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All Journal Journal of Educational Science and Technology DIDAKTIKA BIOLOGI: Jurnal Penelitian Pendidikan Biologi Jurnal Biologi Tropis ILKOM Jurnal Ilmiah Spirit Publik : Jurnal Administrasi Publik Jurnal Inovasi dan Teknologi Pembelajaran (JINOTEP) Kajian dan Riset Dalam Teknologi Pembelajaran Jurnal Kesehatan Tambusai Agrisintech (Journal of Agribusiness and Agrotechnology) Jurnal EBONI Bhakti Nagori (Jurnal Pengabdian kepada Masyarakat) Mediagro Sanskara Pendidikan dan Pengajaran Jurnal Penelitian Pendidikan Indonesia Balanting : Jurnal Pengabdian Kepada Masyarakat Balanting
Prihatin Prihatin, Prihatin
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Nursing Public Health Social Sciences Other

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A Hybrid Deep Learning–Machine Learning Approach for the Identification of Active Compounds in Blumea balsamifera (Sembung Leaves) Kusnaeni, Kusnaeni; Prihatin, Prihatin; Rahmatullah, Rahmatullah; Hafid, Mega Sartika; Nisardi, Muhammad Rifki; Nurmalasari, Nurmalasari; Andy B, Afif Budi
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3195.165-179

Abstract

Blumea balsamifera (sembung) is a medicinal plant with well-documented antibacterial, anti-inflammatory, and analgesic properties. However, the systematic identification of its bioactive compounds remains a significant challenge due to the complexity and high dimensionality of LC–MS (Liquid Chromatography–Mass Spectrometry) data. This study aims to develop a robust computational framework for automated compound identification using a hybrid modeling approach.A hybrid model integrating Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) was employed to enhance feature extraction and classification performance. The LSTM component was utilized to capture sequential dependencies in spectral data, while XGBoost performed optimized classification through gradient boosting. This integration enables efficient handling of complex spectral patterns and improves predictive accuracy.The proposed model achieved an accuracy of 91%, demonstrating strong performance in classifying and identifying bioactive compounds. Feature importance analysis identified several key compounds contributing to the model predictions, including Luteolin-7-methyl-ether, Umbelliferone, Blumeatin, Dihydroquercetin-7,4′-dimethylether, Chrysosplenol C, Blumealactone B, and Blumeaene E. These compounds are associated with known pharmacological activities, supporting the therapeutic relevance of B. balsamifera.The proposed hybrid LSTM–XGBoost framework provides an effective and scalable approach for LC–MS-based compound identification. This method reduces analytical complexity, enhances classification reliability, and offers a data-driven strategy for accelerating phytochemical research and bioactive compound validation
Co-Authors Andy B, Afif Budi Aristyarini, Rizki Bambang Fry Due, Maria Serviana Elisa Rosiana Etik Purwanti Evar, Fitrawaty Orista Fara Merian Sari Fathulliansyah, Noor Fatirul, Achmad Noor Gustam, Andriyana Hafid, Mega Sartika Hamakonda, Umbu Awang Harwanto Harwanto Hatimah, Husnul Hatimah Hidayati Desy Irwan Irwan Kusnaeni Kusnaeni, Kusnaeni Manggala Putra, Ardi Muhammad Rifki Nisardi Niken Nur Kasim, Niken Nur Nur Hardina Nurmalasari Nurmalasari Nurpeni, Nurpeni Orista Evar, Fitrawaty P., Agus Setyowati Devita Prawista, Yeni Eka Priyadi Priyadi Puspita, Victoria Ayu Putra, Ardi Manggala Rahmatullah Rahmatullah Rasidi, Rasidi Robani, Anidah Rosdiana, Shinta P. Rufi'i, Rufi'i Sabariah Sabariah Saktiawan, Yusup Setiawan, Eka Setiawan Sihombing, David Bastian Sri Sukmawati Sukri, Hadija Surahman, Suryansyah Susi Herawati Suwestian, M.Firdaus Taus, Igniosa Victoria Coo Lea Wahyuni, Ike Dian Wawan Gunawan Wiridannisaa, Nurul Yusuf Yusuf