SISTEM PENDUKUNG KEPUTUSAN PERSONALISASI PEMBELAJARAN CODING MENGGUNAKAN METODE VIKOR (STUDI KASUS: STMIK KAPUTAMA KOTA BINJAI)

Authors

  • Toni Prabowo Sekolah Tinggi Manajemen Informatika dan Komputer Kaputama

Keywords:

VIKOR, Sistem Pendukung Keputusan, Personalisasi Pembelajaran Coding, Multi-Criteria Decision Making

Abstract

Proses pembelajaran coding di perguruan tinggi memerlukan pendekatan personalisasi yang objektif untuk menjaga efektivitasnya, mengingat karakteristik unik setiap mahasiswa. Selama ini, penentuan metode pembelajaran coding masih konvensional dan belum mempertimbangkan preferensi individu, serta belum ada sistem objektif untuk penilaian. Penelitian ini mengusulkan rancang bangun Sistem Pendukung Keputusan (SPK) berbasis metode VIKOR (VIšekriterijumsko KOmpromisno Rangiranje) untuk personalisasi pembelajaran coding. Sistem ini bertujuan mengevaluasi lima metode pembelajaran (Group Tutoring, Project-based Learning, Gamified Learning, Peer Instruction, dan Interactive Learning Modules) berdasarkan lima kriteria (Pemahaman Materi, Jalur Pembelajaran, Proses Refleksi, Kecepatan Pembelajaran, dan Pendampingan Belajar). Aplikasi SPK dikembangkan menggunakan metode Waterfall dan diuji dengan black-box testing pada 52 mahasiswa STMIK Kaputama. Hasil penelitian menunjukkan bahwa metode VIKOR berhasil meranking alternatif pembelajaran, dengan Gamified Learning sebagai metode paling direkomendasikan (28.85% preferensi). Diikuti oleh Interactive Learning Modules (26.92%), Group Tutoring (23.08%), Project-based Learning (13.46%), dan Peer Instruction (7.69%). Aplikasi SPK terbukti efektif memberikan rekomendasi personalisasi pembelajaran coding yang sesuai karakteristik mahasiswa, mendukung pengambilan keputusan yang lebih efisien.

Downloads

Download data is not yet available.

References

Bayly-Castaneda, K., Ramirez-Montoya, M.S., Morita-Alexander, A., 2024. Crafting personalized learning paths with AI for lifelong learning: a systematic literature review. Front. Educ. 9, 1–12. https://doi.org/10.3389/feduc.2024.1424386

Damayanti, T.A., Prabawa, H.W., Rahman, E.F., 2023. Web-Based Personalized Learning Media for Enhancing Cognitive Abilities of Vocational High School Students in Basic Programming Subject. J. Guru Komput. 3, 69–82. https://doi.org/10.17509/jgrkom.v3i2.31571

Hakim, N., Jastacia, B., Mansoori, A.A.-, 2024. Personalizing Learning Paths: A Study of Adaptive Learning Algorithms and Their Effects on Student Outcomes. J. Emerg. Technol. Educ. 2. https://doi.org/10.70177/jete.v2i4.1365

Handayani, D., Yusuf, D., Larasati, G.P., Ardhiyanto, O., 2024. Exemplary Teacher Selection Using a VIKOR-Based Decision Support System. PIKSEL Penelit. Ilmu Komput. Sist. Embed. Log. 12, 47–58. https://doi.org/10.33558/piksel.v12i1.8311

Ishaq, K., Alvi, A., 2023. Personalization, Cognition, and Gamification-based Programming Language Learning: A State-of-the-Art Systematic Literature Review.

Jacobs, S., Peters, H., Jaschke, S., Kiesler, N., 2025. Unlimited Practice Opportunities: Automated Generation of Comprehensive, Personalized Programming Tasks, Proceedings of Innovation and Technology in Computer Science Education (ITiCSE ’25). Association for Computing Machinery. https://doi.org/10.1145/3724363.3729089

Muharlisiani, L.T., Mulawarman, W.G., Rugaiyah, R., Azizah, S.N., Karuru, P., 2023. A Decision Support System for Personalized Learning in Higher Education. AL-ISHLAH J. Pendidik. 15, 5168–5175. https://doi.org/10.35445/alishlah.v15i4.4110

Mulyana, H.L., Rumaisa, F., 2024. Course Learning Recommendation System Using Neural Collaborative Filtering. Brill. Res. Artif. Intell. 4, 517–524. https://doi.org/10.47709/brilliance.v4i2.4699

Nawas, A., Kiswanto, R.H., 2024. Admission Selection Decision Support System New Students Use the Vikor Method. Int. J. Comput. Inf. Syst. 5, 78–83. https://doi.org/10.29040/ijcis.v5i2.165

Peng, H., Ma, S., Spector, J.M., 2019. Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learn. Environ. 6. https://doi.org/10.1186/s40561-019-0089-y

Saravanos, A., Curinga, M.X., 2023. Simulating the Software Development Lifecycle: The Waterfall Model. Appl. Syst. Innov. 6. https://doi.org/10.3390/asi6060108

Sembiring, F.B., Hasibuan, N.A., Purba, B., 2022. Implementasi Metode MOORA Dalam Sistem Pendukung Pemilihan Kepala Mandor Pada PT. PP Proyek Bendungan Lau Simeme. KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer) 6, 526–532. https://doi.org/10.30865/komik.v6i1.5772

Tang, X., Wong, S., Huynh, M., He, Z., Yang, Y., Chen, Y., 2025. SPHERE: Supporting Personalized Feedback at Scale in Programming Classrooms with Structured Review of Generative AI Outputs, Conference on Human Factors in Computing Systems - Proceedings . Association for Computing Machinery. https://doi.org/10.1145/3706599.3720203

Ulhaq, M.D.U., Irawati, 2021. Implementasi Metode Visekriterijumsko Kompromisno Rangiranje (VIKOR) Pada Seleksi Program Keluarga Harapan Komponen Pendidikan Berbasis Web. Indones. J. Data Sci. 2, 38–49. https://doi.org/10.33096/ijodas.v2i1.30

الاردن, غ.ص.ا.ا., 2021. No Titleقطاع الصناعات الكيماوية ومستحضرات التجميل 1, 1–6.

Downloads

Published

2025-08-09

How to Cite

Prabowo , T. (2025). SISTEM PENDUKUNG KEPUTUSAN PERSONALISASI PEMBELAJARAN CODING MENGGUNAKAN METODE VIKOR (STUDI KASUS: STMIK KAPUTAMA KOTA BINJAI). Global Research and Innovation Journal, 1(2), 2725–2734. Retrieved from https://journaledutech.com/index.php/great/article/view/496

Issue

Section

Articles

Similar Articles

<< < 9 10 11 12 13 14 15 16 17 18 > >> 

You may also start an advanced similarity search for this article.