Evaluating Naïve Bayes Automated Classification for GBAORD

• Published in: Computer engineering and applications journal 2020-02, Vol.9 (1), p.29-37
• Main Authors: Mustika, Hani Febri, Syafiandini, Arida Ferti, Manik, Lindung Parningotan, Rianto, Yan
• Format: Article
• Language: English
• Subjects: Algorithms; Appropriations; Budgets; Classification; Expenditures; Machine learning; Performance evaluation; R&D; Research & development; Robustness
• ISSN: 2252-4274; 2252-5459
• DOI: 10.18495/comengapp.v9i1.320

The Indonesian Government Budget Appropriations or Outlays for Research and Government (GBAORD) has been analyzed manually every year to measure the government expenditures in research and development. The analysis process involved several experts in making the budget classification. This method, commonly known as manual classification, has its downsides, which are time consumption and inconsistent result. Therefore, a study about implementing the machine learning method in GBAORD budget classification to avoid inconsistency is proposed in the previous research. For further analysis, this paper evaluates the performance of the Naïve Bayes algorithm for the GBAORD budget classification. This paper aims to measure the robustness of the Naïve Bayes to classify GBAORD data taken from 2017 until 2019. This paper uses three models of Naive Bayes with different preprocessing methods and features. This paper concludes that using the Naïve Bayes algorithm in Indonesian GBAORD budget classification is suitable since the robustness of the algorithm is proved to be high with 96.788+-0.185% average accuracy.