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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Bioedunis Journal
Rizki Khairani Nasution
UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan
Biochemistry, Genetics & Molecular Biology Computer Science & IT Economics, Econometrics & Finance

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Inventory of Moss (Bryophyta) in Batang Gadis National Park, Mandailing Natal Regency as a Basis for Developing Contextual Biology Learning Rizki Khairani Nasution; Latifah Hannum Siregar; Lidya Putri Solehan Rambe; Fatimah Ammarwiyah Dongoran; Aztri Ramadhani Harahap; Hasim Pajar Siregar; Reza Pahlevi
Bioedunis Journal Vol 5, No 1 (2026)
Publisher : UIN Syekh Syekh Ali Hasan Ahmad Addary Padangsidimpuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24952/bioedunis.v5i1.20404

Abstract

This study aims to inventory the diversity of mosses (Bryophyta) in Batang Gadis National Park, Mandailing Natal Regency and analyze its potential as a basis for contextual biology learning in higher education. The study used a descriptive exploratory method through field surveys in several microhabitats, namely dense canopy forests, around streams, damp rocks, decaying wood, and forest paths. Data were collected through observation, morphological documentation, substrate recording, and sample identification. The results showed the discovery of 15 moss morphospecies consisting of mosses, liverworts, and hornworts. Habitats around streams have the highest diversity, while tree trunks are the dominant substrate. The diversity value is in the medium category, indicating that this area has microhabitats that support the growth of Bryophyta. The inventory results have the potential to be developed as learning resources in Lower Plant Botany, Plant Taxonomy, Plant Ecology, Biodiversity, and Conservation courses. This study contributes to the enrichment of local biodiversity data while supporting research-based biology learning and environmental potential.
An Explainable Clinical Variant Risk Assessment Framework for Genomic Decision Support Fahmi Izhari; Hanna Willa Dhany; Syarif Hidayat Matondang; Rizki Khairani Nasution
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp350-356

Abstract

Clinical variant interpretation is essential for precision medicine; however, conventional machine learning approaches often focus on prediction accuracy without providing sufficient interpretability and decision-support capabilities. This study proposes a hybrid framework integrating Light Gradient Boosting Machine (LightGBM), SHapley Additive exPlanations (SHAP), and a Fuzzy Decision Support System (FDSS) for clinical variant risk assessment using the ClinVar dataset. A stratified sample of 200,000 genetic variants was utilized for model development and evaluation. LightGBM was employed to predict variant pathogenicity, while SHAP was applied to identify feature contributions and improve model transparency. The resulting prediction probabilities were subsequently processed through fuzzy inference to generate interpretable risk categories and recommendation-oriented outputs. Experimental results showed that the proposed framework achieved an Accuracy of 95.89%, Precision of 95.58%, Recall of 82.97%, F1-Score of 88.83%, and ROC-AUC of 98.73%. Explainability analysis revealed that variant-type representation was the most influential predictor of pathogenicity. The proposed framework extends conventional classification by transforming predictive outputs into actionable risk assessments, thereby enhancing transparency and supporting informed genomic decision-making. These findings demonstrate the potential of integrating predictive analytics, explainable artificial intelligence, and fuzzy reasoning for clinical variant assessment in precision medicine.
Co-Authors Aztri Ramadhani Harahap Fahmi Izhari Fatimah Ammarwiyah Dongoran Hanna Willa Dhany Hasim Pajar Siregar Latifah Hannum Siregar Lidya Putri Solehan Rambe Reza Pahlevi Syarif Hidayat Matondang