Model Group Decision Support System Based on Depression Anxiety Stress Scales Using Ordered Weighted Averaging Aggregation Method
Abstract
Depression, anxiety, and stress are common psychological conditions often triggered by the pressures of daily life. Depression Anxiety Stress Scale (DASS), is a widely used tool for assessing the severity of these disorders, available in different versions such as the DASS-21 and DASS-42. In line with these findings, DASS-21 consists of 21 symptom items, categorized into three types of disorders, with seven items assigned to each. In contrast, the DASS-42 includes 42 symptom items, with 14 items allocated per disorder. Both versions serve as standardized tools for assessing the severity of depression, anxiety, and stress, and the different versions show that one item only affects one disorder. In practice, it can affect several disorders with different priorities. This condition increases the risk of subjective bias in a psychologist's decision-making, as personal experiences and perceptions may influence their assessments. Therefore, this study aims to develop a Group Decision Support System (GDSS) model that considers the preferences of several psychologists in determining the priority of disorders based on the DASS-42 and DASS-21 items. The model has been built using the psychologist's preference method for DASS-42 and DASS-21 in fuzzy form, then combined using the Ordered Weighted Averaging (OWA) method to produce one decision. The alignment of top-priority items between GDSS and DASS was assessed as part of the evaluation. The results show a high degree of similarity, with GDSS matching 16 out of 21 symptom items in DASS-21 and 35 out of 42 items in DASS-42. The GDSS model can accommodate the preferences of decision-makers in providing weighting of the influence on each item in the DASS-21 and DASS-42, thereby providing more objective decisions.
Downloads
References
S. Muhammad Khir et al., “Efficacy of progressive muscle relaxation in adults for stress, anxiety, and depression: A systematic review,” Psychol. Res. Behav. Manag., pp. 345-365, 2024, doi: 10.2147/PRBM.S437277.
F. Kaligis et al., “Mental health problems and needs among transitional-age youth in Indonesia,” Int. J. Environ. Res. Public Health, Vol. 18, No. 8, pp. 1-14, 2021, doi: 10.3390/ijerph18084046.
A. Anjum, S. Hossain, M. T. Hasan, E. Christopher, Md. E. Uddin, and Md. T. Sikder, “Stress symptoms and associated factors among adolescents in Dhaka, Bangladesh: Findings from a cross-sectional study,” BMC Psychiatry, Vol. 22, No. 1, pp. 1-11, 2022, doi: 10.1186/s12888-022-04340-0.
A. E. Wahdi, S. A. Wilopo, and H. E. Erskine, “The Prevalence of Adolescent Mental Disorders in Indonesia: An Analysis of Indonesia – National Mental Health Survey (I-NAMHS),” J. Adolesc. Heal., vol. 72, no. 3, p. S70, 2023, doi: 10.1016/j.jadohealth.2022.11.143.
S. Shorey, E.D.Ng, C.H.J. Wong, "Global prevalence of depression and elevated depressive symptoms among adolescents: A systematic review and meta-analysis," Br. J. Clin. Psychol., Vol. 61, No. 2, pp. 287–305, June. 2022, doi:10.1111/bjc.12333.
H. S. Lovibond and P. F. Lovibond, “The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories," Behav. Res. Ther., Vol. 33, No. 3, pp. 335-343, 1995, doi: 10.1016/0005-7967(94)00075-U
J. D. Henry, and J. R. Crawford, “The short-form version of the Depression Anxiety Stress Scales (DASS-21): Construct validity and normative data in a large non-clinical sample,” Br. J. Clin. Psychol, Vo. 44, No. 2, pp. 227-239, 2005, doi: 10.1348/014466505X29657.
M. Makara-Studzińska, E. Tyburski, M. Załuski, K. Adamczyk, J. Mesterhazy, and A. Mesterhazy, “Confirmatory Factor Analysis of Three Versions of the Depression Anxiety Stress Scale (DASS-42, DASS-21, and DASS-12) in Polish Adults,” Front. Psychiatry, vol. 12, pp. 1-9, January 2022, doi: 10.3389/fpsyt.2021.770532.
N. Syafitri, Y. Arta, A. Siswanto, and S. P. Rizki, “Expert System to Detect Early Depression in Adolescents using DASS 42,” Proceedings of the Second International Conference on Science, Engineering, and Technology, 2019, pp. 211–218, doi: 10.5220/0009158202110218.
