Alumni Data Grouping Using the K-Means Clustering Method for Study Program Curriculum Development

Penda Sudarto Hasugian; Jijon Raphita Sagala; Lela Dwi Ani

Authors

Penda Sudarto Hasugian STMIK Pelita Nusantara
Jijon Raphita Sagala STMIK Pelita Nusantara
Lela Dwi Ani STMIK Pelita Nusantara

Keywords:

Datamining, K-means method, Data grouping, Rapid Miner

Abstract

Application of Datamining by applying the k-means clustering method to classify STMIK Pelita Nusantara alumni data as a basis for developing study program curricula that are more relevant to the needs of the world of work or industrial needs. Where the K-Means Clustering Method is used to group alumni based on similar characteristics they have, such as personal data, academic achievement, areas of expertise, and job information after graduating from college. The research data source used is graduate data for the 2021/2022 academic year. The data collection method was carried out by distributing questionnaires directly to alumni. The application of the k-means method is carried out by forming 2 groups (clusters), namely C1 = Liner and C2 = Not Linear. Data testing is also carried out using the rapid miner application. So that by grouping alumni data, it is hoped that tertiary institutions can identify the needs and preferences of alumni for the study programs followed so that they can develop study program curricula that are more targeted and in accordance with the needs of the job market.Application of Datamining by applying the k-means clustering method to classify STMIK Pelita Nusantara alumni data as a basis for developing study program curricula that are more relevant to the needs of the world of work or industrial needs. Where the K-Means Clustering Method is used to group alumni based on similar characteristics they have, such as personal data, academic achievement, areas of expertise, and job information after graduating from college. The research data source used is graduate data for the 2021/2022 academic year. The data collection method was carried out by distributing questionnaires directly to alumni. The application of the k-means method is carried out by forming 2 groups (clusters), namely C1 = Liner and C2 = Not Linear. Data testing is also carried out using the rapid miner application. So that by grouping alumni data, it is hoped that tertiary institutions can identify the needs and preferences of alumni for the study programs followed so that they can develop study program curricula that are more targeted and in accordance with the needs of the job market.

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