Model Prediksi Ketercapaian Learning Outcome Based Education Mahasiswa di Program Studi Teknik Informatika Menggunakan Algoritma Machine Learning | Jurnal Informatika Ekonomi Bisnis

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Model Prediksi Ketercapaian Learning Outcome Based Education Mahasiswa di Program Studi Teknik Informatika Menggunakan Algoritma Machine Learning

Muhtajuddin Danny Universitas Pelita Bangsa Muhamad Fatchan Universitas Pelita Bangsa DOI: https://doi.org/10.37034/infeb.v7i3.1259 Keywords: Outcome Based Education, Learning Outcomes, Program Learning Outcomes, Machine Learning, Random Forest Abstract The Informatics Engineering Undergraduate Program, Faculty of Engineering, Pelita Bangsa University, implements Outcome Based Education (OBE) by emphasizing the achievement of student Learning Outcomes (LO) as an indicator of the quality of learning in higher education. LO achievement measurement has been mostly done manually through academic assessments, so it is less than optimal in predicting student performance comprehensively. This study aims to build a prediction model for student Learning Outcomes achievement using machine learning algorithms. Research data were obtained from academic results, attendance, lecture activities, and student skill indicators. The prediction model was developed by comparing the Support Vector Machine (SVM), Random Forest, Decision Tree, and Artificial Neural Network (ANN) algorithms, with performance evaluation using accuracy, precision, recall, and F1-score metrics. The results showed that the Random Forest algorithm provided the best performance with more stable accuracy compared to other algorithms. Furthermore, the distribution of Program Learning Outcomes (PLO) in the curriculum shows: PLO 1 (57 courses), PLO 2 (10 courses), PLO 3 (3 courses), PLO 4 (27 courses), PLO 5 (8 courses), PLO 6 (20 courses), PLO 7 (33 courses), PLO 8 (10 courses), PLO 9 (54 courses), and PLO 10 (57 courses). Based on student scores in 57 courses, the distribution of assessment categories is as follows: Very Good 38.1%, Good 46.3%, Fair 8.4%, and Fail 7.2%. Thus, the PLO achievement of the Informatics Engineering Undergraduate Study Program reached 84.4% in the Good and Very Good categories. This finding provides a significant contribution to efforts to monitor and plan strategies for improving the quality of OBE-based learning adaptively and data-driven. Downloads Download data is not yet available. References Gede Agus Jaya Negara, Ni Rai Vivien Pitriani, and Luh Putu Widya Fitriani (2024). Kurikulum Berbasis OBE (Outcome Based Education) dengan Nilai-Nilai Karakter untuk Meningkatkan Kualitas Mutu Pendidikan Perguruan Tinggi. Jurnal Penelitian dan Pengembangan Pendidikan, 8(1), pp. 41–48. DOI: https://doi.org/10.23887/jppp.v8i1.68767 . M. Hernández-Campos, A. Gonzalez-Torres, and F. J. García-Peñalvo (2025). Learning Outcomes Evaluation Through Learning Analytics Systems in Higher Education: A Systematic Literature Review. Sage Open, 15(3). DOI: https://doi.org/10.1177/21582440251347374 . Gede Agus Jaya Negara, Ni Rai Vivien Pitriani, and Luh Putu Widya Fitriani (2024). Kurikulum Berbasis OBE (Outcome Based Education) dengan Nilai-Nilai