scholarly journals Analisis Faktor-Faktor yang Mempengaruhi Permohonan Kredit Modal Kerja Usaha Kecil dan Menengah (Studi Kasus Pada PT. Bank X Capem Y Kota Banda Aceh)

2016 ◽  
Vol 1 (1) ◽  
pp. 571-585
Author(s):  
Rizka Oky Pryanka ◽  
Widyawati Widyawati ◽  
Safrida Safrida
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Financial Institutions ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Working Capital ◽  
Negative Effect ◽  
Loan Interest Rates ◽  
Banda Aceh

Abstrak - Kredit merupakan salah satu bagian pembentukan modal yang dilakukan oleh lembaga keuangan. PT. Bank X Kota Banda Aceh telah merealisasikan permohonan Kredit Modal Kerja kepada pengusaha Usaha Kecil dan menengah. Penelitian ini bertujuan untuk mengetahui faktor-faktor suku bunga, pendapatan dan agunan dalam mempengaruhi permohonan kredit Modal Kerja Usaha Kecil dan Menengah pada PT. Bank X. Metode yang digunakan dalam penelitian ini adalah metode studi kasus. Pemilihan sampel sebanyak 20 orang dan diambil keseluruhan dari jumlah populasi dengan menggunakan teknik purposive sampling. Metode pengolahan dan analisis data menggunakan model regresi linear berganda. Hasil penelitian menunjukkan bahwa variabel  suku bunga (X1) berpengaruh negatif dan signifikan terhadap permohonan kredit modal kerja. Variabel pendapatan (X2) tidak berpengaruh secara signifikan terhadap permohonan kredit modal kerja. Sedangkan variabel agunan (X3) berpengaruh positif dan signifikan terhadap permohonan kredit modal kerja.Kata Kunci : Kredit, Kredit Modal Kerja, Suku Bunga, Pendapatan, dan AgunanAbstract - Credit is one of capital formation carried out by financial institutions. PT. Bank X Banda Aceh has provided working capital loan to small and medium entrepreneurs (SMEs). This study aims to determine the influence of interest rates, income and collateral in affecting working capital loan application in PT. Bank X. 20 customers of PT. Bank X who received working capital loan were purposefully selected (purposive technique sampling). Data were analyzed using multiple linear regression model. The result showed that interest rate (X1) has a significant negative effect on the working capital credit application. Variabel income (X2) do not significantly affect the working capital credit application. While the variabel collateral (X3) has positive and significant impact on working capital credit application.Keywords : Loans, Working Capital Loan, Interest Rates, Revenue, and Collateral.

scholarly journals Pengaruh Variabel Makro Ekonomi Terhadap Indeks Jakarta Islamic Index (JII) Periode Januari 2010 Hingga November 2015

2017 ◽  
Vol 3 (1) ◽  
pp. 70
Author(s):  
Ayu Tri Utami ◽  
Leo Herlambang
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Multiple Linear Regression ◽  
Independent Variables ◽  
Negative Effect ◽  
Study Population ◽  
Positive Effect ◽  
Science Governance

The study aimed to know whether there were influences of inflation, interest rate, and exchange rate on the Jakarta Islamic Index (JII). The study population were factors which influenced the Jakarta Islamic Index. The sample were those factors in the period of January 2010- November 2015. Multiple linear regression was used for the analysis. The analysis showed that inflation had a negative effect on Jakarta Islamic Index (JII) index, interest rates had a positive effect on Jakarta Islamic Index (JII) index, and the exchange rate had a positive effect on Jakarta Islamic Index (JII) index. All independent variables simultaneously had an effect on the Jakarta Islamic Index (JII) index. This study was expected to add insightsto science, governance and other fields.

scholarly journals PENGARUH DPK, NPL, LDR DAN SUKU BUNGA KREDIT TERHADAP PENYALURAN KREDIT UMKM (STUDI PADA BPR DI KOTA SEMARANG TAHUN 2013-2016)

2018 ◽  
Vol 10 (2) ◽  
pp. 42-57
Author(s):  
Farida Qusnul Khotimah ◽  
Suci Atiningsih
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Financial Services ◽  
Sampling Method ◽  
Secondary Data ◽  
Third Party ◽  
Research Use ◽  
Analysis Tool ◽  
Loan Interest Rates

The study aims to determine the effect of third-party Funds (DPK)), Non Performing Loan (NPL), Loan to Deposit Ratio (LDR), and Loan Interest Rates to UMKM lending. This research is quantitative by using secondary data. The population in this study ias all Rural Banks (BPR) in Semarang City registered in the Financial Services Authority (OJK) for the period of 2013-2016. By using purposive sampling method, obtained a sample of 64 banks. This research use analysis tool that is doubled linear regression. The variables in this study consist of four independent variabels which include third-party Funds (DPK), Non Performing Loan (NPL), Loan to Deposit Ratio (LDR), Loan Interest Rate and one dependent variable UMKM credit distribution. Based on the results found that the DPK has a positive and significant impact on the credit distribution of UMKM, the NPL has no negative and significant impact on the lending of UMKM, the LDR has a positive and significant impact on the credit distribution of UMKM, and the Interest Rates has no negative and significant impact on the lending of UMKM.

