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Spatial-temporal analysis using two-stage clustering and GIS-based MCDM to identify potential market regions

Promotion is essential in a competitive environment. Promotion to the right areas increases success and saves resources. However, due to Indonesia's vast territory and numerous regions of origin school, universities with student markets from all over the country must select target areas for pro...

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Main Authors: Ernawati, Kamal Baharin, Safiza Suhana, Kasmin, Fauziah
Format: Article
Language:English
Published: Success Culture Press 2021
Online Access:http://eprints.utem.edu.my/id/eprint/26607/2/SPATIAL-TEMPORAL%20ANALYSIS%20USING%20TWO-STAGE.PDF
http://eprints.utem.edu.my/id/eprint/26607/
http://www.aasmr.org/jsms/Vol11/vol.11.4.5.pdf
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spelling my.utem.eprints.266072023-07-20T13:09:16Z http://eprints.utem.edu.my/id/eprint/26607/ Spatial-temporal analysis using two-stage clustering and GIS-based MCDM to identify potential market regions Ernawati Kamal Baharin, Safiza Suhana Kasmin, Fauziah Promotion is essential in a competitive environment. Promotion to the right areas increases success and saves resources. However, due to Indonesia's vast territory and numerous regions of origin school, universities with student markets from all over the country must select target areas for promotion to meet their objectives and save resources. Unlike for-profit businesses, besides quantity factors, educational institutions need to consider student quality factors in selecting promotional locations. This study aims to conduct a data-driven spatio-temporal analysis to identify potential regions for university promotions targets. This study uses enrollment and academic data from one private university in Indonesia for the empirical study. In Geographic Information System (GIS) environment, the origin schools' locations were geocoded, and various thematic maps were analyzed. This study integrates two-stage clustering and GIS-based multi-criteria decision-making (MCDM) to identify potential market regions. A potential region is one that consistently sends many qualified students. First, time-series clustering is conducted to groups regencies/cities based on the enrolled students' patterns over time in the university. Subsequently, the origin schools' regencies/cities were clustered using the k- prototypes algorithm based on their time-series pattern category, the consistency in sending students, average cumulative grade point average (CGPA), and dropout (DO) rate. The clusters are scored using the sum weighting method. The highest valued cluster that consists of eight regencies and 18 cities that consistently contributed high quantity and quality students were selected as the priority regions. The proposed approach's results were compared to the Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods for evaluation. The proposed method can assist the university management in determining potential regions for promotion purposes. Success Culture Press 2021 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26607/2/SPATIAL-TEMPORAL%20ANALYSIS%20USING%20TWO-STAGE.PDF Ernawati and Kamal Baharin, Safiza Suhana and Kasmin, Fauziah (2021) Spatial-temporal analysis using two-stage clustering and GIS-based MCDM to identify potential market regions. Journal of System and Management Sciences, 11 (4). pp. 87-112. ISSN 1816-6075 http://www.aasmr.org/jsms/Vol11/vol.11.4.5.pdf 10.33168/JSMS.2021.0405
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Promotion is essential in a competitive environment. Promotion to the right areas increases success and saves resources. However, due to Indonesia's vast territory and numerous regions of origin school, universities with student markets from all over the country must select target areas for promotion to meet their objectives and save resources. Unlike for-profit businesses, besides quantity factors, educational institutions need to consider student quality factors in selecting promotional locations. This study aims to conduct a data-driven spatio-temporal analysis to identify potential regions for university promotions targets. This study uses enrollment and academic data from one private university in Indonesia for the empirical study. In Geographic Information System (GIS) environment, the origin schools' locations were geocoded, and various thematic maps were analyzed. This study integrates two-stage clustering and GIS-based multi-criteria decision-making (MCDM) to identify potential market regions. A potential region is one that consistently sends many qualified students. First, time-series clustering is conducted to groups regencies/cities based on the enrolled students' patterns over time in the university. Subsequently, the origin schools' regencies/cities were clustered using the k- prototypes algorithm based on their time-series pattern category, the consistency in sending students, average cumulative grade point average (CGPA), and dropout (DO) rate. The clusters are scored using the sum weighting method. The highest valued cluster that consists of eight regencies and 18 cities that consistently contributed high quantity and quality students were selected as the priority regions. The proposed approach's results were compared to the Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods for evaluation. The proposed method can assist the university management in determining potential regions for promotion purposes.
format Article
author Ernawati
Kamal Baharin, Safiza Suhana
Kasmin, Fauziah
spellingShingle Ernawati
Kamal Baharin, Safiza Suhana
Kasmin, Fauziah
Spatial-temporal analysis using two-stage clustering and GIS-based MCDM to identify potential market regions
author_facet Ernawati
Kamal Baharin, Safiza Suhana
Kasmin, Fauziah
author_sort Ernawati
title Spatial-temporal analysis using two-stage clustering and GIS-based MCDM to identify potential market regions
title_short Spatial-temporal analysis using two-stage clustering and GIS-based MCDM to identify potential market regions
title_full Spatial-temporal analysis using two-stage clustering and GIS-based MCDM to identify potential market regions
title_fullStr Spatial-temporal analysis using two-stage clustering and GIS-based MCDM to identify potential market regions
title_full_unstemmed Spatial-temporal analysis using two-stage clustering and GIS-based MCDM to identify potential market regions
title_sort spatial-temporal analysis using two-stage clustering and gis-based mcdm to identify potential market regions
publisher Success Culture Press
publishDate 2021
url http://eprints.utem.edu.my/id/eprint/26607/2/SPATIAL-TEMPORAL%20ANALYSIS%20USING%20TWO-STAGE.PDF
http://eprints.utem.edu.my/id/eprint/26607/
http://www.aasmr.org/jsms/Vol11/vol.11.4.5.pdf
_version_ 1772816024937168896
score 13.211869

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