The Study Program Selection System: Integrated Analytical Hierarchy Process (AHP) and Technique For Others Preference by Similarity to Ideal Solution (TOPSIS) Approach


  • Marni Erlina Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Okfalisa Okfalisa Universitas Islam Negeri Sultan Syarif Kasim Riau



Decision Support System, Study Program Selection, Analytical Hierarchy Process (AHP), Technique For Others Preference by Similarity to Ideal Solution (TOPSIS), Integrated AHP and TOPSIS


The study program selection in several high schools or madrasah aliyah (MA) in Riau province is conducted manually. The initial survey found that the study program is commonly chosen by following the friend’s preference and parents’ option instead of their knowledge capability and desire. As a reflection, many students fail to adhere to the school subjects, obtain unsatisfactory results, and even change their study program. Therefore, this paper aims to assist students in altering the appropriate study program by considering students' capabilities, talents and interests by developing a multi-criteria decision support system (DSS). This DSS employs the integrated AHP approach for criteria weighting and TOPSIS for ranking alternatives.  Herein, seventy students' data from grade ten MAN 2 Kuantan Singingi grows into this case study. The automation system analysis is executed through the web base DSS system using PHP programming language and MySQL database. As a result, AHP calculates the significance values of the criterion whereby the student interest score values at 0.34, academic report, potential academic test, physiological test, Pre-Test/Post-Test, interview scores, and teacher recommendation scores at 0.21, 0.14, 0.11, 0.09, 0.06, and 0.05, respectively. Subsequently, TOPSIS ranks the students according to the assessment and criteria weighting based on the standard requirement of study program in Mathematics and Science program (MIPA) and Social Science program (IIS). The DSS study program selection application has been tested using the Blackbox and User Acceptance Test (UAT) methods. Both of these approaches indicate that this application is functionally approved and capable of aiding the users in the optimal study program selection, with 81.9% agreement. In a nutshell, this DSS has successfully recommended the optimum study program per the students’ talents, interests, and capabilities and provided the direction development of students’ expertise area for the next level of education.


Download data is not yet available.


Suprianto, Guntur; Nurdyansyah, Nurdyansyah; Nyong, E. T. I. S. Analysis of Character Education in Curriculum 13 to Build Moral Awareness in Education at SMA Muhammadiyah 2 Sidoarjo. Proceedings of The ICECRS, 2020, 5.

Ridwan, Muhammad Rais, et al. The instrument development to measure the verbal ability of prospective high school students. Int J Eval & Res Educ, 2023, 12.1: 357-368.

Arifin, Nur; Saputro, Pujo Hari. Selection Index (PSI) Method in Developing a Student Scholarship Decision Support System. International Journal of Computer and Information System (IJCIS), 2022, 3.1: 12-16.

Giawa, Desi Kristina; Jannah, Miftahul. Decision Support System Of Provision Of Assistance To Unable Students In SMP Pab 6 Lubuk Pakam With Analytical Hierarchy Process (AHP) Method. DISTANCE: Journal of Data Science, Technology, and Computer Science, 2021, 1.1: 27-45.

Sudaryono, U. Rahardja; Rahardja, Untung; Masaeni. Decision Support System for Ranking of Students in Learning Management System (LMS) Activities using Analytical Hierarchy Process (AHP) Method. In: Journal of Physics: Conference Series. Institute of Physics Publishing, 2020.

Okfalisa, O, Hidayati, R., Dwi, U., B. Pranggono., Elin, H., Toto Saktioto. Decision Support System For Smartphone Recommendation: The Comparison Of Fuzzy AHP and Fuzzy ANP In Multi-Attribute Decision Making. Sinergi, 2020, 25.1: 101-110.

Okfalisa, W Anggraini, G Nawanir, S Saktioto, K Wong. Measuring the effects of different factors influencing on the readiness of SMEs towards digitalization: A multiple perspectives design of decision support system. Decision Science Letters, 2021, 10.3: 425-442.

Okfalisa, Okfalisa, et al. Integrated analytical hierarchy process and objective matrix in balanced scorecard dashboard model for performance measurement. Telekomnika (Telecommunication Computing Electronics and Control), 2018, 16.6: 2703-2711.

Putra, Dewa Gede Edi; Julianti, M. Ramaddan; Maesaroh, Siti. Decision Support System for the INAIMA AIS Officer of the Year Award using AHP-TOPSIS Method. Jurnal Sisfotek Global, 2023, 13.1: 52-59.

Kumar, Rahul; Singh, Kanwarpreet; Jain, Sanjiv Kumar. A combined AHP and TOPSIS approach for prioritizing the attributes for successful implementation of agile manufacturing. International Journal of Productivity and Performance Management, 2020.

