scholarly journals Support Vector Machine Predictive Analysis Implementation: Case Study of Tax Revenue in Government of South Lampung

2020 ◽  
Vol 3 ◽  
pp. 323-327
Author(s):  
Arina Ashfa Fikriya ◽  
Sanny Hikmawati
Keyword(s):  
Support Vector Machine ◽  
Business Intelligence ◽  
Predictive Analysis ◽  
Support Vector ◽  
Tax Revenue ◽  
Local Revenue ◽  
Revenue Sources ◽  
Tax Reduction ◽  
The Government

Tax is potential revenue that is used by the government as a source of funding to run the government. One of the conditions needed to implement decentralization is the availability of sources of local revenue. Some local revenue sources are hotel and restaurant taxes. Issues raised in this research are that the number of hotel and restaurant visitors changes every year, resulting in fluctuations in the amount of tax. On the other hand, the Government of South Lampung had difficulty in predicting the target of hotel and restaurant tax revenue when arranging a revenue budget. This is due to the absence of formula to calculate the potential tax revenue accurately, resulting in a lack of strategic management for local revenue improvement. Now, Business Intelligence is becoming a trend. Many sectors use Business Intelligence to analyze and prepare new strategies and improve performance. Thus, it is necessary to use Business Intelligence to predict the potential of hotel and restaurant tax revenue so that the Government of South Lampung can develop appropriate strategies to improve local tax revenue and minimize tax reduction. The method used is a predictive analysis using the Support Vector Machine (SVM).  The result of this study is expected to be taken into consideration for South Lampung Local Government Revenue Service, in particular for the determination of the target of the hotel and restaurant tax sector in the coming year.

scholarly journals Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

2021 ◽  
Vol 13 (6) ◽  
pp. 3497
Author(s):  
Hassan Adamu ◽  
Syaheerah Lebai Lutfi ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Rohail Hassan ◽  
Assunta Di Vaio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Primary Objective ◽  
Support Vector ◽  
Standard English ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
The Public ◽  
The Government

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

Determination of Pineapple Ripeness Using Support Vector Machine for Philippine Standards

2021 ◽  
Author(s):  
Erin Jelacio L. Aguilar ◽  
Giann Karlo P. Borromeo ◽  
Jocelyn Flores Villaverde
Keyword(s):  
Support Vector Machine ◽  
Support Vector

scholarly journals Twitter sentiment analysis of the relocation of Indonesia's capital city

2020 ◽  
Vol 9 (4) ◽  
pp. 1620-1630
Author(s):  
Edi Sutoyo ◽  
Ahmad Almaarif
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Point Of View ◽  
Capital City ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Evaluation Algorithm ◽  
The Government ◽  
The Many

Indonesia has a capital city which is one of the many big cities in the world called Jakarta. Jakarta's role in the dynamics that occur in Indonesia is very central because it functions as a political and government center, and is a business and economic center that drives the economy. Recently the discourse of the government to relocate the capital city has invited various reactions from the community. Therefore, in this study, sentiment analysis of the relocation of the capital city was carried out. The analysis was performed by doing a classification to describe the public sentiment sourced from twitter data, the data is classified into 2 classes, namely positive and negative sentiments. The algorithms used in this study include Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The results of the performance evaluation algorithm showed that support vector machine outperformed as compared to 3 algorithms with the results of Accuracy, Precision, Recall, and F-measure are 97.72%, 96.01%, 99.18%, and 97.57%, respectively. Sentiment analysis of the discourse of relocation of the capital city is expected to provide an overview to the government of public opinion from the point of view of data coming from social media. 

Michigan and Ohio K–12 Educational Financing Systems: Equality and Efficiency

2014 ◽  
Vol 9 (4) ◽  
pp. 417-445 ◽  
Author(s):  
Michael Conlin ◽  
Paul N. Thompson
Keyword(s):  
School Funding ◽  
State Level ◽  
Capital Expenditures ◽  
Tax Revenue ◽  
Local Revenue ◽  
Revenue Sources ◽  
Expenditures Per Pupil ◽  
Average Revenue ◽  
Operating Expenditures ◽  
K 12

We consider issues of equality and efficiency in two different school funding systems—a state-level system in Michigan and a foundation system in Ohio. Unlike Ohio, the Michigan system restricts districts from generating property or income tax revenue to fund operating expenditures. In both states, districts fund capital expenditures with local tax revenue. Our results indicate that although average revenue and expenditures per pupil in Michigan and Ohio are almost identical, the distributions of the various revenue sources are quite different. Ohio’s funding system has greater equality in terms of total revenue, largely due to Ohio redistributing state funds to the least wealthy districts while Michigan does not. We find relatively wealthy Michigan districts spend more on capital expenditures, whereas relatively wealthy Ohio districts spend more on labor and materials. This suggests that constraints on raising local revenue to fund operating expenditures in Michigan could create efficiency issues.

Utilization of Classification Techniques for the Determination of Liquefaction Susceptibility of Soils

2016 ◽  
pp. 124-160 ◽  
Author(s):  
J. Jagan ◽  
Prabhakar Gundlapalli ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Earthquake Engineering ◽  
Relevance Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Ground Acceleration ◽  
Geotechnical Earthquake Engineering ◽  
Liquefaction Susceptibility ◽  
Input Variables

The determination of liquefaction susceptibility of soil is a paramount project in geotechnical earthquake engineering. This chapter adopts Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Least Square Support Vector Machine (LSSVM) for determination of liquefaction susceptibility based on Cone Penetration Test (CPT) from Chi-Chi earthquake. Input variables of SVM, RVM and LSSVM are Cone Resistance (qc) and Peak Ground Acceleration (amax/g). SVM, RVM and LSSVM have been used as classification tools. The developed SVM, RVM and LSSVM give equations for determination of liquefaction susceptibility of soil. The comparison between the developed models has been carried out. The results show that SVM, RVM and LSSVM are the robust models for determination of liquefaction susceptibility of soil.

scholarly journals Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

2015 ◽  
Vol 22 (3) ◽  
pp. 341-350 ◽  
Author(s):  
Łukasz Lentka ◽  
Janusz M. Smulko ◽  
Radu Ionescu ◽  
Claes G. Granqvist ◽  
Laszlo B. Kish
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Temperature Change ◽  
Nonlinear Regression ◽  
Ultraviolet Irradiation ◽  
Support Vector ◽  
Gas Mixture ◽  
Gas Concentration ◽  
Single Sensor

Abstract This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based) nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.

Sign in / Sign up

Export Citation Format

Share Document