Keefektifan Model Problem Based Learning (Pbl), Model Generative Learning (Gl) Dan Integrasinya Terhadap Hasil Belajar Ditinjau Dari Kemampuan Menganalisis Dan Kreativitas Siswa

Tujuan penelitian ini untuk mengetahui: pengaruh penggunaan model PBL, model GL dan model Integrasi PBL-GL, kemampuan menganalisis, kreativitas, dan interaksinya terhadap hasil belajar siswa. Penelitian ini merupakan penelitian eksperimen yang dilakukan pada bulan April hingga Juni 2014. Populasi penelitian adalah seluruh siswa kelas XI Analisis Kimia SMK Negeri 3 Madiun yang berjumlah 96 siswa terbagi dalam 3 kelompok penelitian terdiri dari tiga kelas yaitu XI AK2, XI AK3 dan XI AK4 yang ditentukan teknik cluster random sampling. Kelas eksperimen I ( 31 siswa), II ( 33 siswa ) dan III ( 32 siswa ) masing-masing diberi perlakuan dengan model GL, PBL dan integrasi PBL-GL. Hasil belajar aspek kognitif diambil dengan teknik tes, sedangkan kemampuan menganalisis, kreativitas , aspek afektif dan psikomotor dilakukan dengan metode angket. Data dianalisis menggunakan uji ANAVA tiga jalan, desain faktorial 3x2x2 dengan taraf signifikansi 5%. Berdasarkan analisis data, dapat disimpulkan bahwa; 1) ada pengaruh model (PBL, GL, dan Integrasi PBL-GL) terhadap hasil belajar siswa; 2) tidak ada pengaruh kemampuan menganalisis terhadap hasil belajar siswa; 3) tidak ada pengaruh antara kreativitas terhadap hasil belajar siswa; 4) tidak ada interaksi antara model (PBL, GL dan Integrasi PBL-GL) dengan kemampuan menganalisis terhadap hasil belajar siswa; 5) tidak ada interaksi model (PBL, GL, dan Integrasi PBL-GL) dengan kreativitas terhadap hasil belajar siswa; 6) tidak ada interaksi antara kemampuan menganalisis dan kreativitas terhadap hasil belajar siswa; 7) tidak ada interaksi antara model (PBL, GL dan Integrasi PBL-GL), kemampuan menganalisis dan kreativitas terhadap hasil belajar siswa

Inflation Rate Modelling in Indonesia

The purposes of this research were to analyse: (i) Modelling the inflation rate in Indonesia with parametric regression. (ii) Modelling the inflation rate in Indonesia using non-parametric regression spline multivariable (iii) Determining the best model the inflation rate in Indonesia (iv) Explaining the relationship inflation model parametric and non-parametric regression spline multivariable. Based on the analysis using the two methods mentioned the coefficient of determination (R2) in parametric regression of 65.1% while non-parametric amounted to 99.39%. To begin with, the factor of money supply or money stock, crude oil prices and the rupiah exchange rate against the dollar is significant on the rate of inflation. The stability of inflation is essential to support sustainable economic development and improve people's welfare. In conclusion, unstable inflation will complicate business planning business activities, both in production and investment activities as well as in the pricing of goods and services produced.DOI: 10.15408/etk.v15i2.326

Tourism income and economic growth in Greece: Empirical evidence from their cyclical components

This paper examines the relationship between the cyclical components of Greek GDP and international tourism income for Greece for the period 1976–2004. Using spectral analysis the authors find that cyclical fluctuations of GDP have a length of about nine years and that international tourism income has a cycle of about seven years. The volatility of tourism income is more than eight times the volatility of the Greek GDP cycle. VAR analysis shows that the cyclical component of tourism income is significantly influencing the cyclical component of GDP in Greece. The findings support the tourism-led economic growth hypothesis and are of particular interest and importance to policy makers, financial analysts and investors dealing with the Greek tourism industry

Optimization of Fuzzy Support Vector Machine (FSVM) Performance by Distance-Based Similarity Measure Classification

This research aims to determine the maximum or minimum value of a Fuzzy Support Vector Machine (FSVM) Algorithm using the optimization function. As opposed to FSVM, which is less effective on large and complex data because of its sensitivity to outliers and noise, SVM is considered an effective method of data classification. One of the techniques used to overcome this inefficiency is fuzzy logic, with its ability to select the right membership function, which significantly affects the effectiveness of the FSVM algorithm performance. This research was carried out using the Gaussian membership function and the Distance-Based Similarity Measurement consisting of the Euclidean, Manhattan, Chebyshev, and Minkowsky distance methods. Subsequently, the optimization of the FSVM classification process was determined using four proposed FSVM models and normal SVM as comparison references. The results showed that the method tends to eliminate the impact of noise and enhance classification accuracy effectively. FSVM provides the best and highest accuracy value of 94% at a penalty parameter value of 1000 using the Chebyshev distance matrix. Furthermore, the model proposed will be compared to the performance evaluation model in preliminary studies. The result further showed that using FSVM with a Chebyshev distance matrix and a Gaussian membership function provides a better performance evaluation value. Doi: 10.28991/HIJ-2021-02-04-02 Full Text: PD

