a 366 (100,0%) profesionales de la salud. La puntuación global de cumplimiento de las precauciones estándar fue de 13,4 (66,8%), con un rango de 4 a 20. En cuanto a la media de las puntuaciones entre las instituciones, los profesionales del hospital 1 tuvieron una media de 12,6 y el hospital 2 mostró un cumplimiento de 13,6.Conclusiones: El cumplimiento de las precauciones estándar entre los profesionales de la salud no se produjo en su totalidad. Objetivo: Avaliar o cumprimento às precauções-padrão por profissionais de saúde de dois hospitais. Método: Trata-se de um estudo descritivo, de abordagem quantitativa, realizado em dois hospitais do Estado do Rio de Janeiro. A amostra é composta por profissionais de saúde que atuam na assistência. Estudo desenvolvido no período entre fevereiro de 2019 até fevereiro de 2020. Para a coleta de dados foram utilizados: 1- Formulário de informações individuais e profissionais; 2- Versão para o Português do Brasil da Compliance with Standard Precautions Scale. Os dados foram analisados utilizando estatística descritiva e testes de hipótese. Resultados: Participaram do estudo 366 (100,0%) profissionais de saúde. O escore geral de cumprimento das precauções-padrão foi de 13,4 (66,8%) variando entre 4 e 20. Quanto a média dos escores entre as instituições, os profissionais do hospital 1 obtiveram uma média de 12,6 e o hospital 2 apresentou 13,6 de cumprimento.Conclusões: O cumprimento às precauções-padrão entre profissionais de saúde não aconteceu em sua totalidade.

El miedo al contagio de covid-19, como mediador entre la exposición a las noticias y la salud mental, en población peruana

Introduction: In this COVID-19 pandemic, protective measures against the disease and government-imposed policies should be known. However, the media also report on deaths and health service shortages, but their impact on the mental health of the population is ignored.Objective: To determine whether fear of COVID-19 infection acts as a mediator between exposure to news about the pandemic and mental health in the Peruvian population.Method: Explanatory study with observable variables in which 541 persons selected by non-probabilistic sampling participated. They responded to a sociodemographic file and the following scales: Mental Health Inventory-5 (MHI-5) and Fear of COVID-19 Scale. Data were processed using IBM SPSS Statistics 25 and Macro PROCESS for SPSS programs; linear regression and bootstrapping of 10 000 simulations were used.Results: The number of hours watching and/or listening to covid-19 information is a good predictor of the COVID-19 fear mediator variable (β= ,75; t = 3.77, p<.001**). In turn, this has a predictor effect on mental health (β= -,24; t = -13.57, p<.001**). However, the number of hours of exposure to COVID-19 information had no direct positive effect on mental health (β= -.10; t = -1.184, p=.23).Conclusion: Fear of COVID-19 has a total mediating effect between exposure to pandemic news and mental health in the Peruvian population. Introducción: En esta pandemia por covid-19 se deben conocer las medidas de protección contra la enfermedad y las políticas impuestas por el gobierno; empero, los medios de comunicación también informan sobre las muertes y el desabastecimiento de los servicios de salud, pero se ignora su impacto en la salud mental de la población.Objetivo: Determinar si el miedo al contagio de la covid-19 actúa como mediador entre la exposición a las noticias sobre la pandemia y la salud mental en población peruana.Método: Estudio explicativo con variables observables, donde participaron 541 personas seleccionadas con un muestreo no probabilístico. Respondieron una ficha sociodemográfica y las escalas: Mental Health Inventory-5 (MHI-5) y Fear of Covid-19 Scale. Los datos fueron procesados mediante los programas IBM SPSS Statistics 25 y Macro PROCESS para SPSS; se utilizó la regresión lineal y bootstrapping de 10 000 simulaciones.Resultados: El número de horas viendo y/o escuchando información de la covid-19 es un buen predictor de la variable mediadora de miedo a la covid-19 (β= ,75; t = 3,77, p<,001**); a su vez, esta tiene un efecto predictor sobre la salud mental (β= -,24; t = -13,57, p<,001**); sin embargo, el número de horas de exposición a la información de la covid-19 no tuvo un efecto directo positivo en la salud mental (β= -,10; t = -1,184, p=,23).Conclusión: El miedo a la covid-19 tiene un efecto mediador total entre la exposición a las noticias sobre la pandemia y la salud mental en la población peruana.

A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues

The past decade has been characterized by the growing volumes of data due to the widespread use of the Internet of Things (IoT) applications, which introduced many challenges for efficient data storage and management. Thus, the efficient indexing and searching of large data collections is a very topical and urgent issue. Such solutions can provide users with valuable information about IoT data. However, efficient retrieval and management of such information in terms of index size and search time require optimization of indexing schemes which is rather difficult to implement. The purpose of this paper is to examine and review existing indexing techniques for large-scale data. A taxonomy of indexing techniques is proposed to enable researchers to understand and select the techniques that will serve as a basis for designing a new indexing scheme. The real-world applications of the existing indexing techniques in different areas, such as health, business, scientific experiments, and social networks, are presented. Open problems and research challenges, e.g., privacy and large-scale data mining, are also discussed.

SHAT: A Novel Asynchronous Training Algorithm That Provides Fast Model Convergence in Distributed Deep Learning

The recent unprecedented success of deep learning (DL) in various fields is underlied by its use of large-scale data and models. Training a large-scale deep neural network (DNN) model with large-scale data, however, is time-consuming. To speed up the training of massive DNN models, data-parallel distributed training based on the parameter server (PS) has been widely applied. In general, a synchronous PS-based training suffers from the synchronization overhead, especially in heterogeneous environments. To reduce the synchronization overhead, asynchronous PS-based training employs the asynchronous communication between PS and workers so that PS processes the request of each worker independently without waiting. Despite the performance improvement of asynchronous training, however, it inevitably incurs the difference among the local models of workers, where such a difference among workers may cause slower model convergence. Fro addressing this problem, in this work, we propose a novel asynchronous PS-based training algorithm, SHAT that considers (1) the scale of distributed training and (2) the heterogeneity among workers for successfully reducing the difference among the local models of workers. The extensive empirical evaluation demonstrates that (1) the model trained by SHAT converges to the higher accuracy up to 5.22% than state-of-the-art algorithms, and (2) the model convergence of SHAT is robust under various heterogeneous environments.