Decision Support System Model for PPPK Teacher Selection Using the AHP Method
DOI:
https://doi.org/10.59976/jurit.v2i2.106Abstract
This study to develop a decision support system based on the Analytical Hierarchy Process (AHP) in the recruitment process of Government Employees with Work Agreements (PPPK) at SMP Negeri 1 Ukui. The background of this study is the need for a more objective, transparent, and accountable selection mechanism to reduce subjectivity in determining the passing grade for honorary teachers. The research method used a descriptive quantitative approach with data collection through observation, interviews, and paired comparison questionnaires administered to the principal, senior teachers, administrative staff, and school supervisors. The results of the analysis show that the Administration criterion received the highest weight (0.343), followed by Suitability Selection (0.535) and Interviews (0.122). In the subcriteria, the aspect of administrative document filing was the dominant factor (48%), while in suitability selection, teacher performance ranked highest (39%). Sensitivity analysis proved that the ranking results of teacher candidates were relatively stable even though the criteria weights changed. These findings confirm that AHP can minimize the subjectivity of selection and provide a quantitative basis for determining candidate priorities. This study has implications for improving the accountability of PPPK recruitment policies at the school level while opening up opportunities for the integration of hybrid methods such as AHP–TOPSIS in future research.
Downloads
References
Y. Yu, “Evaluating Teaching Quality in Colleges and Universities of Public Art Education Using the AHP Fuzzy Comprehensive Method,” Sci. Program., vol. 2022, 2022, doi: 10.1155/2022/3529311.
Z. Feng, “The impact of students’ lack of learning motivation and teachers’ teaching methods on innovation resistance in the context of big data,” Learn. Motiv., vol. 87, 2024, doi: 10.1016/j.lmot.2024.102020.
M. T. Mohammed, “Intelligent Approach for School Teacher Recruitment: Distributing IT Subjects Based on Multiple Attributes,” Appl. Model. Simul., vol. 7, pp. 100–110, 2023, [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85176762178&origin=inward
I. J. Navarro, “Evaluation of Higher Education Students’ Critical Thinking Skills on Sustainability,” Int. J. Eng. Educ., vol. 39, no. 3, pp. 592–603, 2023, doi: 10.13039/501100011033.
G. Li, “Achievement Prediction of English Majors Based on Analytic Hierarchy Process and Genetic Algorithm,” Mob. Inf. Syst., vol. 2022, 2022, doi: 10.1155/2022/6542300.
Y. Yu, “Construction and Application of the Talent Training System in Colleges and Universities Based on the Fuzzy Analytic Hierarchy Process,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/7295875.
K. P. Gupta, “Prioritising barriers of online teaching during COVID-19 from teachers’ perspective: using the analytic hierarchy process,” Int. J. Knowl. Learn., vol. 15, no. 3, pp. 203–232, 2022, doi: 10.1504/IJKL.2022.123956.
V. T. N. Tu, “Impact of Factors on Students’E-Learning Outcomes: Evidence from Pedagogical Universities in Vietnam with Applications in Decision Sciences,” Adv. Decis. Sci., vol. 27, no. 2, 2023, doi: 10.47654/v27y2023i2p28-45.
H. Yu, “Strategies of Improving Information Literacy of College Foreign Language Teachers Under the Background of Artificial Intelligence,” Int. J. Web Based Learn. Teach. Technol., vol. 19, no. 1, 2024, doi: 10.4018/IJWLTT.336853.
S. Yin, “Practical teaching method innovation decisions related to labor and reading at agricultural colleges based on entropy-fuzzy AHP combination weights,” Aims Math., vol. 8, no. 4, pp. 7641–7661, 2023, doi: 10.3934/math.2023383.
D. Wei, “Analysis on the Classification and Evaluation System of Talents in Colleges and Universities from the Perspective of AHP,” Mob. Inf. Syst., vol. 2022, 2022, doi: 10.1155/2022/6515974.
A. Parizad, “Designing a Model of Student Support in e-Learning Using Qualitative Content Analysis and Analytic Hierarchy Process,” Teh. Glas., vol. 17, no. 1, pp. 59–67, 2023, doi: 10.31803/tg-20220517223720.
H. Liang, “Process Evaluation for Diversified Academic Assessment Mechanism in Higher Education Institutions by Use of Data Mining,” Int. J. Emerg. Technol. Learn., vol. 18, no. 14, pp. 200–214, 2023, doi: 10.3991/ijet.v18i14.41921.
J. Liu, “Evaluation Model of Teaching Quality based on Algorithm of Stepwise Regression Analysis,” 2022. doi: 10.1109/ICISCET56785.2022.00014.
