Prediction of Local Government Revenue using Data Mining Method

Local Government Revenue or commonly abbreviated as PAD is part of regional income which is a source of regional financing used to finance the running of government in a regional government. Each local government must plan Local Government Revenue for the coming year so that a forecasting method is needed to determine the Local Government Revenue value for the coming year. This study discusses several methods for predicting Local Government Revenue by using data on the realization of Local Government Revenue in the previous years. This study proposes three methods for forecasting local Government revenue. The three methods used in this research are Multiple Linear Regression, Artificial Neural Network, and Deep Learning. In this study, the data used is Local Revenue data from 2010 to 2020. The research was conducted using RapidMiner software and the CRISP-DM framework. The tests carried out showed an RMSE value of 97 billion when using the Multiple Linear Regression method and R2 of 0,942, the ANN method shows an RMSE value of 135 billion and R2 of 0.911, and the Deep Learning method shows the RMSE value of 104 billion and R2 of 0.846. This study shows that for the prediction of Local Government Revenue, the Multiple Linear Regression method is better than the ANN or Deep Learning method. Keywords— Local Government Revenue, Multiple Linear Regression, Artificial Neural Network, Deep Learning, Coefficient of Determination

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An application of QSRR approach and multiple linear regression method for lipophilicity assessment of flavonoids

Journal of Pharmaceutical and Biomedical Analysis ◽

10.1016/j.jpba.2018.11.024 ◽

2019 ◽

Vol 164 ◽

pp. 681-689 ◽

Cited By ~ 4

Author(s):

Mariusz Zapadka ◽

Mateusz Kaczmarek ◽

Bogumiła Kupcewicz ◽

Przemysław Dekowski ◽

Agata Walkowiak ◽

...

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Regression Method ◽

Linear Regression Method ◽

Multiple Linear Regression Method

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Improvement of multiple linear regression method for statistical downscaling of monthly precipitation

International Journal of Environmental Science and Technology ◽

10.1007/s13762-017-1511-z ◽

2017 ◽

Vol 15 (9) ◽

pp. 1897-1912 ◽

Cited By ~ 4

Author(s):

H. A. Pahlavan ◽

B. Zahraie ◽

M. Nasseri ◽

A. Mahdipour Varnousfaderani

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Statistical Downscaling ◽

Regression Method ◽

Linear Regression Method ◽

Monthly Precipitation ◽

Multiple Linear Regression Method

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Accident Prediction Model at Unsignalized Intersection Using Multiple Linear Regression Method

Nigerian Journal of Environmental Sciences and Technology - March 2018 ◽

10.36263/nijest.2017.02.0047 ◽

2017 ◽

Vol 1 (2) ◽

pp. 379-389

Author(s):

N. K. Oghoyafedo ◽

J. O. Ehiorobo ◽

Ebuka Nwankwo

Keyword(s):

Linear Regression ◽

Prediction Model ◽

Multiple Linear Regression ◽

Regression Method ◽

Linear Regression Method ◽

Design Standards ◽

Accident Prediction ◽

Geometric Characteristics ◽

Unsignalized Intersections ◽

Multiple Linear Regression Method

The issue of road accidents is an increasing problem in developing countries. This could be due to increasing road traffic/vehicle occupancy, geometric characteristics and road way condition. The factors influencing accidents occurrence are to be analysed for remedies. The purpose of this research is to develop an accident prediction model as a measure for future study, aid planning phase preceding the designed intervention, enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections. Five intersections were selected randomly within Benin City and traffic count carried out at these intersections as well as geometric characteristics and roadway conditions. The prediction model was developed using multiple linear regression method and the standard error of estimate was computed to show how close the observed value is to the regression line. The model was validated using coefficient of multiple determination. The establishment of the relationship between accidents and traffic flow site characteristics on the other hand would enable improvement to be more realistically accessed. This study will also enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections.