A. Singh and D. Kumar, “Identification of Anxiety and Depression Using DASS-21 Questionnaire and Machine Learning,” in First International Conference on Advances in Computing and Future Communication Technologies (ICACFCT), Meerut, India, 2021, pp. 69-74.
S. Singh et al., "Comparative Analysis of Machine Learning Models to Predict Depression, Anxiety and Stress," in 11th International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, India, 2022, pp. 1-5.
S. Kusumadewi, and H. Wahyuningsih, “Group Decision Support System Model For Assessment Of Depression, Anxiety And Stress Disorders Based On DASS-42,” Jurnal Teknologi Informasi dan Ilmu Komputer, Vol. 7, No. 2, pp. 219-228, 2020, doi: 10.25126/jtiik.202071052.
S. Wan, J. Dong, and S. Chen, “An integrated method for shared power bank supplier selection based on linguistic hesitant fuzzy multi-criteria group decision making,” Knowledge-Based Syst., vol. 301 pp.1-25, 2024, doi: 10.1016/j.knosys.2024.112300.
P. Sugiartawan, I. M. Yudiana, and P. I. Prakoso, “Group Decision Support System Fuzzy Profile Matching Method With Organizational Citizenship Behaviour,” IJCCS (Indonesian J. Comput. Cybern. Syst.), Vol. 15, No. 4, 2021, doi: 10.22146/ijccs.70047.
P. Liu, R. Dang, P. Wang, and X. Wu, “Unit consensus cost-based approach for group decision-making with incomplete probabilistic linguistic preference relations,” Inf. Sci. (Ny)., vol. 624, pp. 849–880, 2023, doi: 10.1016/j.ins.2022.12.114.
S. M. Ghavami, J. Maleki, and T. Arentze, "A multi-agent assisted approach for spatial Group Decision Support Systems: A case study of disaster management practice," Int. J. Disaster Risk Reduct., vol. 38, June, p. 101223, 2019, doi: 10.1016/j.ijdrr.2019.101223..
A. P. Sahida, B. Surarso, and R. Gernowo, “The combination of the MOORA method and the Copeland Score method as a Group Decision Support System (GDSS) Vendor Selection,” International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2019, pp. 340–345, doi: 10.1109/ISRITI48646.2019.9034579.
M. Rabiee, B. Aslani, and J. Rezaei, "A decision support system for detecting and handling biased decision-makers in multi-criteria group decision-making problems," Expert Syst. Appl., Vol. 171, p. 114597, 2021, doi:10.1016/j.eswa.2021.114597.
P. Ziemba, M. Piwowarski, and K. Nermend,” Software systems supporting remote education – Fuzzy assessment using a multi-criteria group decision-making method,” Appl. Soft Comput. Vol. 149, p. 110971, 2023, doi: 10.1016/j.asoc.2023.110971.
S. M. Ghavami, M. Taleai, and T. Arentze, “An intelligent web-based spatial group decision support system to investigate the role of the opponents’ modeling in urban land use planning,” Land Use Policy, Vol. 120, p. 106256, 2022, doi: 10.1016/j.landusepol.2022.106256.
J. Xie, B. Wu, and W. Zou, “Ordered Weighted Utility Distance Operators and Their Applications in Group Decision-Making,” Appl. Soft Comput, Vol. 150, p. 111016, 2024, doi: 10.1016/j.asoc.2023.111016.
L. Jin, Z. Chen, R. R. Yager, T. Senapati, R. Mesiar, D. G. Zamora, B. Dutta, and L. Martínez, “Ordered weighted averaging operators for basic uncertain information granules,” Inf. Sci, Vol. 645, p. 119357, 2023, doi: 10.1016/j.ins.2023.119357.
R. Cheng, R. Zhu, Y. Tian, B. Kang, and J. Zhang, “A multi-criteria group decision-making method based on OWA aggregation operator and Z-numbers,” Soft Comput, Vol. 27, No. 3, pp. 1439–1455, 2023, doi: 10.1007/s00500-022-07667-8.
L. Jin, R. Mesiar, and R.R. Yager, “Ordered Weighted Averaging Aggregation on Convex Poset,” EEE Trans. Fuzzy Syst., Vol. 27. No. 3. pp. 612-617, 2019, doi:10.1109/tfuzz.2019.2893371.
