The temporary closure of all educational institutions as an effort to prevent the spread of an outbreak of Covid-19 worldwide has an impact on millions of students, including in Indonesia. Disturbances in the teaching and learning process directly between teacher and students as well as the termination of the assessment of learning have an impact on the psychological of students, causing a decrease in the quality of skills. This burden is the responsibility of all elements of education, in particular the government, in facilitating the continuity of schools for all stakeholders in education to conduct distance education. How should Indonesia plan, prepare for and overcome co-recovery 19, to reduce the loss of education in the future. Keywords: Covid-19, school, Stakeholders, Indonesia Abstrak Penutupan sementara lembaga pendidikan sebagai upaya menahan penyebaran pendemi covid-19 di seluruh dunia berdampak pada jutaan pelajar, tidak kecuali di Indonesia. Gangguan dalam proses belajar langsung antara siswa dan guru dan pembatalan penilaian belajar berdampak pada psikologis anak didik dan menurunnya kualitas keterampilan murid. Beban itu merupakan tanggung jawab semua elemen pendidikan khususnya negara dalam memfasilitasi kelangsungan sekolah bagi semua steakholders pendidikan guna melakukan pembelajaran jarak jauh. Bagaimana mestinya Indonesia merencanakan, mempersiapkan, dan mengatasi pemulihan covid 19, untuk menekan kerugian dunia pendidikan di masa mendatang. Kata Kunci; covid-19, sekolah, steakhorders pendidikan, Indonesia

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Dampak COVID-19 Pada Pendidikan Di Indonesia: Sekolah, Keterampilan, Dan Proses Pembelajaran

Article history: Received: October 30, 2019 Received in revised format: November 28 2019 Accepted: December 2, 2019 Available online: December 2, 2019 The article substantiates the essence of the concept of “social capital” as a resource in terms of formed, permanent, established types of highly effective socio-economic interactions. In addition, the main directions of its manifestation and its structural elements are defined. It is stated that for quantitative measurement of social capital at the enterprise level, it is necessary to establish its precise framework, to determine its structure and key features. As an object of accounting, social capital should be regarded as an intangible asset. The expediency of using the Value explorer method for social capital estimation, which requires some necessary steps, has been substantiated as follows: the use of clearly established algorithm of actions in the implementation of rational selection for a new product; the use of internal and external innovations in the process of generating new ideas and production of innovations within the framework of the product realization strategy; identify areas of key competitive advantages related to social capital; determining the role of the sphere of competitive advantages in production and sales of products; gross profit distribution by key competitive advantages; calculation of the potential of the competitive advantages sphere; assessment of the durability and sustainability of the competitive advantages of the enterprise and calculation of the present value of all elements of social capital of the enterprise. Five main levels of social intangibles are identified and grounded. The study allows to form an appropriate matrix to analyze the effectiveness of their use in the enterprise. © 2020 by the authors; licensee Growing Science, Canada

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Social capital measurement based on “The value explorer” method

Deep learning models are tools for data analysis suitable for approximating (non-linear) relationships among variables for the best prediction of an outcome. While these models can be used to answer many important questions, their utility is still harshly criticized, being extremely challenging to identify which data descriptors are the most adequate to represent a given specific phenomenon of interest. With a recent experience in the development of a deep learning model designed to detect failures in mechanical water meter devices, we have learnt that a sensible deterioration of the prediction accuracy can occur if one tries to train a deep learning model by adding specific device descriptors, based on categorical data. This can happen because of an excessive increase in the dimensions of the data, with a correspondent loss of statistical significance. After several unsuccessful experiments conducted with alternative methodologies that either permit to reduce the data space dimensionality or employ more traditional machine learning algorithms, we changed the training strategy, reconsidering that categorical data, in the light of a Pareto analysis. In essence, we used those categorical descriptors, not as an input on which to train our deep learning model, but as a tool to give a new shape to the dataset, based on the Pareto rule. With this data adjustment, we trained a more performative deep learning model able to detect defective water meter devices with a prediction accuracy in the range 87–90%, even in the presence of categorical descriptors.

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An alternative approach to dimension reduction for pareto distributed data: a case study

This paper provides a critical review of the burgeoning literature on social capital. While our focus is primarily on social capital's place in economics, we do consider its broader social science context. In recent years, social capital literature has produced many insights, however, a number of conceptual and empirical problems remain. In this setting, we trace a panorama of ideas of the principal theorists of social capital for then focusing us on the numerous critics whose it is the subject. Finally we provide recommendations for a prudent use of the concept.