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THE EFFECT OF LEADERSHIP, JOB SATISFACTION AND ORGANIZATIONAL COMMITMENT ON EMPLOYEE PERFORMANCE AT RUMAH SAKIT UMUM DAERAH BESUKI SITUBONDO, INDONESIA

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v6.i6.2018.1377 ◽

2018 ◽

Vol 6 (6) ◽

pp. 322-334

Author(s):

Amrozi ◽

Zarah Puspitaningtyas ◽

Djoko Poernomo

Keyword(s):

Job Satisfaction ◽

Organizational Commitment ◽

Linear Regression ◽

Multiple Linear Regression ◽

Random Sampling ◽

Employee Performance ◽

Sampling Technique ◽

Regression Method ◽

Linear Regression Method ◽

Multiple Linear Regression Method

This study is to examine the influence of leadership, job satisfaction and organizational commitment on employee performance. Population in this research was the entire employees of Rumah Sakit Umum Daerah (RSUD) Besuki, Situbondo, Indonesia which was about 295 peoples. Then, the researcher applied probability random sampling technique to select 170 respondents as the sampling. The researcher analyzed the data by applying multiple linear regression method. The result shows that leadership and job satisfaction contribute positive and significant effect on employee performance, while organizational commitment has no effect on employee performance.

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Pengaruh Disiplin Kerja dan Pemberian Insentif terhadap Kinerja Karyawan PT Putra Karisma Palembang

Jurnal Nasional Manajemen Pemasaran & SDM ◽

10.47747/jnmpsdm.v2i1.211 ◽

2021 ◽

Vol 2 (1) ◽

pp. 25-42

Author(s):

Andini Dwi Saputri ◽

Susi Handayani ◽

Muhammad Kurniawan DP

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Employee Performance ◽

Regression Method ◽

Linear Regression Method ◽

Multiple Linear Regression Method ◽

Sample Technique ◽

Saturated Sample ◽

Analyze Data

This study aims to analyze the effects of work discipline and incentives on the performance of employees of PT Putra Karisma Palembang. The sample was selected using the saturated sample technique. Data from 57 respondents were collected through interviews, documentation, and questionnaires. This study implemented the multiple linear regression method to analyze data. The results prove that partially each of work discipline and incentives has no significant effect on employee performance. However, simultaneously work discipline and incentives have a significant effect on employee performance. This insight is beneficial for PT Putra Karisma Palembang. To improve employees' performance, the company should consider both work discipline and incentives factors together. Penelitian ini bertujuan untuk menganalisis pengaruh disiplin kerja dan pemberian insentif terhadap kinerja karyawan PT Putra Karisma Palembang. Sampel dipilih menggunakan teknik sampel jenuh. Data 57 responden dikumpulkan melalui wawancara, dokumentasi, dan kuesioner. Studi ini mengimplementasikan metode regresi linier berganda untuk menganalisis data. Hasil investigasi membuktikan bahwa secara parsial baik disiplin kerja maupun pemberian insentif masing-masing tidak berpengaruh signifikan terhadap kinerja karyawan. Tetapi, secara simultan disiplin kerja dan pemberian insentif berpengaruh signifikan terhadap kinerja karyawan. Fakta ini bermanfaat bagi PT Putra Karisma Palembang. Untuk meningkatkan kinerja karyawan, maka perusahaan harus mempertimbangkan faktor disiplin kerja dan pemberian insentif secara bersama-sama.

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ANALYSIS OF FACTORS AFFECTING THE PRICE FLUCTUATION OF CAYENNE PEPPER IN MALANG REGENCY

Agrisocionomics Jurnal Sosial Ekonomi Pertanian ◽

10.14710/agrisocionomics.v5i1.7123 ◽

2021 ◽

Vol 5 (1) ◽

pp. 19-26

Author(s):

Nurul Laili ◽

Sri Hindarti ◽

Dwi Susilowati

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Research Method ◽

Commodity Prices ◽

Secondary Data ◽

Regression Method ◽

Linear Regression Method ◽

Factors Affecting ◽

Independent Variables ◽

Multiple Linear Regression Method

This study aims to 1) Analyze the pattern of changes in commodity prices for spanish pepper in Malang District. 2) Analyzing the factors that influence fluctuations in the price of spanish pepper in Malang District. The research method used is quantitative method that uses secondary data in the form of time series obtained from several related agencies, namely the Central Statistics Agency of Malang District, Department of Industry and Trade, and Department of food crops, horticulture, and plantation in Malang District. Analysis of the data used is multiple linear regression with the dependent variable is the price at the consumer level from 2009-2018, while the independent variables use the data of the price of spanish pepper at the producer level, the amount of production, and the amount of consumption from 2009-2018. The study found that: 1) The development of the price of spanish pepper had a trend that tended to increase during the last 10 years. 2) From the results of data processing using multiple linear regression method with Eviews 9.0 application, it is found that the factor that significantly influences changes in the price of spanish pepper is the price at the producer level, while the amount of production of spanish pepper and the number of requests does not significantly affect the change in spanish pepper prices in Malang District.

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