PENGGUNAAN KAIN TROSO DAN APLIKASI MAKRAME PADA BUSANA READY TO WEAR

Indonesia memiliki kekayaan ragam kain tenun tradisional dari masing-masing daerah. Salah satu daerah yang menghasilkan kain tenun tradisional yaitu kabupaten Jepara, Jawa Tengah. Kain tenun yang diproduksi di desa Troso Kabupaten Jepara disebut dengan kain tenun Troso. Dalam upaya memperkenalkan kain tenun Troso, maka disusun rencana penelitian ini yang bertujuan untuk memberikan alternatif baru penggunaan kain tradisional tenun ikat troso dan aplikasi teknik ikat mengikat atau lebih dikenal dengan makrame yang pembuatannya dilakukan secara manual sebagai nilai tambah dalam pembuatan busana. Hasil dari penelitian ini berupa busana ready to wear yang terinspirasi dari Trend Forecasting Fashion tahun 2019-2020 Singularity yang diterbitkan oleh Bekraf dengan tema Svarga. Metodologi yang digunakan adalah studi literatur dan studi lapangan dalam proses eksperimen pembuatan produk. Kain tenun Troso dan kain tambahan yang akan digunakan memerlukan sifat yang menghasilkan kenyamanan serta kekuatan yang baik bagi penggunanya. Hal tersebut mendasari perlunya dilakukan pengujian untuk kain tenun troso dan kain pendukung. Selain itu aplikasi makrame yang diterapkan akan diproduksi dengan teknik pencelupan zat warna untuk memperoleh hasil gradasi warna. Produk tekstil atau fashion tersebut kemudian dinilai secara ekonomi di masyarakat dengan menggunakan metode kuantitatif berupa kuisioner.

SUSTAINABLE FASHION PADA BUSANA READY-TO-WEAR DENGAN TEKNIK ECOPRINT

AbstrakDewasa ini, produk fashion berkonsep ramah lingkungan, seperti eco fashion, menjadi salah satu daya tarik tersendiri bagi masyarakat. Filosofi eco fashion berkesinambungan dengan konsep produk yang berkelanjutan (sustainable product). Salah satu metoda pewarnaan yang dapat digunakan untuk memenuhi konsep eco fashion dan sustainable product adalah teknik eco printing. Pada penelitian ini digunakan teknik ecoprinting metoda pukul pada kain kapas yang telah dicelup dengan warna dasar menggunakan pewarna alami, kulit kayu tegeran. Proses pre-mordanting menggunakan zat kapur dan tawas dilakukan sebagai upaya untuk mencegah terjadinya kelunturan warna akibat penggunaan zat warna alam. Kain yang telah diproses ecoprinting kemudian di produksi menjadi 2 buah busana Ready-to-wear dengan tema neo medieval subtema dystopian fortress pada trend forecasting singularity 2019-2020. Survey kelayakan harga dilakukan berdasarkan uji kuantitatif sehingga didapatkan data bahwa sebanyak 55% - 80% responden menyatakan tertarik dengan model produk yang ditawarkan, 75 -77% responden merasa bahwa produk pertama dan kedua yang ditawarkan layak dihargai Rp 1.000.000 – Rp 1.500.000.

BORROWING ALTERNATIVES FOR HOUSEHOLDS IN LITHUANIA: CURRENT SITUATION, TRENDS AND CHALLENGES

Purpose – to analyse the main borrowing alternatives available to Lithuanian households and the credit market as a whole, focusing on its peer-to-peer (P2P) segment, the forecast of its growth, and possible challenges. Research methodology – the research methods applied were scientific literature analysis, statistical data analysis, comparative analysis, correlation-regression analysis, linear trend forecasting method. Findings – the prevailing borrowing alternative for Lithuanian households still remain bank credits. Besides, borrowing from P2P market is becoming more and more popular. Although the macroeconomic environment for all the credit market segments is the same, the P2P segment is developing significantly faster. If this trend remains unchanged, the whole credit market is likely to face challenges, such as the growth of overdue loans, insolvent customers, the rising share of non-performing-loans (NPL), etc., that may affect its overall stability. Research limitations – the empirical study relies on the country’s macroeconomic indicators that influence household borrowing. Such factors as borrower’s age, income level, marital status and others were not taken into account in this study. The forecast of the P2P segment growth of the consumer credit market and comparison with its banking segment is based on the analysis of 4 years of real monthly statistics for both segments. Practical implications – the performed analysis and its results can be useful for the future research within the household borrowing trends, especially in Peer-to-Peer platforms, and specifically for the Central Bank, the Ministry of Finance and other institutions that regulate the credit market, as it provides information on modern borrowing trends and the challenges it might bring. Also, for P2P platforms themselves, planning and further developing their activities and adjusting lending conditions with the aim to attract higher-quality customers. Originality/Value – household borrowing, the credit market and the P2P platforms are widely analysed by both academics and financial institutions, such as central banks. However, it is mainly limited to the analysis of statistical data and does not pay attention to possible market development issues. This study focuses on the analysis of the growth trends of the P2P market and the potential challenges that may arise thereafter.