Korespondensi FORECASTING EDUCATED UNEMPLOYED PEOPLE IN INDONESIA USING THE BOOTSTRAP TECHNIQUE

Forecasting is an essential analytical tool used to make future predictions based on preliminary data. However, the use of small sample sizes during analysis provides inaccurate results, known as asymptotic forecasting. Therefore, this study aims to analyze the unemployment rate of educated people in Indonesia using the bias-corrected forecasting bootstrap technique. Data were collected from a total of 30 time series of educated unemployed from 2015 to 2019 using the bias-corrected bootstrap technique and determined using the interval prediction method. The bootstrap replication used is at intervals of 100, 250, 500, and 1000. The results obtained using the R program showed that the bootstrap technique provides consistent forecasting results, better accuracy, and unbiased estimation. Moreover, the results also show that for the next 10 periods, the number of educated unemployed people in Indonesia is projected to decline. The bootstrap coefficient also tends to decrease with an increase in the number of replications, at an average of 0.958. The interval prediction is also known to be smooth, along with a large number of bootstrap replications

Reactivation of low avidity tumor-specific CD8+ T cells associates with immunotherapeutic efficacy of anti-PD-1

Data availability statement: Data are available on reasonable request. RNA sequencing data have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE221590. All algorithms used for RNAseq analysis were publicly available R packages. Additional information required is available from the lead contact on request.Background: CD8+ T cells are a highly diverse population of cells with distinct phenotypic functions that can influence immunotherapy outcomes. Further insights on the roles of CD8+ specificities and TCR avidity of naturally arising tumor-specific T cells, where both high and low avidity T cells recognizing the same peptide-major histocompatibility complex (pMHC) coexist in the same tumor, are crucial for understanding T cell exhaustion and resistance to PD-1 immunotherapy. Methods: CT26 models were treated with anti-PD-1 on days 3, 6 and 9 following subcutaneous tumor implantation generating variable responses during early tumor development. Tetramer staining was performed to determine the frequency and avidity of CD8+ T cells targeting the tumor-specific epitope GSW11 and confirmed with tetramer competition assays. Functional characterization of high and low avidity GSW11-specific CD8+ T cells was conducted using flow cytometry and bulk RNA-seq. In vitro cytotoxicity assays and in vivo adoptive transfer experiments were performed to determine the cytotoxicity of high and low avidity populations. Results: Treatment success with anti-PD-1 was associated with the preferential expansion of low avidity (Tetlo) GSW11-specific CD8+ T cells with Vβ TCR expressing clonotypes. High avidity T cells (Tethi), if present, were only found in progressing PD-1 refractory tumors. Tetlo demonstrated precursor exhausted or progenitor T cell phenotypes marked by higher expression of Tcf-1 and T-bet, and lower expression of the exhaustion markers CD39, PD-1 and Eomes compared with Tethi, whereas Tethi cells were terminally exhausted. Transcriptomics analyses showed pathways related to TCR signaling, cytotoxicity and oxidative phosphorylation were significantly enriched in Tetlo found in both regressing and progressing tumors compared with Tethi, whereas genes related to DNA damage, apoptosis and autophagy were downregulated. In vitro studies showed that Tetlo exhibits higher cytotoxicity than Tethi. Adoptive transfer of Tetlo showed more effective tumor control than Tethi, and curative responses were achieved when Tetlo was combined with two doses of anti-PD-1. Conclusions: Targeting subdominant T cell responses with lower avidity against pMHC affinity neoepitopes showed potential for improving PD-1 immunotherapy. Future interventions may consider expanding low avidity populations via vaccination or adoptive transfer.Worldwide Cancer Research Fund (20-0229) awarded to TE and Cancer Research UK Programme Grant (A28279) awarded to TE and EJ

Energy Security and Economics of Indian Biofuel Strategy in a Global Context

The emergence of biofuel as a renewable energy source offers opportunities for climate change mitigation and greater energy security for many countries. At the same time, biofuel represents the possibility of substitution between energy and food. For developing countries like India, which imports over 75% of its crude oil, fossil fuels pose two risks - global warming pollution and negative economic impacts of oil price hikes. This paper examines India's options for managing energy price risk in three ways: biofuel development, energy efficiency promotion, and food productivity improvements. The overall results suggest that biodiesel shows promise as a transport fuel substitute that can be produced in ways that fully utilize marginal agricultural resources and hence promote rural livelihoods. First-generation bioethanol, by contrast, appears to have a limited ability to offset the impacts of oil price hikes. Combining the biodiesel expansion policy with energy efficiency improvements and food productivity increases proved to be a more effective strategy to enhance both energy and food security, help mitigate climate change, and cushion the economy against oil price shocks