Sistem Pendukung Keputusan Pemilihan Program Studi Berbasis Naive Bayes dengan Likelihood Pakar dan Kuesioner Minat

Ahmad Latif; Muhtyas Yugi; Nurul Alfiah; Beny Riswanto

doi:10.56211/sudo.v5i3.1939

Authors

Ahmad Latif Universitas Komputama
Muhtyas Yugi Universitas Komputama
Nurul Alfiah Universitas Komputama
Beny Riswanto Universitas Komputama

DOI:

https://doi.org/10.56211/sudo.v5i3.1939

Keywords:

Likelihood Pakar; Naive Bayes; Pemilihan Program Studi ; Rekomendasi Pendidikan; Sistem Pendukung Keputusan

Abstract

Pemilihan program studi merupakan keputusan pendidikan berisiko tinggi karena ketidaksesuaian antara minat dan karakteristik program dapat memengaruhi keterlibatan, keberhasilan, dan keberlanjutan studi. Penelitian ini merancang dan mengimplementasikan sistem pendukung keputusan berbasis web yang memeringkat lima program studi di Universitas Komputama Cilacap menggunakan Naive Bayes. Model menggunakan 30 indikator minat dengan respons rendah, sedang, dan tinggi. Matriks likelihood dibentuk melalui elicitation terhadap tiga pakar akademik. Ketiga pakar mendiskusikan 150 kombinasi indikator-program studi dan menetapkan secara konsensus tiga probabilitas bersyarat untuk respons rendah, sedang, dan tinggi pada setiap kombinasi, dengan jumlah probabilitas sebesar 1,00. Nilai konsensus digunakan langsung sebagai likelihood dalam model Naive Bayes. Instrumen kuesioner belum melalui validasi psikometrik formal pada tahap pilot. Pengembangan mengikuti Waterfall, sedangkan evaluasi saat ini terbatas pada keterlacakan fungsi utama, analisis deskriptif enam rekam konsultasi pada 25 Mei-9 Juli 2026, serta satu laporan kasus terperinci; validasi prediktif belum dilakukan karena label kebenaran belum tersedia. Pada data pilot, Sistem Informasi menjadi rekomendasi utama terbanyak (50,0%). Rata-rata skor rekomendasi utama adalah 75,07% dengan rentang 42,32%-99,09%, dan rata-rata margin terhadap rekomendasi kedua sebesar 55,83 poin persentase. Laporan kasus menghasilkan Teknik Informatika sebagai rekomendasi utama dengan posterior 89,72%. Kontribusi penelitian adalah kerangka probabilistik tahap awal yang dapat beroperasi ketika dataset berlabel belum memadai, menghasilkan peringkat dan margin yang transparan, serta menyediakan jejak konsultasi untuk kalibrasi empiris berikutnya. Skor posterior tidak ditafsirkan sebagai akurasi klasifikasi, sehingga sistem diposisikan sebagai alat bantu konseling, bukan pengganti keputusan akademik manusia.

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References

[1] N. de Vries, M. Meeter, B. R. J. Jansen, and M. Huizinga, “A mediated model of study choice profiles and person-environment fit with bachelor's program as predictors of academic success in higher education,” Learning and Individual Differences, vol. 114, Art. no. 102512, 2024, doi: 10.1016/j.lindif.2024.102512.

[2] A. O. Alsayed et al., “Selection of the right undergraduate major by students using supervised learning techniques,” Applied Sciences, vol. 11, no. 22, Art. no. 10639, 2021, doi: 10.3390/app112210639.

[3] Y. Zayed, Y. Salman, and A. Hasasneh, “A recommendation system for selecting the appropriate undergraduate program at higher education institutions using graduate student data,” Applied Sciences, vol. 12, no. 24, Art. no. 12525, 2022, doi: 10.3390/app122412525.

[4] C. Lahoud, S. Moussa, C. Obeid, H. El Khoury, and P.-A. Champin, “A comparative analysis of different recommender systems for university major and career domain guidance,” Education and Information Technologies, vol. 28, pp. 8733-8759, 2023, doi: 10.1007/s10639-022-11541-3.

[5] J. R. Rico-Juan, M. L. Pertegal-Felices, and A. Jimeno-Morenilla, “Using explainable AI to align pre-university profiles with bachelor’s degree success,” Scientific Reports, vol. 15, Art. no. 44373, 2025, doi: 10.1038/s41598-025-28179-z.

[6] S. Chockalingam, W. Pieters, A. M. H. Teixeira, and P. H. A. J. M. van Gelder, “Probability elicitation for Bayesian networks to distinguish between intentional attacks and accidental technical failures,” Journal of Information Security and Applications, vol. 75, Art. no. 103497, 2023, doi: 10.1016/j.jisa.2023.103497.