Developing the approaches to monitoring of risks of failure to achieve the national development objective ‘Digital Transformation’ in the strategic planning system of the Russian Federation

Subject. The article investigates identification of risks that prevent the achievement of national development objectives and implementation of national projects, as the methodological approach to their assessment is still in the process of formation. Objectives. The purpose is to develop conceptual and methodological approaches to monitoring of risks of missing the national development targets in the strategic planning system of the Russian Federation. Methods. The research methodology rests on the theoretical basis of indicative analysis. In its logic, the risks of failure to achieve the targets of national development of the Russian Federation can be tracked through monitoring the achieved and predicted values of target indicators within their valid values. Results. The paper specifies factors that have a significant impact on the achievement of the national target ‘Digital Transformation’, defines quantifiable values of the target indicator ‘The Share of Households with Broadband Internet Access’ and limits of its permissible values. We presented a forecast, showing the most likely behavior of the analyzed indicator in the strategic perspective until 2030, revealed that the target value of the indicator, defined at the level of 97%, significantly exceeds the results of trend forecasting, which is indicative of a high risk of non-achievement of target by the key date. Conclusions. Determination of risks of failure to achieve the digital transformation of national development objectives should become an essential stage in the procedure for monitoring the implementation of strategic planning documents in Russia.

A stock trading algorithm based on trend forecasting and time series classification

Algorithm trading relies on the automatic identification of buying and selling points of a given asset to maximize profit. In this paper, we propose the Trend Classification Trading Algorithm (TCTA) which is based on time series classification and trend forecasting to perform trade. TCTA first employs the K-means to cluster 5-days closing price segments and label them according to its trend. A deep learning classification model is then trained with these label sequences to estimate the next trend. Trading points are given by the alternation on trend estimates. Results considering 20 shares from Ibovespa show TCTA present higher profit than buy-and-hold and trading schemes based on Moving Average Converge Divergence (MACD) or Bollinger bands.

Algorithm and Results of Short-Term Forecast of Changes in the COVID-19 Spread Coefcient in the Constituent Entities of the Russian Federation

There was a decrease in the number of COVID-19 cases across many entities of the Russian Federation towards the end of summer season-2020. However, the disease remains a relevant threat to the public health and economy and the possibility of a second epidemic wave is not excluded. Rate of infection transmission (Rt) is one of the most important indicators to justify the transition to next stage of removing/introducing restrictive measures on COVID-19.Objective of the work was to describe the algorithm of analysis and short-term forecast of coronavirus spread rate, to assess correspondence between theoretically expected and actual values of this indicator.Materials and methods. Procedure for making a short-term extrapolation forecast of Rt in 10 RF constituent entities, depending on the presence or absence of indicator trends with calculation of a 95 % confdence interval of possible changes in its value is provided.Results and discussion. It is proposed to carry out Rt forecast based on averaged values over a week, combining regression analysis and expert assessment of time series dynamics nature for prompt transition from trend forecasting to extrapolation of stationary observation sequences and vice versa. It has been demonstrated that predicted Rt values are not statistically different from actual values. When making managerial decisions on COVID-19 prevention, special attention should be paid to cases when actual value of Rt exceeds the upper limit of confdence interval. Six (20.0 %) such cases were detected in surveyed entities on calendar weeks 33–35. Three of them were registered in Trans-Baikal Territory, where upward trend was reported during that period of time. The cause of this phenomenon should be analyzed. The put forward algorithm of analysis and forecasting of the Rt value changes, which was tested in 10 entities of Russia, provides a reliable basis for making management decisions on removing/introducing restrictive measures for COVID-19 prevention.

Medium—And Long-Term Load Forecasting Method for Group Objects Based on the Image Representation Learning

Medium-and long-term load forecasting in the distribution network has important guiding significance for overload warning of distribution transformer, transformation of distribution network and other scenarios. However, there are many constraints in the forecasting process. For example, there are many predict objects, the data sample size of a single predict object is small, and the long term load trend is not obvious. The forecasting method based on neural network is difficult to model due to lack of data, and the forecasting method based on time sequence law commonly used in engineering is highly subjective, which is not effective. Aiming at the above problems, this paper takes distribution transformer as the research object and proposes a medium-and long-term load forecasting method for group objects based on Image Representation Learning (IRL). Firstly, the data of distribution transformer is preprocessed in order to restore the load variation in natural state. And then, the load forecasting process is decoupled into two parts: the load trend forecasting of the next year and numerical forecasting of the load change rate. Secondly, the load images covering annual and inter-annual data change information are constructed. Meanwhile, an Image Representation Learning forecasting model based on convolutional neural network, which will use to predict the load development trend, is obtained by using load images for training; And according to the data shape, the group classification of the data in different periods are carried out to train the corresponding group objects forecasting model of each group. Based on the forecasting data and the load trend forecasting result, the group forecasting model corresponding to the forecasting data can be selected to realize the numerical forecasting of load change rate. Due to the large number of predict objects, this paper introduces the evaluation index of group forecasting to measure the forecasting effect of different methods. Finally, the experimental results show that, compared with the existing distribution transformer forecasting methods, the method proposed in this paper has a better overall forecasting effect, and provides a new idea and solution for the medium-and long-term intelligent load forecasting of the distribution network.

CARDINAL WTRL: TECHNOLOGY MATURITY, SCHEDULE SLIPPAGE AND TREND FORECASTING.

Maturity assessments of technology is a crucial process to identify and acquire compatible technologies for a system’s development. However, being a complex and highly subjective process, the Government Accountability Office (GAO) has reported cost overruns and schedule slippages through the years. This study provides a unique Weighted Technology Readiness Level (WTRL) framework which utilizes cardinal factors to ascertain the maturity, schedule and trend of NASA’s 7 Technologies based on their maturity time. The framework utilizes MCDM methods to determine the cardinal complexity of each TRL. It allows the assimilation of other cardinal factors using a simple, open structure to track the overall technology maturity and readiness. Furthermore, this study highlights the importance of tailored TRL frameworks to determine the accurate cardinal coefficient of the said technology and the inferences derived otherwise. It eliminates several limitations of previous frameworks and compares against their performance using a verified statistical representation of processed data.