Jurnal Sistim Informasi dan Teknologi
2019, Vol. 1, No. 3

Algoritma K-Means Untuk Klasterisasi Tugas Akhir Mahasiswa Berdasarkan Keahlian

Weri Sirait (Universitas Putra Indonesia YPTK Padang)
Sarjon Defit (Universitas Putra Indonesia YPTK Padang)
Gunadi Widi Nurcahyo (Universitas Putra Indonesia YPTK Padang)



Article Info

Publish Date
01 Sep 2021

Abstract

School of Information and Computer Management (STMIK) Indonesia Padang is a private university under the auspices of the Higher Education Service Institution (LLDIKTI) Region X, producing graduates who are competent in the field of system analysts and database administrators. Requirements to meet undergraduate graduates (S1) final year students need to complete a final project or thesis. Final year students at STMIK Indonesia Padang often experience confusion in taking the final assignment topic. This is due to the fact that the final year students have not been able to direct their potential in determining the final assignment topic. In this case, researchers conducted the process of grouping final level students using the Data Mining K-means Clustering technique. The process of grouping final-level students is done by utilizing the data of course values ​​from the field mapping system analysts and database administrators. In this grouping two clusters will be produced, namely students taking the final assignment of system analysts and database administrator. So by using this K-means Clustering method, students have direction in taking the final assignment topic. The results obtained from 40 data samples used were students who took the topic of the final project system analysts as many as 20 students and students who took the final assignment of database administrators were 20 students.

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Journal Info

Abbrev

JSisfotek

Publisher

Subject

Computer Science & IT Decision Sciences, Operations Research & Management

Description

The Jurnal Sistim Informasi dan Teknologi (JSISFOTEK) aims to publish manuscripts that explore information systems and technology research and thus develop computer information systems globally. We encourage manuscripts that cover the following topic areas: - Analytics, Business Intelligence, and ...