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All Journal Widya Balina :Jurnal Ilmu Pendidikan dan Ekonomi Journal of Computer Networks, Architecture and High Performance Computing
Raharjo, Rizki Anom
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Computer Science & IT Economics, Econometrics & Finance Education

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The implementation of the Random Forest Algorithm with Resampling and Without Resampling on the Hepatitis C Disease Dataset Hendrayana, I Gede; Dewi, Ni Putu Dita Ariani Sukma; Aryasa, Jiyestha Aji Dharma; Prayoga, I Made Ade; Raharjo, Rizki Anom
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6089

Abstract

This study evaluates the performance of Random Forest models for Hepatitis C classification using a dataset from Kaggle, focusing on addressing class imbalance through resampling techniques. We compare three approaches: baseline Random Forest without resampling, Random Forest with SMOTE+ENN (Synthetic Minority Oversampling Technique + Edited Nearest Neighbors), and Random Forest with SMOTE+OSS (Synthetic Minority Oversampling Technique + One-Sided Selection). Results show that the baseline model achieved high accuracy (0.9837) but failed to detect minority classes (e.g., suspect Blood Donor recall=0.00). SMOTE+ENN significantly improved performance, achieving perfect classification (precision=1.00, recall=1.00) for Hepatitis, Fibrosis, and Cirrhosis, while maintaining high accuracy (0.9919) and ROC AUC (0.9999). In contrast, SMOTE+OSS showed limitations in detecting Hepatitis (recall=0.00) and yielded lower precision for Fibrosis (0.44), indicating its undersampling approach may be too aggressive. The study highlights SMOTE+ENN as the most effective method for balancing class distribution and enhancing model robustness in medical diagnostics. These findings underscore the importance of selecting appropriate resampling techniques to improve minority class detection in imbalanced datasets, with implications for developing reliable AI-based diagnostic tools for Hepatitis C.
EFEKTIVITAS PENGGUNAAN MEDIA PEMBELAJARAN VISUAL BERBASIS GENERATIVE AI TERHADAP TINGKAT KETERLIBATAN BELAJAR MAHASISWA Raharjo, Rizki Anom
Widya Balina Vol. 11 No. 1 (2026): Widya Balina - Jurnal Ilmu Pendidikan dan Ekonomi
Publisher : widya balina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53958/wb.v11i1.990

Abstract

Keterlibatan belajar (student engagement) merupakan faktor kunci dalam keberhasilan akademik, terutama dalam mata kuliah di Program Studi Ilmu Komputer yang sering dianggap abstrak dan kompleks. Materi visual tradisional seperti slide presentasi statis sering kali gagal mempertahankan fokus mahasiswa. Penelitian ini bertujuan untuk mengukur efektivitas media pembelajaran visual yang didesain menggunakan alat Generative AI (seperti DALL-E 3 atau Midjourney) terhadap tingkat keterlibatan belajar mahasiswa. Penelitian menggunakan metode quasi-eksperimen (Nonequivalent Control Group Design) pada dua kelas paralel di salah satu perguruan tinggi di Indonesia. Satu kelas (Kelas Eksperimen) menggunakan media ajar visual hasil prompting AI yang disesuaikan secara presisi dengan silabus, sedangkan kelas lainnya (Kelas Kontrol) menggunakan media visual buku teks standar. Keterlibatan belajar mahasiswa diukur melalui angket Likert yang mencakup tiga dimensi: Kognitif, Emosional, dan Perilaku. Analisis data menggunakan uji-t independen (independent t-test). Hasil sintesis penelitian menunjukkan peningkatan skor keterlibatan belajar yang signifikan di Kelas Eksperimen pada semua dimensi dibandingkan Kelas Kontrol, dengan peningkatan tertinggi pada dimensi emosional dan kognitif. Temuan ini menyimpulkan bahwa media pembelajaran visual berbasis Generative AI efektif meningkatkan keterlibatan belajar mahasiswa dan direkomendasikan sebagai alat bantu pedagogi modern di era digital.
Co-Authors Aryasa, Jiyestha Aji Dharma Dewi, Ni Putu Dita Ariani Sukma I Gede Hendrayana Prayoga, I Made Ade