[7] F. L. da Silva, B. K. Slodkowski, K. K. A. da Silva, and S. C. Cazella, “A systematic literature review on educational recommender systems for teaching and learning: Research trends, limitations and opportunities,” Education and Information Technologies, vol. 28, no. 3, pp. 3289-3328, 2023, doi: 10.1007/s10639-022-11341-9.

[8] M. Elahi, A. D. Starke, N. El Ioini, A. A. Lambrix, and C. Trattner, “Developing and evaluating a university recommender system,” Frontiers in Artificial Intelligence, vol. 4, Art. no. 796268, 2022, doi: 10.3389/frai.2021.796268.

[9] M. C. Urdaneta-Ponte, A. Mendez-Zorrilla, and I. Oleagordia-Ruiz, “Recommendation systems for education: Systematic review,” Electronics, vol. 10, no. 14, Art. no. 1611, 2021, doi: 10.3390/electronics10141611.

[10] S. Somyürek and N. Aksoy, “Navigating academia: Designing and evaluating a multidimensional recommendation system for university and major selection,” Psychology in the Schools, vol. 61, no. 9, pp. 3748-3769, 2024, doi: 10.1002/pits.23255.

[11] E. J. Delahoz-Domínguez and R. Hijón-Neira, “Recommender system for university degree selection: A socioeconomic and standardised test data approach,” Applied Sciences, vol. 14, no. 18, Art. no. 8311, 2024, doi: 10.3390/app14188311.

[12] W. E. Nugroho, T. Prihandoyo, and O. Somantri, “Optimalisasi metode Naive Bayes untuk menentukan program studi bagi calon mahasiswa baru dengan pendekatan unsupervised discretization,” Infotekmesin, vol. 13, no. 1, 2022, doi: 10.35970/infotekmesin.v13i1.1048.

[13] R. Suwanda, S. F. Anshari, R. P. Fhonna, and T. Setiawan, “Penerapan metode Naïve Bayes dalam sistem rekomendasi pemilihan program studi pendidikan tinggi berbasis website,” Jurnal Minfo Polgan, vol. 14, no. 2, pp. 2361-2369, 2025, doi: 10.33395/jmp.v14i2.15858.

[14] K. Mustofa, T. Tasa, and D. Kurniawan, “Appropriateness of student major selection using Naive Bayes and K-nearest neighbor algorithms at SMK Plus Al Musyarrofah,” Eduvest: Journal of Universal Studies, vol. 4, no. 6, pp. 5436-5456, 2024, doi: 10.59188/eduvest.v4i6.1483.

[15] K. Alshaikh, N. Bahurmuz, O. Torabah, S. Alzahrani, Z. Alshingiti, and M. Meccawy, “Using recommender systems for matching students with suitable specialization: An exploratory study at King Abdulaziz University,” International Journal of Emerging Technologies in Learning, vol. 16, no. 3, pp. 316-324, 2021, doi: 10.3991/ijet.v16i03.17829.

[16] G. Lampropoulos, “Recommender systems in education: A literature review and bibliometric analysis,” Advances in Mobile Learning Educational Research, vol. 3, no. 2, pp. 829-850, 2023, doi: 10.25082/AMLER.2023.02.011.

[17] W. E. Nugroho, A. Sofyan, and O. Somantri, “Metode Naive Bayes dalam menentukan program studi bagi calon mahasiswa baru,” Infotekmesin, vol. 12, no. 1, pp. 59-64, 2021, doi: 10.35970/infotekmesin.v12i1.491.

[18] L. Bojke, M. Soares, K. Claxton, A. Colson, A. Fox, C. Jackson, D. Jankovic, A. Morton, L. Sharples, and A. Taylor, “Developing a reference protocol for structured expert elicitation in health-care decision-making: a mixed-methods study,” Health Technology Assessment, vol. 25, no. 37, pp. 1-124, 2021, doi: 10.3310/hta25370.

[19] C. J. Williams, K. J. Wilson, and N. Wilson, “A comparison of prior elicitation aggregation using the classical method and SHELF,” Journal of the Royal Statistical Society: Series A (Statistics in Society), vol. 184, no. 3, pp. 920-940, 2021, doi: 10.1111/rssa.12691.

[20] M. Soares et al., “Recommendations on the use of structured expert elicitation protocols for healthcare decision making: A good practices report of an ISPOR task force,” Value in Health, vol. 27, no. 11, pp. 1469-1478, 2024, doi: 10.1016/j.jval.2024.07.027.