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All Journal JIPMat (Jurnal Ilmiah Pendidikan Matematika) AR-RIAYAH : Jurnal Pendidikan Dasar EKSAKTA : Jurnal Penelitian dan Pembelajaran MIPA PeTeKa JURNAL MathEdu (Mathematic Education Journal) JURNAL PENDIDIKAN MIPA Edukasi Lingua Sastra Jurnal Nauli Edumatika Journal of Digital Learning and Distance Education Dharma Jnana Innovative: Journal Of Social Science Research Journal Neosantara Hybrid Learning Jurnal Pengabdian Kepada Masyarakat Radisi JAR'S (Journal of Advanced Research in Informatics) Welfare: Jurnal Pengabdian Masyarakat
Erwina Azizah Hasibuan, Erwina Azizah
Universitas Graha Nusantara (UGN)
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Analisis Association Rule Untuk Memprediksi Kelulusuan Mahasiswa F-Kip Universitas Graha Nusantara Padangsidimpuan Wendi, Al; Mandopa, Andi Saputra; Hasibuan, Erwina Azizah
Journal of Advanced Research in Informatics Vol 2 No 1 (2023): JARS : Journal of Advanced Research in Informatics
Publisher : Fakultas Teknik, Universitas Wiraraja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24929/jars.v2i1.3044

Abstract

University administrators need graduation predictions to determine initial steps to avoid dropout. The length of a student's study period is determined by various factors. Therefore, it is important to know which students may not graduate on time. Data mining techniques can be used to explore new insights to predict student graduation. By using the association rule technique we can obtain information from large data such as data from universities. The aim of this research is to determine the pattern of study duration for Graha Nusantara University F-KIP students. by using the association rule data mining method and comparing a priori algorithms and hash-based algorithms. The data used is Graha Nusantara University F-KIP master data which is processed using association rule data mining techniques with a priori algorithms and hash-based algorithms with minimum support of 1% and minimum confidence of 1%. The results of data processing with the a priori algorithm are the same as the results of data processing with the hash-based algorithm, namely 49 2-itemset combinations. The pattern that was formed included 7.5% of graduates from the mathematics department studying for more than 5 years with a confidence value of 38.5%.
Co-Authors Adek Nilasari Harahap Adiguzel, Tufan Afniria Pakpahan Afniria, Afniria Agus Makmur Agus Makmur Panjaitan Alvi Sahrin Nasution Alwendi, Alwendi Dina Syahfitri Eva Permata Sari Evi Susanti Siregar Fita Delia Gultom Harahap, Adek Nilasari Harahap, Adek Nilasari Harahap, Sahrul Hrp, Haritsah Hammamah Jainal Abidin Jainal Abidin Julianto Julianto Khairunnisa Samosir Mahdalia Harahap mandopa, andi saputra Nasution, Alvi Sahrin Nur Haminah Hasibuan Nurhawani Sitompul Rini Anggraini Pakpahan Siregar, Yasser Arafat Sumyanti, Sri Susi Sulastri Lubis Trinanda Anugrah Tambun wendi, al Winda Sari Wirna Arifitriana Zahraini, Najmi