Alazemi, Fahad Kh AOH, et al. A new fuzzy TOPSIS-based machine learning framework for minimizing completion time in supply chains. International Journal of Fuzzy Systems, 2022, 24.3: 1669-1695.

Putra, Dewa Gede Edi; Julianti, M. Ramaddan; Maesaroh, Siti. Decision Support System for the INAIMA AIS Officer of the Year Award using AHP-TOPSIS Method. Jurnal Sisfotek Global, 2023, 13.1: 52-59.

Mukhoriyah, Mukhoriyah, et al. Analysis of land use and spatial planning in the Upstream Citarum watershed of West Java based on remote sensing data. Journal of Degraded and Mining Lands Management, 2023, 10.3: 4315-4324..

Kutlu Gündoğdu, Fatma; Kahraman, Cengiz. A novel spherical fuzzy analytic hierarchy process and its renewable energy application. Soft Computing, 2020, 24: 4607-4621.

Zhang, Li-Jun, et al. Green supplier evaluation and selections: a state-of-the-art literature review of models, methods, and applications. Mathematical Problems in Engineering, 2020, 2020: 1-25.

Lin, Changsheng, et al. Aggregation of the nearest consistency matrices with the acceptable consensus in AHP-GDM. Annals of Operations Research, 2020, 1-17.

Han, Ke. Evaluation of teaching quality of college physical education based on analytic hierarchy process. International Journal of Emerging Technologies in Learning (iJET), 2020, 15.10: 86-99.

Arora, Anchal, et al. Identifying sustainability drivers in higher education through fuzzy AHP. Higher education, skills and work-based learning, 2020, 11.4: 823-836.

Muhammad, Abdulhafeez, et al. Factors affecting academic integrity in E-learning of Saudi Arabian Universities. An investigation using Delphi and AHP. Ieee Access, 2020, 8: 16259-16268.

Yuan, Mei; LI, Chunyang. Research on global higher education quality based on BP neural network and analytic hierarchy process. Journal of Computer and Communications, 2021, 9.6: 158-173.

Fu, Yi, et al. Information technology-based revolution in music education using AHP and TOPSIS. Soft Computing, 2022, 26.20: 10957-10970.

Dixit, Jitendra Kumar, et al. Competencies development for women edupreneurs community–an integrated AHP-TOPSIS approach. Journal of Enterprising Communities: People and Places in the Global Economy, 2021, 15.1: 5-25.

Rahman, Aulia Tegar, et al. The application of the analytical hierarchy process and simple additive weighting methods in making decisions for high achieving students in vocational high schools. JNANALOKA, 2021, 63-71.

Firgiawan, W.; Zulkarnaim, N.; Cokrowibowo, S. A Comparative Study using SAW, TOPSIS, SAW-AHP, and TOPSIS-AHP for Tuition Fee (UKT). In: IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2020. p. 012088.

Hajduk, Sławomira. Multi-criteria analysis in the decision-making approach for the linear ordering of urban transport based on TOPSIS technique. Energies, 2021, 15.1: 274.

Afrian, F., et al. Developing Winning Tender Recommendation System: Fuzzy Moora Approach. IT Journal Research and Development, 2023, 7.2: 228-241.

Looi, Chee-Kit, et al. Interest-Driven Creator Theory: case study of embodiment in an experimental school in Taiwan. Research and Practice in Technology Enhanced Learning, 2023, 18.

Gumantan, Aditya; Nugroho, Reza Adhi; Yuliandra, Rizki. Learning during the covid-19 pandemic: Analysis of e-learning on sports education students. Journal Sport Area, 2021, 6.1: 51-58.

Dewaele, Jean-Marc; Botes, Elouise; Meftah, Rachid. A Three-Body Problem: The effects of foreign language anxiety, enjoyment, and boredom on academic achievement. Annual Review of Applied Linguistics, 2023, 1-16.

Sellbom, Martin; Tellegen, Auke. Factor analysis in psychological assessment research: Common pitfalls and recommendations. Psychological assessment, 2019, 31.12: 1428.

Chau, Janita PC, et al. Development and evaluation of a technology-enhanced, enquiry-based learning program on managing neonatal extravasation injury: A pre-test/post-test mixed-methods study. Nurse Education Today, 2021, 97: 104672.

Jones, Carol. Qualitative interviewing. In: Handbook for research students in the social sciences. Routledge, 2020. p. 203-214.

Hunsaker, Scott (ed.). Identification: The theory and practice of identifying students for gifted and talented education services. Taylor & Francis, 2023.




How to Cite

Erlina, M., & Okfalisa, O. (2023). The Study Program Selection System: Integrated Analytical Hierarchy Process (AHP) and Technique For Others Preference by Similarity to Ideal Solution (TOPSIS) Approach. IT Journal Research and Development, 8(1), 32–47.




Most read articles by the same author(s)