IMPLEMENTASI ALGORITMA FREQUENT PATTERN-GROWTH TERHADAP POLA MAHASISWA LULUSAN DENGAN RAPIDMINER - Ria Rismayati; Ismarmiaty Ismarmiaty (Jurnal Informatika dan Rekayasa Elektronik, 2021)
This research aims to analyze the patterns formed from interconnected items using existing data mining techniques related to the graduation data of students of the S1 Informatics Engineering study program at STMIK Bumigora Mataram. The Undergraduate Informatics Engineering Study Program is one of the study programs at the Bumigora College of Management and Informatics (STMIK) which was founded on November 23, 1993. One of the factors that is considered influencing graduation is the preparation of a thesis for final year students. The number of graduates produced is inversely proportional to the use of graduate data for institutional advancement. This shows that the graduate data has not been maximally used which is used as material or input, especially for the study program to develop a thesis implementation management system to make it more effective and efficient. This study uses the association rule with the calculation of the FP-Growth Algorithm. FP-Growth is part of the association technique in data mining, where an alternative algorithm can be used to determine the data set that appears most frequently in a data set. The steps taken were (a) data collection, (b) selecting data, (c) applying the FP-Growth method, (d) implementing the software and (e) testing the results. From the test results obtained the output of 24 rules, which are then taken 8 strong association rules that have a high level of trust and are supported by a percentage of the overall data with a value of lift ratio> 1. The conclusion is that the students graduated with Mr. RA has a good thesis score between the 70-80 range and comes from the competence of Computer Networks with a 100% confidence level and is supported by 14.4% of the overall data, in line with graduate students with Mr. BK with a satisfactory thesis score between the range 80-90 which comes from Multimedia competence with a confidence level of 94.1% and is supported by 12.1% of the overall data with an infinite lift ratio value of 2.4