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All Journal JUITA : Jurnal Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal CoreIT Techno Nusa Mandiri : Journal of Computing and Information Technology Jiko (Jurnal Informatika dan komputer) Jurnal Tekno Kompak Jurnal Ilmiah Ilmu dan Teknologi Rekayasa Jurnal Teknologi Informasi dan Komunikasi Bulletin of Information Technology (BIT) Melek IT: Information Technology Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Tedc Jurnal Ilmiah Pengabdian Kepada Masyarakat SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) Journal of Elektronik Sistem InformasI Inventor: Jurnal Inovasi dan Tren Pendidikan Teknologi Informasi
Tri Herdiawan Apandi
Politeknik Negeri Subang
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Journal : jurnal coreit

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Violation Types Determination of The Whistleblowing System Using the C4.5 Algorithm Dwi Vernanda; Rian Piarna; Helfira Lustiana; Tri Herdiawan Apandi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 1 (2023): June 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (317.21 KB) | DOI: 10.24014/coreit.v9i1.22897

Abstract

Whistleblowing is a complaint system and follow-up management of each violation report. The problem that arises is when determining the follow-up, namely determining the severity or severity of the violation and the sanctions given are only based on the superior's assessment without adhering to standard guidelines or rules. This results in the sanctions given not in accordance with the violations committed. The purpose of this study is to classify the types of violations so as to facilitate the determination of sanctions on the whistleblowing system using the C4.5 Algorithm. The partition was performed three times with the highest additional value of 0.8516 and a decision tree was obtained. Based on the decision tree, the final node that has been generated is then extracted into 27 rules. The classification results from the C4.5 Algorithm can be used to classify the types of violations with an accuracy rate of more than 80%. The first validation with 15 tests obtained an accuracy rate of 86.66%. The second validation is the combination of data on 125 cases of combination data and obtained an accuracy rate of 84.8%. The decision tree generated from three partitions consists of 27 rules that can be used as a pattern to classify the types of violations.
Co-Authors Abdullah, Muhammad Julmawansa Adi Firmansyah Asep Saepuloh Castaka Agus Sugianto Daniel Happy Putra Desy Triastuti Dwi Fernanda Dwi Vernanda Erick Febriyanto Giar Rahma Yunita Gumelar, Mikal Raya H, Haryati Haryati Haryati Haryati Helfira Lustiana Kurniawan, Bagas Lani Nurlani Masesa Angga Wijaya Maudy, Tri Rezqy Mohammad Iqbal Muhammad Fauzi, Willy Mustikasari Putri, Dewi Nida Hanifah Niqotaini, Zatin Nunu Nugraha Purnawan NurAfifah, Amirah Nurfitria Khoirunisa Nurfitria Khoirunnisa pohan, Nurul Fauziah Rian Hermawan Rian Piarna Risky Prabowo Roby Bayu Maulana Santika, Nisa Slamet Rahayu Slamet Rahayu Subagja, Fikri Hari Suhailla Binti Jili, Aisyah Sulaeman, Muhammad Soleh Taufan Abdurrachman Wulan Siti Nurul Yogi Saputra, Yogi Yohanes Sinung Nugroho