Spatio-Temporal Analysis of Rupiah Loans Provided by Commercial Banks and Rural Banks

Susila, Muktar Redy

doi:10.30598/barekengvol16iss3pp1003-1012

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Inner model terdiri dari analisis koefisien determinasi yang dapat menjelaskan hubungan antar variabel dan prediction relevance. Untuk mengetahui pengaruh dari tujuan penelitian ini maka digunakan uji dari output boostrapping Susila, 2022). Variabel dikatakan berpengaruh signifikan apabila nilai p-value kurang dari 0,05 (Susila & Pradhani, 2022).…”

Section: Metode Penelitianunclassified

Customer Relationship Management melalui Orientasi Pasar dan Inovasi Organisasi untuk Meningkatkan Kinerja Pemasaran Bisnis Online

Rithmaya¹,

Kholid²,

Tianto³

2023

JSEB

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The study aims to examine the effect of customer relationship management on marketing performance mediated by market orientation and organizational innovation. The data was obtained through a questionnaire distributed to 50 MSME respondents in Surabaya. Determination of respondents using the quota sampling method. The variables analyzed are customer relationship management, market orientation, organizational innovation, and marketing performance. The results of the study state that partially, customer relationship management has a significant and positive effect on market orientation, marketing performance, and work innovation; and that market orientation and organizational innovation have a significant and positive effect on marketing performance. Another result obtained is that organizational innovation is proven to be able to mediate the effect of customer relationship management on marketing performance. Market orientation is also proven to be able to mediate customer relationship management on marketing performance.

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Section: Metode Penelitianunclassified

Customer Relationship Management melalui Orientasi Pasar dan Inovasi Organisasi untuk Meningkatkan Kinerja Pemasaran Bisnis Online

Rithmaya¹,

Kholid²,

Tianto³

2023

JSEB

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“…The best modelling is done by looking at the smallest RMSE value of each model. Here is the RMSE formula [1], [9], [20], [24]:…”

Section: Gstar-sur Best Model Determinationmentioning

confidence: 99%

Forecasting the Consumer Price Index With Generalized Space-Time Autoregressive Seemingly Unrelated Regression (Gstar-Sur): Compromise Region and Time

Arum

Indriani

Haris

2023

BAREKENG: J. Math. & App.

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Economic success will provide benefits for improving people’s welfare. An important indicator to determine economic success can be seen through inflation by calculating the Consumer Price Index (CPI). CPI is a time series data that is influenced by elements between locations. The GeneralizedSpace-Time Autoregressive (GSTAR) method is a suitable method to be applied to CPI data because it involves elements of time and location (spatiotemporal). The problem is that the GSTAR model cannot detect any correlated residuals. The GSTAR model was developed into the GSTAR-SUR model to estimate parameters with correlated residuals so produce more efficient estimates. The purpose of this study was to determine the best GSTAR-SUR model to predict the CPI of six cities in Central Java, namely Cilacap, Purwokerto, Kudus, Surakarta, Semarang, and Tegal. The data that used is secondary data sourced from BPS Central Java Province. Based on the results of the analysis, the best model formed is the GSTAR-SUR (11)-I(1) model with an RMSE value of 6.213. Forecasting results show that the CPI value for the next 6 months will increase every month for each city

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“…119 minimum value of the AR and MA lags lies at lag 0 (Gusnadi et al, 2015) (Susila, 2022) . Table 5 shows the VAR model's minimum AR and MA error values ( 1,4,5).…”

Section: Causal Influence Between Jsx Composite Index and Ftse Strait...mentioning

confidence: 99%

Causal Influence between the JSX Composite Index and the FTSE Straits Times Index in Time Series

Susila

2022

JEP

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This research analyzes the causal influence between the JSX Composite Index and the FTSE Straits Times Index. The data used in this study is a monthly period time series data. The period for the JCI to be observed is January 2015 to June 2022. The VAR method was used to analyze the causal influence of the two indices. The analysis results show that the appropriate VAR model is VAR ( 1,4,5), RMSE value of 212.15 for the JSX Composite Index and 123.76 for the FTSE Straits Times Index. Based on the analysis results using the VAR model, information is obtained that the two indices influence each other. The value of the JSX Composite Index formed in the current period is controlled by the value of the JSX Composite Index and the FTSE Straits Times Index in the previous period. Likewise, for the FTSE Straits Times Index, the results show that the value of the FTSE Straits Times Index formed in the current period is influenced by the value of the JSX Composite Index and the FTSE Straits Times Index in the previous period.

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Spatio-Temporal Analysis of Rupiah Loans Provided by Commercial Banks and Rural Banks

Cited by 4 publications

References 12 publications

Customer Relationship Management melalui Orientasi Pasar dan Inovasi Organisasi untuk Meningkatkan Kinerja Pemasaran Bisnis Online

Customer Relationship Management melalui Orientasi Pasar dan Inovasi Organisasi untuk Meningkatkan Kinerja Pemasaran Bisnis Online

Forecasting the Consumer Price Index With Generalized Space-Time Autoregressive Seemingly Unrelated Regression (Gstar-Sur): Compromise Region and Time

Causal Influence between the JSX Composite Index and the FTSE Straits Times Index in Time Series

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