S. Z. Salas-Pilco, “Correction to: Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review (Education Sciences, (2022), 12, 8, (569), 10.3390/educsci12080569),” 2023. doi: 10.3390/educsci13090897.
Y. Wang, “Comprehensive Evaluation of Experimental Teaching Quality Using AHP-TOPSIS Technique,” Int. J. Emerg. Technol. Learn., vol. 18, no. 11, pp. 211–225, 2023, doi: 10.3991/ijet.v18i11.40185.
L. Bai, “Evaluating of Education Effects of Online Learning for Local University Students in China: A Case Study,” Sustain. Switz., vol. 15, no. 13, 2023, doi: 10.3390/su15139860.
J. H. Wang, “Study of learning climate, mobile game addiction, learning attitude, and learning motivation with teaching attraction as the moderator–evidence from higher education in Macau,” Cogent Educ., vol. 11, no. 1, 2024, doi: 10.1080/2331186X.2024.2353474.
D. Wu, “Multi-criteria evaluation of knowledge sharing level of university teachers based on improved AHP-CRITIC,” J. Comput. Methods Sci. Eng., vol. 24, no. 4, pp. 2503–2516, 2024, doi: 10.3233/JCM-247475.
H. Jia, “A study on evaluation of english hybrid teaching courses based on AHP and K-means,” Peerj Comput. Sci., vol. 10, pp. 1–14, 2024, doi: 10.7717/PEERJ-CS.2074.
S. Bortoluzzi-Balconi, “Teacher Actions: The Influence on Entrepreneurial Behavioral Characteristics of Students,” Rev. Electron. Educ., vol. 27, no. 1, 2023, doi: 10.15359/ree.27-1.14455.
H. Yue, “AHP Evaluation Algorithm based on Balanced Data Mining Algorithm for Kindergarten Teachers Equipped with Intelligent Hardware,” 2022. doi: 10.1109/ICECAA55415.2022.9936367.
S. Li, “Research on the construction of the evaluation index system of teacher morality in universities under the background of big data,” Appl. Math. Nonlinear Sci., vol. 9, no. 1, 2024, doi: 10.2478/amns.2023.2.00613.
J. Qu, “Higher Education Evaluation Model Based on AHP Algorithm,” 2022. doi: 10.1117/12.2638701.
K. Zhao, “A New Method to Evaluate the Engineering Practice Ability of Young University Teachers,” 2022. doi: 10.1145/3572549.3572628.
Ashish, “DNA Sequence based Advanced Technique for Prediction of Hereditary Diseases using Support Vector Machine (SVM),” 2023. doi: 10.1109/ICICT57646.2023.10133969.
H. Flor-Cunza, “Model for the Selection of Virtual Educational Tools in University Leveling Cycles in Engineering Using AHP,” 2024. doi: 10.1109/EDUNINE60625.2024.10500660.
C. Li, “A New Inquiry into the Ideological and Political Education of College Students in the Context of Free Trade Port,” Appl. Math. Nonlinear Sci., vol. 9, no. 1, 2024, doi: 10.2478/amns.2023.1.00245.
D. Gong, “Research on the enhancement of informatization literacy of design practice teaching faculty in applied colleges and universities based on OBE model in the context of artificial intelligence,” Appl. Math. Nonlinear Sci., vol. 9, no. 1, 2024, doi: 10.2478/amns-2024-0971.
Z. Guo, “Design and Application of Swarm Intelligence Algorithm in Mathematics Teaching Quality Evaluation System in Higher Vocational Colleges,” 2023. doi: 10.1109/EKI61071.2023.00009.
C. Tang, “Evaluation of entrepreneurship failure education in higher education from the perspective of the CIPP model and AHP-FCE methods,” Aims Math., vol. 9, no. 8, pp. 20641–20661, 2024, doi: 10.3934/math.20241003.
X. Guo, “Teaching Quality Evaluation and Analysis System Based on AHP-PSO-BP Neural Network,” 2022. doi: 10.1109/ICOT56925.2022.10008161.
X. Bi, “Advanced Mathematics’ Quality Evaluation Model Based on Grey Correlation Analysis,” 2022. doi: 10.1145/3568739.3568788.
Z. Liang, “The Performance Evaluation of IEE in Colleges and Universities Based on AHP Model,” Discret. Dyn. Nat. Soc., vol. 2022, 2022, doi: 10.1155/2022/4616501.
X. Zhu, “Evaluation of Stakeholders ’ Influence on Financial Risks of Non-profit Private Universities in China,” 2022. doi: 10.1145/3564858.3564882.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Adinda Fadilah Hamdi, Refliana Refliana
This work is licensed under a Creative Commons Attribution 4.0 International License.
