Seleksi Beasiswa Menggunakan Analytical Hierarchy Process dan Penilaian Rubrik dengan Explainability Serta Audit Fairness

Penulis

  • Amanda Listiana Puspanagara* Universitas Sebelas April, Sumedang, Indonesia
  • Fathoni Mahardika Universitas Sebelas April, Sumedang, Indonesia
  • Dani Indra Junaedi Universitas Sebelas April, Sumedang, Indonesia
  • Asep Saeppani Universitas Sebelas April, Sumedang, Indonesia

DOI:

https://doi.org/10.71456/jimt.v2i2.1588

Kata Kunci:

Analytical Hierarchy Process, Sistem Pendukung Keputusan, Rubric Scoring, Explainability, Fairness Auditing

Abstrak

Seleksi beasiswa di perguruan tinggi sering terdistorsi oleh subjektivitas, penerapan kriteria yang tidak konsisten, dan rendahnya transparansi ketika penilaian dilakukan manual atau berbasis spreadsheet. Penelitian ini mengembangkan dan memvalidasi sistem pendukung keputusan yang memadukan Analytical Hierarchy Process untuk pembobotan kriteria dengan penilaian berbasis rubrik untuk menilai pelamar, disertai fitur explainability dan fairness auditing. Sistem menampilkan kontribusi skor per kriteria, menghasilkan justifikasi naratif deterministik yang diturunkan dari level rubrik dan bobot, serta merangkum dampak kelompok melalui dashboard audit. Artefak dirancang, diimplementasikan, dan dievaluasi menggunakan Design Science Research Methodology pada uji coba sandbox menggunakan 100 pelamar sintetis dalam tiga konfigurasi kebijakan. Pada kebijakan dasar, skor ternormalisasi berada pada rentang 0.48-0.93 dengan rata-rata 0.774, dan pelamar terkelompok menjadi 64 direkomendasikan, 34 borderline, dan 2 tidak direkomendasikan. Dibandingkan dengan keputusan referensi pakar, sistem menunjukkan kesepakatan tinggi (akurasi 0.96; presisi 0.953; recall 0.983; F1-score 0.96) serta keselarasan peringkat kuat (Spearman 0.97; Kendall 0.86). Audit keadilan berdasarkan kelompok semester mengindikasikan disparitas pada kebijakan dasar, dengan tingkat direkomendasikan 0% untuk semester 1-2, 52% untuk semester 3-4, dan 96% untuk semester 5-8. Hasil ini menunjukkan bahwa pendekatan hibrida meningkatkan konsistensi dan transparansi penilaian sekaligus menyediakan bukti untuk meninjau pertukaran antara prestasi, kebutuhan, dan dampak kelompok, dengan keputusan akhir tetap berada pada komite.

Biografi Penulis

Amanda Listiana Puspanagara*, Universitas Sebelas April, Sumedang, Indonesia

Program Studi Informatika, Fakultas Teknologi Informasi

Fathoni Mahardika, Universitas Sebelas April, Sumedang, Indonesia

Program Studi Informatika, Fakultas Teknologi Informasi

Dani Indra Junaedi, Universitas Sebelas April, Sumedang, Indonesia

Program Studi Informatika, Fakultas Teknologi Informasi

Asep Saeppani, Universitas Sebelas April, Sumedang, Indonesia

Program Studi Informatika, Fakultas Teknologi Informasi

Referensi

Al-Habaibeh, A., Christersson, E., Hossain, M. A., & Desouza, N. (2021). Human-centered design science research evaluation for AR games. Frontiers in Virtual Reality, 2, 713718. https://doi.org/10.3389/frvir.2021.713718

Alves, G., Bernier, F., Couceiro, M., Makhlouf, K., Palamidessi, C., & Zhioua, S. (2023). Survey on fairness notions and related tensions. EURO Journal on Decision Processes, 11, 100033. https://doi.org/10.1016/j.ejdp.2023.100033

Anggrawan, A., Mayadi, M., Satria, C., & Putra, L. G. R. (2022). Scholarship recipients recommendation system using AHP and MOORA methods. International Journal of Intelligent Engineering and Systems, 15(2), 260–275. https://doi.org/10.22266/ijies2022.0430.24

Brown, S., Davidovic, J., & Hasan, A. (2021). The algorithm audit: Scoring the algorithms that score us. Big Data & Society, 8(1), 1–15. https://doi.org/10.1177/2053951720983865

Chen, W. (2025). FairDgcl: fairness-aware recommendation with dynamic graph contrastive learning. IEEE Transactions on Knowledge and Data Engineering, 37(9), 5230–5242. https://doi.org/10.1109/TKDE.2025.3580087

Deck, L., Schoeffer, J., De-Arteaga, M., & Kühl, N. (2024). A Critical Survey on Fairness Benefits of Explainable AI. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24), 1579–1595. https://doi.org/10.1145/3630106.3658990

DeLuca, C. (2025). Diversity: a necessary imperative for assessment research. Assessment in Education: Principles, Policy and Practice, 32(3), 253–256. https://doi.org/10.1080/0969594X.2025.2549158

Dethier, A., Delcourt, C., & Dessart, L. (2024). Donor perceptions of nonprofit organizations’ transparency: Conceptualization and operationalization. Nonprofit and Voluntary Sector Quarterly, 53(5), 1230–1260. https://doi.org/10.1177/08997640231211212

English, N., Robertson, P., Gillis, S., & Graham, L. (2022). Rubrics and formative assessment in K-12 education: A scoping review of literature. International Journal of Educational Research, 113, 101964. https://doi.org/10.1016/j.ijer.2022.101964

