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All Journal JURNAL PEMBANGUNAN DAN KEUANGAN DAERAH Teika Journal of Economic, Bussines and Accounting (COSTING) Jurnal Pendidikan dan Konseling JIAI (Jurnal Ilmiah Akuntansi Indonesia) JIIP (Jurnal Ilmiah Ilmu Pendidikan) INTELEKTIVA Jurnal Sosial dan Teknologi Journal of Vision and Ideas (VISA) Journal Transformation of Mandalika Journal of Comprehensive Science (JCS) Jurnal Terapan Ilmu Manajemen dan Bisnis (JTIMB) Jurnal Indonesia Sosial Sains Jurnal Ekonomis VISA: Journal of Vision and Ideas
Joan Yuliana Hutapea
Universitas Advent Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Other

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Journal : Teika

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Prediksi Permintaan Mata Kuliah Pada Semester Padat Dengan Menggunakan Teknik Association Rule Dengan Algoritma Apriori Pada Fakultas Teknologi Informasi Universitas Advent Indonesia Joan Yuliana Hutapea
TeIKa Vol 9 No 1 (2019): TeIKa: April 2019
Publisher : Fakultas Teknologi Informasi - Universitas Advent Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36342/teika.v9i01.794

Abstract

Short semester is a period of lectures carried out by an educational institution / university with the aim of facilitating students who wish to shorten their lecture time, as well as those who wish to improve their grades that are inadequate. In this semester, the preparations made refer to the student's request for certain courses that he wants to attend. In order to make a good preparation, the faculty leaders need to predict what courses are likely to be taken by students, so that they can prepare the registration and assign the lecturers whose related to the course. The purpose of this study is to collect data of students request for certain courses useing previous data. For data mining technique, the author used the Association Rule and apriori algorithm to find the frequent itemset to make prediction. By analyzing 25 participant data of students at the Faculty of Information Technology, on short semester of the academic year 20162017, the resuts shows that there was a high level of support and confidence in several combinations of subjects. In addition, it is also found that the rule with the highest final association value is in the rule of "Choose B then choose K", namely: If the student chooses course B (Algorithm), then he will also choose course K (Algorithm Practice) with the value of support 20% and 100% confidence. Keywords: Data Mining, Association Rule, Apriori Algorithm
Co-Authors Agus Hasiholan Simbolon Alden R. Rajagukguk Andrea Gina Muntuan ANDRES PARU NUBATONIS Daniel Sihotang Doloksaribu, Eveline Angelica Efata Indri Tarigan Efata Indri Tarigan Gena Gemasta Barus Heddry Purba James Uly Reke Julia Chatrine Situmorang Larita Purba Mark Raymond Silitonga Megil Fernanda Mellisa Karen Benedicta Mellisa Karen Benedicta Nathanael Christoper RICKA PUTRI YANI BR SINAGA Sherly Advent Obidience Ndruru Sudarma Sinaga Thalia Therdi Alnando Viona Latersia Sinuhaji William Wiclyfe Panjaitan Yolanda Frisia Sibarani