slot gacor slot gacor hari ini slot gacor 2025 demo slot pg slot gacor slot gacor
METODE ENSEMBLE ROBUST CLUSTERING USING LINKS (ROCK) UNTUK PENGELOMPOKAN PERGURUAN TINGGI SWASTA (PTS) DI KOTA SEMARANG | Jannah | Jurnal Gaussian skip to main content

METODE ENSEMBLE ROBUST CLUSTERING USING LINKS (ROCK) UNTUK PENGELOMPOKAN PERGURUAN TINGGI SWASTA (PTS) DI KOTA SEMARANG

*Berliana Jannah  -  Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia
Iut Tri Utami  -  , Indonesia
Arief Rachman Hakim  -  , Indonesia
Open Access Copyright 2023 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
The purpose of this research is to group PTS that have performance achievements in five years, through the quality of Human Resources and Students (Input), the quality of Institutional Management (process), the quality of Short-Term Performance Achievements (Output) and the quality of Long-Term Performance Achievements (Outcome). In addition, it can also be seen from the form of PTS, PTS Accreditation and PTS Research Performance. This PTS grouping uses mixed data, namely numerical data and categorical data. The method used for grouping mixed data is the ROCK ensemble method (Robust Clustering Using Links). The results of clustering numerical data obtained the optimum number of groups 3, on categorical data obtained the optimum group 4. After clustering each type of data and merging and clustering obtained the optimum group 3 with a threshold (θ) is 0.2. The results of each group are: low quality consist of 29 PTS, medium quality consist of 7 PTS, and high quality there is 1 PTS. The results of this research can be used to cluster private universities in Semarang City, so that it can be used as a reference for prospective students in choosing private universities in Semarang, and can be referenced to the Central Java LLDIKTI in determining the quality of private universities in Semarang City.

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Instrument
Untitled
Subject
Type Research Instrument
  Download (124KB)    Indexing metadata
Keywords: ROCK ensemble; PTS quality; mixed data
Funding: Universitas Diponegoro under contract MetMu123456

Article Metrics:

  1. Alvionita. 2017. Metode Ensemble ROCK dan SFWM untuk Pengelompokkan Data Campuran Numerik dan Kategorik pada Kasus Aksesi Jeruk. Surabaya: Institut Teknologi Sepuluh Nopember
  2. Bunkers, M. J., & James, R. M. 1996. Definition of Climate Region in The Northern Plains Using an Objective Cluster Modification Technique. Journal of Climate, 130-146
  3. Dutta, M., Mahanta, A. K., & Pujari, A. K. 2005. QROCK: A Quick of the ROCK Algorithm for Clustering of Categorical Data. Proceedings of the 15 IEEE International Conference on Data Engineering
  4. Guha, S., Rastogi, R., & Shim, K. 2000. ROCK: A Robust Clustering Algorithm for Categorical Attributes. Proceedings of the 15th International Conference on Data Engineering
  5. Hair, J. F., Black, W. C., Babin, J. B., & Anderson, E. R. 2001. Multivariate Data Analysis (Seventh ed.). New Jersey: Prentice Hall Inc
  6. He, Z., Xu, X. i., & Deng, S. 2005. Clustering Mixed Numeric and Categorical Data: A Cluster Ensemble Approach. Departement of Computer Science and Engineering Harbin Institute of Technology
  7. Pemerintah Republik Indonesia. 2012. Undang-Undang Republik Indonesia Nomor 12 Tahun 2012 Tentang Pendidikan Tinggi. Jakarta

Last update:

No citation recorded.

Last update:

No citation recorded.