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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Medika: Medika
Gultom, Ria Fazelita Br
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Dentistry Education Health Professions Materials Science & Nanotechnology Medicine & Pharmacology Nursing Physics Public Health

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Inovasi Edukasi Digital: Penggunaan E-Booklet untuk Meningkatkan Pengetahuan Remaja Putri Tentang Anemia Defisiensi Besi di SMA Swasta Primbana Medan Lubis, Dita Anggriani; Hidayati, Yusmalia; Nainggolan, Wiwik Elsada; Gultom, Ria Fazelita Br; Wulan, Retno
Jurnal Medika: Medika Vol. 5 No. 1 (2026)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/njf3h920

Abstract

Anemia defisiensi besi merupakan salah satu masalah kesehatan yang signifikan di Indonesia, khususnya pada remaja putri. Data Riskesdas 2018 menunjukkan bahwa 32% remaja putri mengalami anemia, yang dapat berdampak pada penurunan daya tahan tubuh, konsentrasi belajar, dan produktivitas. Kondisi ini diperburuk oleh kurangnya asupan zat besi dari makanan sehari-hari dan rendahnya kepatuhan dalam mengonsumsi tablet tambah darah (TTD). Untuk mengatasi permasalahan ini, dilakukan kegiatan pengabdian kepada masyarakat berupa edukasi pencegahan anemia defisiensi besi kepada siswi SMA Swasta Primbana Medan, Sumatera Utara. Kegiatan ini bertujuan untuk meningkatkan pengetahuan dan kesadaran remaja putri mengenai pentingnya asupan zat besi dan pencegahan anemia. Metode yang digunakan meliputi penyuluhan interaktif, diskusi kelompok, serta pembagian leaflet informatif mengenai anemia dan pentingnya konsumsi TTD. Evaluasi dilakukan melalui pre-test dan post-test untuk mengukur peningkatan pengetahuan peserta
Multimethodology Analysis of Determinants of Breast Cancer Diagnosis Machine Learning Lubis, Dita Anggriani; Irnawati, Yuli; Pamilih, Ayu Trisni; Gultom, Ria Fazelita Br
Jurnal Penelitian Pendidikan IPA Vol 12 No 1 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i1.12497

Abstract

Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, highlighting the urgent need for accurate and interpretable diagnostic models. While machine learning has shown promise in classification tasks, many existing models lack transparency and overlook the individual contribution of cellular features essential for clinical decision-making.This study proposes an integrative and explainable framework to identify the most influential cellular-level features in distinguishing between benign and malignant breast tumors. Using a publicly available dataset comprising 569 observations and 32 numerical features, we conducted a multi-step analysis. Feature relevance was first evaluated using Pearson correlation. Random Forest and Recursive Feature Elimination (RFE) were employed to rank and refine the feature subset, followed by Principal Component Analysis (PCA) for dimensionality reduction and pattern visualization. SHapley Additive exPlanations (SHAP) were utilized to interpret individual predictions. Complementary statistical tests, including t-tests and chi-square analyses, assessed associations between tumor characteristics and diagnosis. A logistic regression model was developed to evaluate predictive performance.Key cellular features—such as mean radius, texture, and concavity—were consistently identified as highly predictive of diagnosis. RFE demonstrated that fewer than 10 features were sufficient for optimal classification. The logistic regression model achieved high accuracy, offering a simpler yet effective alternative for prediction.By combining statistical methods with interpretable machine learning, this study presents a transparent and clinically relevant approach to breast cancer diagnosis. The integration of SHAP values bridges the gap between model performance and interpretability, supporting more informed and personalized clinical decisions. Future work should consider external validation, image-based features, and patient demographic variables to enhance generalizability.
Co-Authors Hidayati, Yusmalia Irnawati, Yuli Lubis, Dita Anggriani Nainggolan, Wiwik Elsada Pamilih, Ayu Trisni Retno Wulan