Karakter untuk Meningkatkan Kualitas Mutu Pendidikan Perguruan Tinggi. Jurnal Penelitian dan Pengembangan Pendidikan, 8(1), pp. 41–48. DOI: https://doi.org/10.23887/jppp.v8i1.68767 . Gefy Fitry Wijaya and Dwi Yuniarto (2024). Tinjauan Penerapan Machine Learning pada Sistem Rekomendasi Menggunakan Model Klasifikasi, Populer: Jurnal Penelitian Mahasiswa, 3(4), pp. 144–153. DOI: https://doi.org/10.58192/populer.v3i4.2798 . A. F. S. Wahyudi and Dadang Heksaputra (2023). Pengembangan Aplikasi Penilaian Outcome-Based Education (OBE) Berbasis Website dengan Metode Waterfall, INSERT : Information System and Emerging Technology Journal, 4(2), pp. 86–94. DOI: https://doi.org/10.23887/insert.v4i2.65287 . Gede Agus Jaya Negara, Ni Rai Vivien Pitriani, and Luh Putu Widya Fitriani (2024). Kurikulum Berbasis OBE (Outcome Based Education) dengan Nilai-Nilai Karakter untuk Meningkatkan Kualitas Mutu Pendidikan Perguruan Tinggi. Jurnal Penelitian dan Pengembangan Pendidikan, 8(1), pp. 41–48. DOI: https://doi.org/10.23887/jppp.v8i1.68767 . U. M. Ishaq, M. F. Wicaksono, and S. Nurhayati (2023). Aplikasi Probe untuk Penilaian Capaian Pembelajaran Mahasiswa pada Kurikulum OBE (Outcame-Based Education), Komputika : Jurnal Sistem Komputer, 12(2), pp. 67–74. DOI: https://doi.org/10.34010/komputika.v12i2.9763 . A. Roihan, P. A. Sunarya, and A. S. Rafika (2020). Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper, IJCIT (Indonesian Journal on Computer and Information Technology), 5(1). DOI: https://doi.org/10.31294/ijcit.v5i1.7951 . K.-L. Du, B. Jiang, J. Lu, J. Hua, and M. N. S. Swamy (2024). Exploring Kernel Machines and Support Vector Machines: Principles, Techniques, and Future Directions, Mathematics, 12 (24), p. 3935. DOI: https://doi.org/10.3390/math12243935 . R. S. M. Meilanie (2020). Survei Kemampuan Guru dan Orangtua dalam Stimulasi Dini Sensori pada Anak Usia Dini, Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini, 5(1) pp. 958–964. DOI: https://doi.org/10.31004/obsesi.v5i1.741 . H. D. Bhakti (2019). Aplikasi Artificial Neural Network (ANN) untuk Memprediksi Masa Studi Mahasiswa Program Studi Teknik Informatika Universitas Muhammadiyah Gresik, Eksplora Informatika, 9(1) pp. 88–95. DOI: https://doi.org/10.30864/eksplora.v9i1.234 . N. Sghir, A. Adadi, and M. Lahmer (2023). Recent Advances in Predictive Learning Analytics: A decade Systematic Review (2012–2022), Educ Inf Technol (Dordr), 28(7), pp. 8299–8333. DOI: https://doi.org/10.1007/s10639-022-11536-0 . J. Wang and Y. Yu (2025). Machine Learning Approach to Student Performance Prediction of Online Learning, PLoS One 20(1), p. e0299018. DOI: https://doi.org/10.1371/journal.pone.0299018 . Gede Agus Jaya Negara, Ni Rai Vivien Pitriani, and Luh Putu Widya Fitriani (2024). Kurikulum Berbasis OBE (Outcome Based Education) dengan Nilai-Nilai Karakter untuk Meningkatkan Kualitas Mutu Pendidikan Perguruan Tinggi, Jurnal Penelitian dan Pengembangan Pendidikan, 8(1), pp. 41–48. DOI: https://doi.org/10.23887/jppp.v8i1.68767 . J. A. Firdaus, A. S. Budi, and E. Setiawan (2023). Analisis Performa Algoritma Machine Learning pada Perangkat Embedded ATmega328P, Jurnal Teknologi Informasi dan Ilmu Komputer, 10(2), pp. 245–254. DOI: