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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Kreativitas PKM Journal of Computer Networks, Architecture and High Performance Computing
Aryasa, Jiyestha Aji Dharma
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Computer Science & IT Economics, Econometrics & Finance Education Health Professions Medicine & Pharmacology Nursing Public Health

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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.
Program Manajemen Pencegahan Anemia melalui Pendekatan Multidisiplin pada Remaja Putri di Wilayah Binaan Desa Melinggih Susanti, Ni Luh Putu Dina; Agustini, Ni Komang Tri; I KetutSwarjana, I KetutSwarjana; Diyu, Ida Ayu Ningrat Pangruating; Lewar, Emanuel Iileatan; Mastryagung, Gusti Ayu Dwina; Anggaraeni, Komang Rossa Tri; Widnyani, Ida Ayu Ary; Aryasa, Jiyestha Aji Dharma; Kusuma, I Gusti Agung Ari; Rittiruang, Amonwan
Jurnal Kreativitas Pengabdian Kepada Masyarakat (PKM) Vol 8, No 10 (2025): Volume 8 No 10 (2025)
Publisher : Universitas Malahayati Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/jkpm.v8i10.22241

Abstract

ABSTRAK Anemia pada remaja masih menjadi masalah global yang memerlukan intervensi dari berbagai multidisiplin. Selain itu, kesadaran terhadap pentingnya pencegahan anemia belum menjadi perhatian utama di kalangan remaja. Kegiatan ini mampu meningkatkan pengetahuan dan remaja dalam manajemen pencegahan anemia serta merubah perilaku remaja dalam menerapkan hidup sehat cegah anemia. Kegiatan yang tercapai adalah peningkatan pengetahuan remaja dalam manajemen pencegahan anemia pada remaja. Kegiatan ini mampu meningkatkan pemahaman remaja dalam melakukan pencegahan anemia dan perubahan perilaku hidup sehat untuk mendukung pencegahan anemia serta meningkatkan kesadarannya melakukan skrining kesehatan secara rutin. Kata Kunci: Anemia, Remaja, Edukasi Kesehatan, Pengetahuan.  ABSTRACT Anemia in adolescents remains a global problem that requires multidisciplinary interventions. In addition, awareness of the importance of anemia prevention has not yet become a major concern among adolescents. This activity is able to increase knowledge and adolescents in anemia prevention management and change adolescent behavior in implementing a healthy lifestyle to prevent anemia. The activity achieved is an increase in adolescent knowledge in anemia prevention management in adolescents. This activity is able to increase adolescents' understanding in preventing anemia and changes in healthy lifestyle behavior to support anemia prevention and increase their awareness of conducting routine health screenings. Keywords: Anemia, Adolescent, Health Education, Knowledge.
Prediksi Perubahan Hemodinamik Pasien setelah Pemberian Premedikasi menggunakan Machine Learning Neural Network Guna Meningkatkan Kinerja Penanganan Medis Aryasa, Jiyestha Aji Dharma; Widodo, Aris Puji; Widodo, Catur Edi
Jurnal Sistem Informasi Bisnis Vol 14, No 3 (2024): Volume 14 Nomor 3 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss3pp256-266

Abstract

This research presents the development process of a machine learning neural network model for predicting hemodynamic changes in patients after premedication, aiming to enhance the performance of medical interventions. The model was constructed using 3055 patients’ data who underwent premedication processes. The developed neural network model has an architecture consisting of 10 nodes in the input layer, 10 nodes in the hidden layer, and 3 nodes in the output layer. The evaluation results of the model indicate an overall accuracy of 85%. The precision values are high for normal class predictions at 0.85 and for hypertension class predictions at 0.81 with corresponding recalls of 1 (high) and 0.6 (moderate), respectively. However, predictions for the hypotension class still have a low precision of 0.6 and a recall of 0.04 (very low) due to the significantly lower number of samples in the hypotension class compared to the normal and hypertension classes. While testing with new data, the model has successfully predicted whether patients will experience hemodynamic pressure changes. It is expected that this model can contribute to improving the performance of medical interventions, thereby minimizing undesirable hemodynamic pressure changes.
Co-Authors Agustini, Ni Komang Tri Anggaraeni, Komang Rossa Tri Aris Puji Widodo Catur Edi Widodo Dewi, Ni Putu Dita Ariani Sukma Diyu, Ida Ayu Ningrat Pangruating Gusti Ayu Dwina Mastryagung I Gede Hendrayana I KetutSwarjana, I KetutSwarjana Ida Ayu Ary Widnyani, Ida Ayu Ary Kusuma, I Gusti Agung Ari Lewar, Emanuel Iileatan Ni Luh Putu Dina Susanti Prayoga, I Made Ade Raharjo, Rizki Anom Rittiruang, Amonwan