@inproceedings{scholars17072,
           pages = {143--148},
       publisher = {Association for Computing Machinery},
         journal = {ACM International Conference Proceeding Series},
           title = {Revenue Prediction for Malaysian Federal Government Using Machine Learning Technique},
            year = {2022},
             doi = {10.1145/3524304.3524337},
            note = {cited By 0; Conference of 11th International Conference on Software and Computer Applications, ICSCA 2022 ; Conference Date: 24 February 2022 Through 26 February 2022; Conference Code:179912},
        keywords = {Decision trees; Predictive analytics; Random forests, Federal governments; Feed forward neural net works; Financial accounts; Forecasting error; Machine learning methods; Machine learning techniques; Malaysia; Malaysians; Random forests; Tax revenue, Feedforward neural networks},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132290252&doi=10.1145\%2f3524304.3524337&partnerID=40&md5=18d2b239ebcf3b6cd7b3e8b589134926},
        abstract = {Every country has its own federal government. Each federal government will have its own financial account which consist of revenue and expenditure. Focusing on the revenue, it has many sources that includes three main categories. They are tax revenue, non-tax revenue and non-revenue receipts. The revenue will then be used for operational and development purposes. Currently in Malaysia, the federal government revenue is only using forecasting. This can cause large forecasting error. Though it can be overcome using predictive analytics. Since there are many machine learning methods available, the appropriate methods can be identified to do the prediction. Based on previous research, feed forward neural network (FFNN), random forest and linear regression seems to be the most suitable. After conducting several experiments, it is found that FFNN achieved highest accuracy, followed by random forest. As for linear regression, it does not achieve good accuracy, thus it is considered as a not suitable method to be used on the federal government revenue dataset. {\^A}{\copyright} 2022 ACM.},
          author = {Noor, N. and Sarlan, A. and Aziz, N.},
            isbn = {9781450385770}
}