Abstract
In the Informatics Study Program there is a concentration which is divided into 2 parts which students will choose when entering semester V. The problem that often occurs is that some students sometimes choose concentration instead of seeing the results of the scores obtained during semesters I to IV, and there are some who choose majors because he followed his friends. This is what causes students to often be unprepared at the end of the semester. Therefore, it is necessary to analyze the value of semester I to semester IV obtained by students to help recommend to students which concentration to choose. The analysis is carried out by applying the K-Means Clustering Method, which will produce 2 groups, namely Software Engineering and Network Infrastructure. The K-Means Clustering method has the ability to group large amounts of data with relatively fast and efficient computation time. The Semester I to Semester IV Value Data Grouping application was made using Visual Basic .Net programming language and SQL Server 2008 database by applying one of the data mining methods used was K-Means Clustering. The grouping is done based on student score data obtained from Semester I to Semester IV (data attached). From this data, grouping is done into 2 groups, namely Cluster C1 Software Engineering and Cluster C2 Network Infrastructure. With this application, it can assist the Study Program in providing recommendations and also material for consideration to students in choosing a major whether Software Engineering or Network Infrastructure. Based on the results of the tests that have been carried out, the application of the K-Means Clustering Method in Grouping Semester Value Data has been successfully carried out, and can provide information based on 2 groups, namely Cluster C1 (Software Engineering) and Cluster C2 (Network Infrastructure), and the functionality of the application has been running. as expected.
References
- Abdurrahman, Ginanjar. 2016. Clustering Data Ujian Tengah Semester (UTS) Data Mining Menggunakan Algoritma K-Means. Jurnal Sistem dan Teknologi Informasi Indonesia Vol.1 No.2 Agustus 2016.
- Asriningtias, Yuli. Mardhiyah, Rodhyah. 2014. Aplikasi Data Mining Untuk Menampilkan Informasi Tingkat Kelulusan Mahasiswa. Jurnal Informatika Vol.8 No.1 Januari 2014.
- Asroni. Adrian, Ronald. 2015. Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang. Jurnal Ilmiah Semesta Teknika Vol.18. No.1 Mei 2015.
- Aswan. 2012. Kumpulan Program Kreatif Dengan Visual Basic .NET. Penerbit Informatika : Bandung.
- Lubis, Adyanata. 2016. Basis Data Dasar Untuk Mahasiswa Ilmu Komputer. Penerbit Deepublish : Yogyakarta.
- Merliana, Ni Putu Eka, dkk. 2015. Analisa Penentuan Jumlah Cluster Terbaik Pada Metode K-Means Clustering. Prosiding Seminar Nasional Multi Disiplin Ilmu & Call For Papers UNISBANK (SENDI_U). ISSN : 978-979-3649-81-8.
- Novanto, Yusak. 2015. Faktor-faktor Yang Berkaitan Dengan Prestasi Akademik Mahasiswa Sekolah Tinggi Teologi X. https://www.researchgate.net /publication/283352540
- Prasetyo, Eko. 2012. Data Mining Konsep dan Aplikasi Menggunakan Matlab. Penerbit Andi : Yogyakarta.
- Putri, Nisya Aldilla Hariza. Desrianty, Arie. Yuniar. 2014. Strategi Peningkatan Prestasi Akademik Mahasiswa Berdasarkan Variabel-Variabel Yang Mempengaruhinya. Jurnal Reka Integra Vol.02. No.1 Juli 2014. ISSN 2338-5081.
- Sudharmono, Neil Casaandra. Ayub, Mewati. 2015. Analisis Prestasi Akademik Mahasiswa Yang Mengikuti Kegiatan Kemahasiswaan. Jurnal Teknik Informatika dan Sistem Informasi Vol.1 No.2 Agustus 2015. e-ISSN: 2443-2229.
- Sulastri, Heni. Gufroni, Acep Irham. 2017. Penerapan Data Mining Dalam Pengelompokan Penderita Thalassaemia. Jurnal Teknologi dan Sistem Informasi Vol.3 No.2 2017. ISSN 2476-8812.
- Sutabri, Tata. 2012. Analisis Sistem Informasi. Penerbit Andi : Yogyakarta.