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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas) Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Computing, Engineering, and Design Journal of Applied Data Sciences Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat Journal of Community Services: Sustainability and Empowerment CSRID
Teddy Mantoro
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Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Journal : Journal of Applied Data Sciences

 1 
Improving MCDM University Rankings through Statistical Validation Using Spearman’s Correlation and THE Benchmark Andryana, Septi; Mantoro, Teddy; Gunaryati, Aris; Raffliansyah, Alfarizky Esah
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.796

Abstract

The evaluation of higher education institutions is a critical field for informing data-driven policy and institutional benchmarking. A key problem in this area is the lack of transparency and consistency in university rankings, particularly when using Multi-Criteria Decision-Making (MCDM) methods such as MABAC and MAIRCA, with limited research on how weighting techniques affect the reliability and alignment of these rankings with international standards like the Times Higher Education (THE) Rankings. This study proposes the use of MABAC and MAIRCA methods combined with two weighting techniques—Rank Order Centroid (ROC) and Rank Sum (RS)—to assess 20 top Indonesian universities based on five performance indicators: research quality, research environment, teaching, industry, and international outlook. Spearman’s rank correlation is used to compare the MCDM-generated rankings with THE Rankings 2025. The study contributes empirical evidence on the impact of weighting schemes on the consistency and reliability of university rankings and demonstrates that the MAIRCA-ROC method achieves the highest agreement with THE Rankings, with a correlation coefficient of 0.8135 and a p-value of 0.00001. These results validate the use of MCDM methods in higher education evaluation and emphasize the importance of selecting appropriate weighting techniques to develop transparent and robust ranking frameworks that support evidence-based policy decisions.
Improving University Ranking Robustness Using Rank Geometric Weight Integration with CoCoSo Method for Reducing Ordinal Weighting Instability Andryana, Septi; Mantoro, Teddy; Mutiara, Achmad Benny; Ernastuti, Ernastuti; Prihandoko, Prihandoko; Gunaryati, Aris
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1024

Abstract

This study lies in the field of decision support systems, focusing on the application of Multi-Criteria Decision Making (MCDM) for ranking alternatives based on predefined organizational criteria. A persistent challenge in this domain is the instability and subjectivity of ordinal weighting methods - such as Rank Order Centroid (ROC), Rank Sum (RS), Rank Reciprocal (RR), and Rank Order Distribution (ROD), which derive weights solely from rank positions, often leading to inconsistent and unreliable outcomes. To address this, this study introduces Rank Geometric (RG) weights, a geometric mean aggregation of ROC, RS, RR, and ROD designed to reduce subjectivity, stabilize weight distribution, and enhance robustness. By using the Combined Compromise Solution (CoCoSo) method, the RG against Times Higher Education’s (THE) official weights were evaluated, and the four individual ordinal methods, applied to the top 10 Indonesian universities across five THE 2025 ranking criteria. Empirical results show that RG-CoCoSo produces stronger and more consistent correlations with THE’s rankings than THE-CoCoSo, as validated by Spearman and Pearson correlation tests, with a p-value of 0.0251. This study contributes a practical, data-driven weighting framework that strengthens the reliability of MCDM-based institutional performance evaluation and can be generalized to other ranking contexts.
Co-Authors Abdul Rahman Wijaya Putra Abdullah Alkalbani Achmad Benny Mutiara Achmad Farid Wadjdi Adamu Abubakar Ibrahim Adi Putra Andriyandi Agung Nugroho Akeem Olowolayemo Alam, Sirojul Alfian Dody Andri Zakariya Andri Zakariya Anggy Pradiftha Anggy Pradiftha Junfithrana Aris Gunaryati Aris Gunaryati, Aris Arisantoso Arisantoso Ben Rahman Benrahman Dholah Rosita, Moneyta Dian Sa’adillah Maylawati Dian Sa’adillah Maylawati Dimas Erlangga Edi Irawan Effendi, Nicholas Lasca Ernastuti Ernastuti Hamdan Sugilar Haris Al Qodri Maarif Haris Al Qodri Maarif I Made Alan Priyatna Ibrahim, Adamu Abubakar Jelita Asean Jelita Asian Lumbia, Mega Lutfil Khakim Media A. Ayu Media Anugerah Ayu, Media Anugerah Mega Lumbia Mohammad Iwan Wahyuddin Moneyta Dholah Rosita Muchlis Nurseno Muhamad Rausyan Fikri Muhammad Agni Catur Bhakti Muhammad Ali Ramdhani Muhammad Anton Permana Nugroho, Agung Prihandoko Prihandoko Putra, Abdul Rahman Wijaya Raffliansyah, Alfarizky Esah Rahman, Ben Rimbawa, H.A Danang Saputro, Joko W. Septi Andryana Septya Egho Pratama Sony Surya Wijaya Tarigan, William Hazkel Umar Aditiawarman Wahyuddin, Mohammad Iwan Wahyudin Darmalaksana Wandy Wandy