Garcia, T., Tenorio, R., Garcia, P., Arquero, J. F., & Diaz, E. (2023). Effects of rubrics on academic performance, self-regulated learning, and self-efficacy: A meta-analysis. Educational Psychology Review, 35, 113. https://doi.org/10.1007/s10648-023-09823-4

Ghoorah, U., Mariyani-Squire, E., & Amin, S. Z. (2025). Relationships between financial transparency, trust, and performance: an examination of donors’ perceptions. Humanities and Social Sciences Communications, 12, 315. https://doi.org/10.1057/s41599-025-04640-2

Jannach, D., Lerche, L., Kovalerchuk, I., Zanker, M., Kuschnig, P., & Jerhot, P. (2024). Fairness in recommender systems: Research landscape and future directions. User Modeling and User-Adapted Interaction, 34, 59–108. https://doi.org/10.1007/s11257-023-09364-z

Jin, D., Wang, L., Zhang, H., Zheng, Y., Ding, W., Xia, F., & Pan, S. (2023). A Survey on Fairness-aware Recommender Systems. Information Fusion, 100, 101906. https://doi.org/10.1016/j.inffus.2023.101906

Jonsson, A., Panadero, E., Pinedo, L., & Fernandez-Castilla, B. (2025). Using rubrics for formative purposes: identifying factors that may affect the success of rubric implementations. Assessment in Education: Principles, Policy and Practice, 32(2), 192–211. https://doi.org/10.1080/0969594X.2025.2486947

Kirat, Th., Tambou, O., Do, V., & Tsoukiàs, A. (2023). Fairness and explainability in automatic decision-making systems. A challenge for computer science and law. EURO Journal on Decision Processes, 11, 100036. https://doi.org/10.1016/j.ejdp.2023.100036

Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J. Del, Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., Jiang, R., Khosravi, H., Lecue, F., Malgieri, G., Páez, A., Samek, W., Schneider, J., Speith, T., & Stumpf, S. (2024). Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions. Information Fusion, 106, 102301. https://doi.org/10.1016/j.inffus.2024.102301

Madzik, P., & Falat, L. (2022). State-of-the-art on analytic hierarchy process in the last 40 years: Literature review based on Latent Dirichlet Allocation topic modelling. PLOS ONE, 17(5), e0268777. https://doi.org/10.1371/journal.pone.0268777

Matejova, M., & Paralic, J. (2025). A multi-criteria decision-making approach for the selection of explainable AI methods. Machine Learning and Knowledge Extraction, 7(4), 158. https://doi.org/10.3390/make7040158

Mulyaningsih, T., Dong, S. X., Miranti, R., Daly, A., & Purwaningsih, Y. (2022). Targeted scholarship for higher education and academic performance: Evidence from Indonesia. International Journal of Educational Development, 88, 102510. https://doi.org/10.1016/j.ijedudev.2021.102510

Murchan, D., Shaw, S., & Likhovtseva, E. (2025). Policy and practice in relation to external moderation of school-based assessment in 13 examination systems internationally. Assessment in Education: Principles, Policy and Practice. https://doi.org/10.1080/0969594X.2025.2562814

Naghiaei, M., Kamishima, T., Kameyada, H., & Sakuma, J. (2021). Fairness metrics and bias mitigation strategies for rating predictions. Information Processing & Management, 58(5), 102645. https://doi.org/10.1016/j.ipm.2021.102646

Pitoura, G., Siachamis, K., Stefanidis, K., & Tsaparas, P. (2022). Fairness in rankings and recommendations: An overview. The VLDB Journal, 31, 431–454. https://doi.org/10.1007/s00778-021-00697-y

Salomon, V. A. P., & Gomes, L. F. A. M. (2024). Consistency Improvement in the Analytic Hierarchy Process. Mathematics, 12(6), 828. https://doi.org/10.3390/math12060828

Shi, C., & Yao, Y. (2025). Explainable multi-criteria decision-making: A three-way decision perspective. International Journal of Approximate Reasoning, 187, 109528. https://doi.org/10.1016/j.ijar.2025.109528

Syverud, M. S. (2025). Oral exams in four Norwegian secondary schools - characteristics and variations in practice and possible threats to validity and fairness. Assessment in Education: Principles, Policy and Practice. https://doi.org/10.1080/0969594X.2025.2563722

Tanaka, M. (2025). Friendship bias in peer assessment of EFL oral presentations. Assessment in Education: Principles, Policy and Practice, 1–23. https://doi.org/10.1080/0969594X.2025.2570248

Teh, L. J. (2025). Exploring postgraduate students’ experience with rubric-referenced assessment: limitations and solutions. Pertanika Journal of Social Sciences and Humanities, 33(2), 541–562. https://doi.org/10.47836/pjssh.33.2.03

van der Veer, S. N., Dowrick, C. J., Genao, M. G. M., O’Connor, M. J., & Teodorczuk, A. (2021). Trading off accuracy and explainability in AI decision-making: Findings from two citizens’ juries. Journal of the American Medical Informatics Association, 28(10), 2128–2138. https://doi.org/10.1093/jamia/ocab118

Unduhan

Diterbitkan

2026-01-12

Cara Mengutip

Puspanagara, A. L., Mahardika, F., Junaedi, D. I., & Saeppani, A. (2026). Seleksi Beasiswa Menggunakan Analytical Hierarchy Process dan Penilaian Rubrik dengan Explainability Serta Audit Fairness. Jurnal Informatika, Multimedia Dan Teknik, 2(2), 92–99. https://doi.org/10.71456/jimt.v2i2.1588