scholarly journals ANALISIS PENGARUH JUMLAH UANG BEREDAR (M1), SUKU BUNGA SBI, NILAI TUKAR SUKU BUNGA DEPOSITO TERHADAP TINGKAT INFLASI

Media Ekonomi ◽  
2017 ◽  
Vol 19 (3) ◽  
pp. 43
Author(s):  
Fadli Ferdiansyah
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Money Supply ◽  
Significant Positive Effect ◽  
Significant Negative Effect ◽  
Negative Effect ◽  
Positive Effect

<p>Inflation is one of the effects of a prolonged economic crisis that hit the country. Inflation is a situation where there is an increase in general prices which continuesover the  long term. The purpose of this study was to determine the effect of the money supply, interrst rate, deposit interest rate and exchange rate (Rp/USD) of the inflation in 2006 – 2011.6 The result of this study suges that the suppy of money have no significant positive effect on inflation. SBI rate have positive and significant effect on inflation. Deposit have rate and no significant negative effect on inflation. Exchange Rate have no significant negative effect on inflation.</p><p>Keywords : Money Supply, Interest Rates, Deposit Interest Rates, Exchange Rate    (IDR /USD), Multiple  Linear Regression, Inflation</p>

scholarly journals Determinan Kinerja Anggaran pada Organisasi Perangkat Daerah

Akuntabilitas ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 109-124
Author(s):  
Syukriy Abdullah ◽  
Afrah Junita
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Financial Management ◽  
Multiple Linear Regression Model ◽  
Fiscal Year ◽  
Government Organizations ◽  
Negative Effect ◽  
Work Units

Budget performance is the basis for evaluating and assessing the quality of financial management and budgeting in government organizations, especially in local government’s agencies. The purpose of this study is to examine the effect of budget size (TB), budget change (PB), and previous year budget variance (VS) on budget performance (KA) at agencies of Gayo Lues Regency. The sample in this study amounted to 44 work units of regional apparatus (SKPD) for fiscal year 2016-2017. Data analysis using multiple linear regression model. The results show that PB has no effect on KA, while TB and VS have negative effect on KA

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI INFLASI DI INDONESIA

2016 ◽  
Vol 5 (2) ◽  
pp. 137
Author(s):  
Chairannisa Arjunita
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Money Supply ◽  
Inflation Targeting ◽  
Time Series Data ◽  
Multiple Linear Regression Model ◽  
Series Data

This study aims to  analyze the effect of interest rate, money supply,exchange rate and inflation targeting framework policy on inflation in Indonesia.The type of this research are descriptive and associative using time series data fromthe first quarter of 1997 until the fourth quarter of 2015 with documentation datacollected technique. Data were analyzed with multiple linear regression model, theprerequisite test (multicolinearity, autocorrelation and heteroscedasticity), t test, andF test. The result shows that (1) Interest Rates has positive and significant effect oninflation in Indonesia. (2) Money Supply has positive and not significant effect oninflation in Indonesia. (3) Exchange rate has negative and not significant effect oninflation in Indonesia.  (4) Inflation Targeting Framewrok Policy has positive andsignificant effect on inflation in Indonesia.

scholarly journals ANALISIS PENGARUH PDB, EKSPOR PERTANIAN, SUKU BUNGA DAN INFLASI TERHADAP INVESTASI PMDN PADA SEKTOR PERTANIAN DI INDONESIA TAHUN 2010-2019

2021 ◽  
Vol 4 (2) ◽  
pp. 40
Author(s):  
Bambang Priadi ◽  
Devi Andriyani
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Interest Rates ◽  
Agricultural Sector ◽  
Time Series Data ◽  
Multiple Linear Regression Model ◽  
Series Data ◽  
Domestic Investment ◽  
Agricultural Exports ◽  
Negative Effect

This study examined and analyzed the effect of GDP, Agricultural Exports, Interest Rates, and Inflation on domestic investment in the agricultural sector in Indonesia. This study used time-series data from 2010 to 2019. The analytical model used was the Multiple Linear Regression Model. The results partially showed that GDP positively and significantly influenced investment in the agricultural sector in Indonesia. Meanwhile, Agricultural Exports, Interest Rates, and Inflation had no significant and negative effect on investment in the agricultural sector in Indonesia. Simultaneously, GDP, Agricultural Exports, Interest Rates, and Inflation positively and significantly influenced investment in the agricultural sector in Indonesia.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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