Penerapan Data Mining Pada Penerimaan Mahasiswa Baru Dengan Algoritma K-Means Clustering

Abstract

This paper aims to grouping data using Clustering method with k-means algorithm to find potential majors and type of schools that produce feature students who have a good GPA score in semester 1 and semester 2 at Politeknik STMI Jakarta. Dataset from academic data for 2017-2020 has been processed with Rapid Miner showing that in Automotive Business Administration study program there are 3 clusters of students where cluster 0 marked as best cluster is dominated by high school students majoring in Science and Social Sciences. Automotive Industry Information System study program produces 2 clusters of students where cluster 0 marked as best cluster is dominated by high school students majoring in science and vocational high school majoring in mechanical engineering. Automotive Industrial Engineering study program produces 2 clusters of students where cluster 1 marked as best cluster is dominated by high school students majoring in science. Polymer Chemical Engineering study program produces 6 student clusters where cluster 4 marked as best cluster which all come from high school students majoring in science.

Implementasi Algoritma K-Means Pada Pengelompokan Data Penerimaan Peserta Didik Baru Di SMKN 1 Balongan

“…Hasil dari penelitian ini dapat disimpulkan bahwa Penerapan algoritma K-means dalam penelitian ini efisien karena mampu mengelompokkan data akademik dari berbagai program studi dengan hasil yang bermakna. Algoritma ini dapat menghasilkan cluster-cluster yang memiliki karakteristik yang berbeda, sehingga memberikan informasi yang berguna bagi panitia penerimaan mahasiswa baru untuk memprioritaskan jurusan dan jenis sekolah tertentu pada proses seleksi penerimaan mahasiswa baru [17].…”

Penerapan Algoritma Regresi Linear Pada Data Harga Cabai Rawit Di Pasar Indihiang

“…Biasanya nilai label -label tersebut dapat berupa nilai numerik maupun nilai kategorikal [17]. Tahapan-tahapan yang dilakukan pada penelitian kali ini mengikuti proses standar dari data mining yaitu Cross-Industry Standard for Data Mining (CRISP-DM) dimana ada enam fase Business Understanding (pemahaman bisnis), Data Understanding (pemahaman data), Data Preparation (persiapan data), Modeling (pemodelan data), Evaluation, Deployment [18].…”