I. Bueno, R. A. Carrasco, R. Ureña, and E. Herrera-Viedma, “Application of an opinion consensus aggregation model based on OWA operators to the recommendation of tourist sites,” Procedia Computer Science, 162, pp. 539–546, 2019 doi: 10.1016/j.procs.2019.12.021.
A. Thiyagarajan, T. G. James, and R. R. Marzo, “Psychometric properties of the 21-item Depression, Anxiety, and Stress Scale (DASS-21) among Malaysians during COVID-19: a methodological study,” Humanit. Soc. Sci. Commun., Vol. 9, p. 220, 2022, doi: 10.1057/s41599-022-01229-x.
S. R. Marsidi, “Identification Of Stress, Anxiety, And Depression Levels Of Students In Preparation For The Exit Exam Competency Test,” J. Vocat. Health Stud., Vol. 5, No. 2, pp. 87-93, 2021, doi: 10.20473/jvhs.V5.I2.2021.87-93.
S. H. Park et al., “Validation of the 21-item Depression, Anxiety, and Stress Scales (DASS-21) in individuals with autism spectrum disorder,” Psychiatry Res., Vol. 291, p. 113300, 2020, doi: 10.1016/j.psychres.2020.113300.
A. O. Coker, O. O. Coker, and D. Sanni, “Psychometric properties of the 21-item Depression Anxiety Stress Scale (DASS-21),” Afr. Res. Rev., Vol. 12, No. 2, pp. 135-142, April, 2018. doi: 10.4314/afrrev.v12i2.13.
S. Kusumadewi et al., “Fuzzy Multiattribute Decision Making (FMDAM),” Yogyakarta: Graha Ilmu, 2006.
F. Chiclana, E. Herrera-Viedma, F. Herrera, "Integrating Three Representation Models in Fuzzy Multipurpose Decision Making Based on Preference Relations," University of Granada, 1998.
R.R. Yager, “Quantifier Guided Aggregation Using OWA Operators,” Int. J. Intell. Syst., Vol. 11, pp. 49-73, 1996, doi: 10.1002/(SICI)1098-111X(199601)11:1%3C49::AID-INT3%3E3.0.CO;2-Z
F. Herrera and E. Herrera-Viedma, “Linguistic Decision Analysis: Steps for Solving Decision Problems under Linguistic Information,” Fuzzy Sets Syst., Vol. 115, pp, 67-82, 2000, doi: 10.1016/S0165-0114(99)00024-X.
Y. H. Sun, Q. Liu, N. Y. Lee, X. Li, and K. Lee, “A novel machine learning approach to shorten depression risk assessment for convenient uses,” J. Affect. Disord., vol. 312, pp. 275–291, Sep. 2022, doi: 10.1016/j.jad.2022.06.035.
H. Sastypratiwi, "Knowledge Base Development Framework with Fuzzy Preference Based on Group Decision Maker ." J. Comput. Soc., vol. 5, No. 1, pp. 15-26. 2024.
M. R. Hidayatullah and Warih Maharani, “Depression Detection on Twitter Social Media Using Decision Tree,” J. RESTI (Rekayasa Sist. Teknol. Inf.), vol. 6, no. 4, pp. 677–683, Aug. 2022, doi: 10.29207/resti.v6i4.4275.
I. Ramadhani and W. Maharani, “Predicting Depressive Disorder Based on DASS-42 on Twitter Using XLNet’s Pretrained Model Classification Text,” JoSYC, vol. 3, no. 4, pp. 379–385, Sep. 2022, doi: 10.47065/josyc.v3i4.2157.
J. Ghorpade-Aher, A. Memon, S. Chugh, A. Chebolu, P. Chaudhari, and J. Chavan, “DASS-21 Based Psychometric Prediction Using Advanced Machine Learning Techniques,” JAIT, vol. 14, no. 3, pp. 571–580, 2023, doi: 10.12720/jait.14.3.571-580.
A. Yesudas, "A Machine Learning Framework to Predict Depression, Anxiety and Stress." M.Sc. Research Project, Sch. Comput., Nat. Coll. of Ireland, Ireland. 2022.
Copyright (c) 2025 Wiharto, Della K. Putri, Sari W. Sihwi, umi Salamah, Esti Suryani, Vihi Atina, Pradityo utomo

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).