What is Really Social Capital? A Critical Review

The effect of Confucian culture on household risky asset holdings: Using categorical principal component analysis

Social capital is a promising concept, widely used by social science researchers in analysing factors that contribute to the persistence of various economic issues Unfortunately, the search for the best way to define, measure and classify the appropriate components that constitute this intangible form of capital is far from complete Generally, data on social capital are qualitative in nature (mostly of the nominal and ordinal types) and encompass a large number of variables This challenges the researcher to find the best way to reduce these data to a small number of composites to be used as a proxy of measurement in further analysis Although principal component analysis (PCA) is considered an appropriate method and has been widely adopted in past studies, the requirement that data must be at the numeric measurement level, as well as the assumptions of linear relationships between variables, might hinder the use of PCA in working with social capital data Categorical principal component analysis (CATPCA) is a more flexible alternative, suitable for variables of mixed measurement levels (nominal, ordinal, and numeric) that may not be linearly related to each other Based on theory and past studies, questionnaires have been constructed and fieldwork has been carried out to gather data on social capital in Malaysia Later, using CATPCA, 42 potential variables were identified to represent components of social capital Final results indicate that after withdrawing 9 variables with bad fits, CATPCA has categorized the balance of 33 variables into four dimensions of social capital These dimensions can be described by 5 principal components, which have been identified as influence of spirituality and culture, benefits from interaction with friend, trusted person during financial difficulties, benefits from financial aid receive and benefits from involvement in association The first component represents culture/spirituality, the new dimension created by this study to address social capital from the perspective of a developing country The second, third, fourth and fifth components are in line with the consensus reached by scholars and advocates regarding the elements or components of social capital The second and fifth actually fall under the rubrics of the social relation/networks dimension while the third and fourth under trust and norms

Identifying the Components of Social Capital by Categorical Principal Component Analysis (CATPCA)

This study examines the impact of human capital on productivity of labor in Indonesia, using the ARDL analysis data method. This study uses the variable of human capital with a proxy level of labor education, the health status of labor and labor productivity. The data used are sourced from World Bank publications in the period of 1981-2014. The outputs of the analysis with the method of ARDL show that the short-run analyses of primary, secondary, tertiary education variables and health variables have a significant positive impact on labor productivity. In contrast, the long-run analysis including only primary and secondary educations showed a significant positive influence on labor productivity while tertiary education variables have a significant negative effect. The labors’ health variable has a positive but not significant effect. This shows that the quality of human capital in Indonesia is still a problem for labor productivity.

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Impact of Human Resource Investment on Labor Productivity in Indonesia

The extensive use of digital technology and information communication and technology (ICT) under Industry 4.0 will replace many jobs with robotic machines and equipment. Therefore, Industry 4.0 is expected to impact the employment structure and demand for labour, particularly low-skilled workers. This group is considered to be the most vulnerable to losing their jobs. Based on the questionnaire data obtained from fieldwork in Selangor, a total of 377 respondents of low-skilled workers were analysed. This study examines the ICT knowledge and skills of low-skilled workers towards Industry 4.0. The aspects studied are economic, labour market, technological and social. Using the Structured Equation Model (SEM), this study found that all aspects (economics, labour market and technology) are positive and significant in influencing the level of ICT knowledge and skills of employees. On the other hand, the social aspects involving the use of social media and knowledge of cybercrime were found to be insignificant. In conclusion, this study suggests that even though employees realise that ICT knowledge and skills, support from employers, government, colleagues, and others are required to improve their skills, especially to face the challenges in Industry 4.0.

The Influence of the ICT Knowledge and Skills of Low-Skilled Workers on Industry 4.0: A Study on Malaysia’s Service Sector / Pengaruh Pengetahuan dan Kemahiran ICT Pekerja Berkemahiran Rendah terhadap Industri 4.0: Kajian Sektor Perkhidmatan Malaysia

Nasir Mohd Saukani

Papers

Identifying the Components of Social Capital by Categorical Principal Component Analysis (CATPCA)

Impact of Human Resource Investment on Labor Productivity in Indonesia

The Influence of the ICT Knowledge and Skills of Low-Skilled Workers on Industry 4.0: A Study on Malaysia’s Service Sector / Pengaruh Pengetahuan dan Kemahiran ICT Pekerja Berkemahiran Rendah terhadap Industri 4.0: Kajian Sektor Perkhidmatan Malaysia