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All Journal Prosiding Semnastek Sistemasi: Jurnal Sistem Informasi IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Pilar Nusa Mandiri Indonesian Journal of Information System IKRA-ITH Informatika : Jurnal Komputer dan Informatika EDUMATIC: Jurnal Pendidikan Informatika INFORMASI (Jurnal Informatika dan Sistem Informasi) ABDIPRAJA (Jurnal Pengabdian kepada Masyarakat) Jurnal Informatika Teknologi dan Sains (Jinteks) PADIMAS: Jurnal Pengabdian Masyarakat Explore
Iskandar, Iqbal Dzulfiqar
UNIVERSITAS BINA SARANA INFORMATIKA
Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Indonesian Journal of Information System

 1 
Parents’ Sum of Salaries Analyses towards School Tuition Fee Arrears Potential with Decision Tree Method Iskandar, Iqbal Dzulfiqar
Indonesian Journal of Information Systems Vol 2, No 1 (2019): Agustus 2019
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (736.538 KB) | DOI: 10.24002/ijis.v2i1.2168

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.
Parents’ Sum of Salaries Analyses towards School Tuition Fee Arrears Potential with Decision Tree Method Iqbal Dzulfiqar Iskandar
Indonesian Journal of Information Systems Vol. 2 No. 1 (2019): Agustus 2019
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v2i1.2168

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.
Co-Authors Agung Baitul Hikmah Amirulloh, , Imam Bambang Kelana Simpony Deddy Supriadi Dede Sofia Rahmah Erwin Triyana Fatah, Haerul Hendri Mardani, Hendri Mardani Imam Amirulloh Imam Amirulloh, Imam Amirulloh Kusmira, Mira Kusmira, Mira Mega Solihah Melisa Winda Pertiwi Miftah Farid Adiwisastra Mira Kusmira Rezkiani Rezkiani Roviani, Roviani Surahman, Mumun Taufik Wibisono Taufik Wibisono Taufik Wibisono Taufiqurrochman Taufiqurrochman yani sri mulyani Yanti Apriyani Yanti Apriyani, Yanti Apriyani Zidane Dwi Kusuma , Zidane Dwi Kusuma