https://doi.org/10.25126/jtiik.20236196 . C. Molla-Esparza, M. I. Gómez-Núñez, and F. J. García-García (2025). Applications of Learning Analytics in the Study of Academic Performance in Higher Education: A Pilot-Tested Meta-Review Protocol, International Journal of Educational Research Open, 8, p. 100433. DOI: https://doi.org/10.1016/j.ijedro.2024.100433 . Z. Luo et al., (2024). A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space, Mathematics, 12(22, p. 3597. DOI: https://doi.org/10.3390/math12223597 . D. Sulistiana (2022). Partisipasi dan Keaktifan Mahasiswa pada Mata Kuliah Metode Penelitian Pendidikan I Menggunakan Aplikasi Zoom Meeting, Konstruktivisme : Jurnal Pendidikan dan Pembelajaran, 14(1) pp. 68–81. DOI: https://doi.org/10.35457/konstruk.v14i1.1975 . J. Shawe-Taylor and S. Sun (2011). A Reviewof Optimization Methodologies in Support Vector Machines, Neurocomputing, 74(17), pp. 3609–3618. DOI: https://doi.org/10.1016/j.neucom.2011.06.026 . A. Fauzi and A. H. Yunial (2022). Optimasi Algoritma Klasifikasi Naive Bayes, Decision Tree, K – Nearest Neighbor, dan Random Forest menggunakan Algoritma Particle Swarm Optimization pada Diabetes Dataset, Jurnal Edukasi dan Penelitian Informatika (JEPIN), 8(3), p. 470. DOI: https://doi.org/10.26418/jp.v8i3.56656 . F. Fadilah, M. Melina, and A. Komarudin (2024). Prediksi Penjualan Obat Menggunakan Metode Artificial Neural Network, Jurnal Informatika Teknologi dan Sains (Jinteks), 6(3), pp. 521–531. DOI: https://doi.org/10.51401/jinteks.v6i3.4346 . F. L. Zahroh and F. Hilmiyati (2024). Indikator Keberhasilan dalam Evaluasi Program Pendidikan, Edu Cendikia: Jurnal Ilmiah Kependidikan, 4(3) pp. 1052–1062. DOI: https://doi.org/10.47709/educendikia.v4i03.5049 . F. P. Oganda, S. Wulandari, V. Meilinda, A. Rossi, and M. Khasanah (2025). Implementasi Outcome-Based Education (OBE) dan Benchmarking Internasional dalam Pengabdian Mahasiswa melalui Pengelolaan Stok Opname di RIC, ADI Pengabdian Kepada Masyarakat, 5(2), pp. 80–90. DOI: https://doi.org/10.34306/adimas.v5i2.1218 . Ramel Iftina Na’ifah, Raihani Salsabila, and Gusmaneli Gusmaneli (2025). Strategi Pembelajaran Peningkatan Kemampuan Berpikir, Jurnal Sadewa : Publikasi Ilmu Pendidikan, Pembelajaran dan Ilmu Sosial, 3(2), pp. 224–231. DOI: https://doi.org/10.61132/sadewa.v3i2.1802 . Yanuarini Nur Sukmaningtyas, R. Makhfuddin Akbar, and G. Rohma Utami Asyafiiyah (2024). Penerapan Predictive Analytics untuk Analisis Faktor-faktor yang Mempengaruhi Performa Akademik Siswa, Arcitech: Journal of Computer Science and Artificial Intelligence, 4(2). DOI: https://doi.org/10.29240/arcitech.v4i2.12048 .	Download Published 2025-09-30 Issue Vol. 7, No. 3 (September 2025) Section Articles How to Cite Danny, M., & Fatchan, M. (2025). Model Prediksi Ketercapaian Learning Outcome Based Education Mahasiswa di Program Studi Teknik Informatika Menggunakan Algoritma Machine Learning. Jurnal Informatika Ekonomi Bisnis, 7(3), 684-691. https://doi.org/10.37034/infeb.v7i3.1259 ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver Download Citation Endnote/Zotero/Mendeley (RIS) BibTeX This work is licensed under a Creative Commons Attribution 4.0 International License.

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