2019•Indonesian Journal of Information SystemsOpen access

Parents’ Sum of Salaries Analyses towards School Tuition Fee Arrears Potential with Decision Tree Method

Iqbal Dzulfiqar Iskandar

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Abstract

School tuition fee is typically used for funding school operational, i.e. paying honorary teachers in public and private schools, purchasing practical instruments, printing examination worksheets, and other net-operational costs. According to the discovered data in the research environment, the funding is unable to be acquired properly due to students’ school tuition fees arrears for months even years until they graduate. Considering the condition, this research is conducted to identify the potential of students’ school tuition arrears, based on the sum of their parents’ salaries centered on the business intelligence approach, using the decision tree method. The analysis results show that, students whose parents’ income is less than Rp 672.500,00 will be potentially in arrears with school tuition more than Rp 900.000,00 each month, while students whose parents’ income is above Rp 672.500,00 are potentially in arrears of less than Rp 900.000,00 or not in arrears. To evaluate the effectiveness of the decision tree algorithm for data processing, it has an accuracy value of 95.97%, with a precision of 94.96% that means the algorithm has a good correlation based on attributes and the data that have been processed by the algorithm.

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What this paper is about

School tuition fee is typically used for funding school operational, i.e. paying honorary teachers in public and private schools, purchasing practical instruments, printing examination worksheets, and other net-operational costs. According to the discovered data in the research environment, the funding is unable to be acquired properly due to students’ school tuition fees arrears for months even years until they graduate. Considering the condition, this research is conducted to identify the potential of students’ school tuition arrears, based on the sum of their parents’ salaries centered on the business intelligence approach, using the decision tree method. The analysis results show that, students whose parents’ income is less than Rp 672.500,00 will be potentially in arrears with school tuition more than Rp 900.000,00 each month, while students whose parents’ income is above Rp 672.500,00 are potentially in arrears of less than Rp 900.000,00 or not in arrears. To evaluate the effectiveness of the decision tree algorithm for data processing, it has an accuracy value of 95.97%, with a precision of 94.96% that means the algorithm has a good correlation based on attributes and the data that have been processed by the algorithm.

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Available abstract

School tuition fee is typically used for funding school operational, i.e. paying honorary teachers in public and private schools, purchasing practical instruments, printing examination worksheets, and other net-operational costs. According to the discovered data in the research environment, the funding is unable to be acquired properly due to students’ school tuition fees arrears for months even years until they graduate. Considering the condition, this research is conducted to identify the potential of students’ school tuition arrears, based on the sum of their parents’ salaries centered on the business intelligence approach, using the decision tree method. The analysis results show that, students whose parents’ income is less than Rp 672.500,00 will be potentially in arrears with school tuition more than Rp 900.000,00 each month, while students whose parents’ income is above Rp 672.500,00 are potentially in arrears of less than Rp 900.000,00 or not in arrears. To evaluate the effectiveness of the decision tree algorithm for data processing, it has an accuracy value of 95.97%, with a precision of 94.96% that means the algorithm has a good correlation based on attributes and the data that have been processed by the algorithm.

Key concepts: Arrears, Purchasing, Value (mathematics), Actuarial science, Decision tree, Business, Finance, Computer science

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