KLASTERISASI DATA PENERIMAAN MAHASISWA BARU UNTUK MENENTUKAN TARGET PROMOSI UNIVERSITAS SRIWIJAYA MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING

ANGGRAINI, NADYA and Jambak, Muhammad Ihsan and Bardadi, Ali (2022) KLASTERISASI DATA PENERIMAAN MAHASISWA BARU UNTUK MENENTUKAN TARGET PROMOSI UNIVERSITAS SRIWIJAYA MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING. Undergraduate thesis, Sriwijaya University.

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Abstract

Saat ini, persaingan antar perguruan tinggi negeri maupun swasta dalam rangka penerimaan mahasiswa baru memang semakin pesat. Universitas Sriwijaya adalah salah satu perguruan tinggi terbesar di Sumatera Selatan yang secara terus-menerus melakukan peningkatan mutu kualitas terhadap sarana dan prasarana untuk menarik calon mahasiswa. Dikarenakan persaingan yang ketat, dibutuhkan strategi yang tepat bagi universitas untuk melakukan promosi. Dalam penelitian ini, menerapkan ilmu data mining dalam melakukan pengolahan data penerimaan mahasiswa baru Universitas Sriwijaya tahun 2019-2020 untuk mengelompokkan data sebaran asal sekolah mahasiswa. Dari pengolahan yang telah dilakukan melalui aplikasi RapidMiner, terbentuklah 3 cluster berdasarkan jumlah penerimaan mahasiswa baru. Dari analisis karakteristik cluster yang terbentuk, diketahui bahwa cluster yang paling efisien dan efektif untuk dijadikan target promosi adalah cluster 1 dengan jumlah anggota cluster sebanyak 60 sekolah. Hasil dari penelitian ini dapat dijadikan usulan untuk Tim PMB Univeresitas Sriwijaya dalam penentuan target promosi pada tahun-tahun berikutnya. Currently, competition between public and private universities in the context of new student admissions is indeed increasing rapidly. Sriwijaya University is one of the largest universities in South Sumatra which continuously improves the quality of facilities and infrastructure to attract prospective students. Due to intense competition, it takes the right strategy for universities to promote. In this study, applying data mining science in processing data on new student admissions at Sriwijaya University in 2019-2020 to classify data from the distribution of student schools. From the processing that has been done through the RapidMiner application, 3 clusters are formed based on the number of new student admissions. From the analysis of the characteristics of the cluster formed, it is known that the most efficient and effective cluster to be used as a promotion target is cluster 1 with a total of 60 cluster members. The results of this study can be proposed to the Sriwijaya University PMB Team in determining promotion targets in the following years.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Data Mining, Klasterisasi, K-Means, Promosi, RapidMiner
Subjects:	H Social Sciences > HF Commerce > HF5410-5417.5 Marketing. Distribution of products > HF5415.126.R38 Database marketing--Statistical methods. Data mining--Statistical methods. Big data--Statistical methods. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data. T Technology > T Technology (General) > T57-57.97 Applied mathematics. Quantitative methods > T57.5 Data processing Cf. HF5548.125+ Business data processing Operations research. Systems analysis
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Nadya Anggraini
Date Deposited:	13 Jul 2022 03:18
Last Modified:	13 Jul 2022 03:18
URI:	http://repository.unsri.ac.id/id/eprint/73748

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