Directorate General of Higher Education (Ditjen Dikti) of the Ministry of Education and Culture of the Republic of Indonesia again announced the clustering of Indonesian universities in 2020, Medan State University ranked 39th out of 2,136 universities. Ranked 2nd in 24th out of 34 universities with a total score of 2103. This announcement was delivered on Monday, (17/8).
The results of the clustering of universities by the Directorate General of Higher Education, in 2019, Unimed ranked 50th. From this result, Unimed’s ranking increased to 39 out of 2136 universities in Indonesia.
Rector of Unimed Dr. Syamsul Gultom, SKM, M.Kes. said Alhamdulilah and was grateful that Unimed’s rating rose from 2019 ago. Unimed is ranked 39th, and is ranked 2nd with 24th out of 34 colleges. This proud achievement is a love of work and performance with all unimed leaders and academic community. We will continue to improve our academic performance and services, so that next year we can enter cluster 1. Of course we will design the best program so that our expectations of achievement and ranking will increase in the next year. We also hope that this clustering can provide stimulation and motivation for Unimed and all universities to continuously improve their quality, achievement and academic services.
Director General of Higher Education, Prof. Nizam, explained that in 2020, in clustering, various information related to the performance of Indonesian universities were again identified based on four main aspects, including the quality of human resources and students (input), institutional management of universities (processes), short-term performance achievement achieved by universities (output), and long-term performance achievement of universities (outcome). However, the indicators that reflect each of the main components there are several changes / additions of indicators so that it is expected that the main components can better reflect the condition of Indonesian universities in accordance with the coverage in each of the main components.
In this clustering in 2020, indicators used to assess the performance of universities in input aspects include the percentage of S3-educated lecturers, the percentage of lecturers in the position of head elector and professors, the ratio of the number of lecturers to the number of students, the number of foreign students, and the number of lecturers working as practitioners in the industry at least 6 months.
In the aspect of the process there are 9 indicators used, among others Institutional Accreditation, Accreditation of Study Programs, Online Learning, Cooperation of universities, Completeness of PDDIKTI Report, Number of Study Programs in collaboration with DUDI, NGO or QS Top 100 WCU by subject, Number of Study Programs carrying out free learning programs, Number of students participating in the Merdeka Belajar Program.
In the output aspect, there are four indicators used, among others, the number of indexed scientific articles per lecturer, research performance, student performance, the number of study programs that have obtained International Accreditation or Certification. While in the outcome aspect, there are five indicators used, among others, innovation performance, the number of sitasi per lecturer, the number of patents per lecturer, the performance of community service, and the percentage of college graduates who get a job within 6 months.
Clustering of universities compiled and built in the framework of continuous improvement for both each college performance data and overall college performance. In accordance with this, clustering data sources use valid and ready-to-use data with the following characteristics: 1) Data that can be directly used, namely data that is routinely reported by universities to the Higher Education Database (PD Dikti); 2) Data of university performance assessment results that have been implemented by work units within the Directorate General of Higher Education but have not been presented in the PD Dikti; 3) Data that is not yet covered by the PD Dikti, but is collected in a structured manner by the work unit and is highly relevant to clustering; and 4) Data from outside PD Dikti that is relatively well established and ready to be used to measure college performance.
At the end of his presentation, Nizam hoped that the results of college clustering in 2020 could encourage universities in Indonesia to continue to make quality improvements continuously through smart work, spirit work and cooperation between universities. In addition, Nizam hopes that universities can orderly and routinely update data and report on the development of output achievements through the Higher Education Database (PD DIKTI) in accordance with the mandate of Law No. 12 of 2012 on Higher Education.
