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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JSiI (Jurnal Sistem Informasi) JURNAL PENDIDIKAN TAMBUSAI JURIKOM (Jurnal Riset Komputer) TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Jurnal Pengabdian Masyarakat Bhinneka Indonesian Journal of Multidisciplinary on Social and Technology Jurnal Abdimas Maduma LOGIC : Jurnal Ilmu Komputer dan Pendidikan OKTAL : Jurnal Ilmu Komputer dan Sains BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat APPA : Jurnal Pengabdian Kepada Masyarakat Riau Jurnal Teknik Informatika
Yuda Samudra
Universitas Pamulang
Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Other

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COMPARATIVE ANALYSIS OF BAGGING AND BOOSTING MODELS IN ENSEMBLE LEARNING FOR GRADUATION PREDICTION Sartika Lina Mulani Sitio; Darmawati; Yuda Samudra
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7579

Abstract

Student graduation prediction is an important aspect in supporting academic decision-making in higher education. However, conventional evaluation approaches have not been able to identify the risk of early graduation delays. This study aims to compare the performance of two ensemble learning approaches, namely Bagging using Random Forest and Boosting using XGBoost, in predicting student graduation. The study used  the Predict Students' Dropout and Academic Success dataset  consisting of 4,424 student data. Both models were trained on the same data and evaluated using the Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics. The results of the experiment showed that both models had almost equal accuracy, i.e. 82.6% for Random Forest and 82.5% for XGBoost. However, XGBoost showed better performance on Recall (0.878) and F1-Score (0.834), which indicated a higher ability to detect students who actually graduated. Based on these results, this study concludes that XGBoost is more effective than Random Forest in the context of predicting student graduation and is more suitable to be applied to  the Academic Early Warning System in universities
Sistem Penunjang Keputusan Berbasis Web Untuk Merekomendasikan Program Pelatihan Intensif Terbaik Berdasarkan Minat Keahlian Menggunakan Metode Simple Additive Weighting (SAW) Studi Kasus: Hima TI UNPAM Muhammad Fiqi Eyriska Rokhim; Yuda Samudra
Indonesian Journal of Multidisciplinary on Social and Technology Vol. 4 No. 2 (2026): Maret - Juni
Publisher : PT Ilmu Data Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijmst.v4i2.9437

Abstract

Perkembangan teknologi informasi menuntut organisasi untuk mampu mengambil keputusan secara cepat, tepat, dan objektif, termasuk dalam menentukan program pelatihan intensif yang sesuai dengan minat dan keahlian mahasiswa. Himpunan Mahasiswa Teknik Informatika Universitas Pamulang (HIMA TI UNPAM) masih mengalami kendala dalam proses pemilihan pelatihan karena dilakukan secara manual dan subjektif tanpa menggunakan metode evaluasi yang terstruktur. Penelitian ini bertujuan untuk merancang dan membangun Sistem Penunjang Keputusan (SPK) berbasis web yang dapat memberikan rekomendasi program pelatihan intensif terbaik berdasarkan minat keahlian mahasiswa menggunakan metode Simple Additive Weighting (SAW). Metode penelitian yang digunakan meliputi observasi, wawancara, dan studi pustaka untuk memperoleh data kebutuhan sistem. Adapun metode pengembangan sistem menggunakan pendekatan Rapid Application Development (RAD) dengan pemodelan Unified Modeling Language (UML). Sistem dikembangkan menggunakan bahasa pemrograman PHP dan database MySQL. Metode SAW diterapkan melalui proses penentuan kriteria, pembobotan, normalisasi matriks, dan perhitungan nilai preferensi untuk menghasilkan peringkat alternatif pelatihan terbaik. Kriteria yang digunakan meliputi lokasi, biaya pelatihan, durasi, metode pelatihan, dan kesesuaian keahlian. Hasil penelitian menunjukkan bahwa sistem mampu memberikan rekomendasi pelatihan secara lebih objektif, cepat, dan transparan berdasarkan nilai preferensi tertinggi. Sistem yang dibangun juga mempermudah mahasiswa dalam memilih program pelatihan yang sesuai dengan kebutuhan kompetensi dan minat keahlian sehingga mendukung pengembangan kemampuan secara optimal.
Co-Authors Ade Putra Prima Suhendri Amin Hidayat Amin Hidayat Andriyanto, Lely Panca Badriah Nursakinah Darmawati Fadilah, Muhammad Farhan Fizram, Muhammad Herwis Gultom Hizkia Bayu Wijaya Lely Panca Andriyanto Lely Panca Andriyanto Meidy Fajar Wahyu Meidy Fajar Wahyu Muhammad Fiqi Eyriska Rokhim Nanang Nanang Nanang nanang nanang Nardiono NURHALIMAH Oke Hariansyah Rahmat Hidayat Sartika Lina Mulani Suhendri, Ade Putra Prima Suryaningrat Wiwin Winarti Zurnan Alfian