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All Journal Jurnal Penelitian Farmasi Indonesia Jurnal Ners
Rika Melati
Universitas Nahdlatul Ulama Kalimantan Timur
Health Professions Medicine & Pharmacology Nursing

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Deep Learning CNN Untuk Identifikasi Gulma Berkhasiat Obat Kusnadi Kusnadi; Rika Melati; Qamarudin Arrasyid
Jurnal Ners Vol. 10 No. 1 (2026): JANUARI 2026
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jn.v10i1.52667

Abstract

Keanekaragaman hayati Indonesia, termasuk gulma yang memiliki senyawa berkhasiat obat namun masih sulit diidentifikasi secara manual. Tujuan penelitian adalah menemukan dan mengembangkan model deep learning berbasis convolution neural network (CNN) untuk identifikasi gulma berkhasiat obat dengan memperhatikan jumlah filter pada setiap lapisan konvolusional. Metode dilakukan melalui pengumpulan dataset citra sebanyak 12 spesies gulma dengan tiap kelas berjumlah 220 citra, prapemrosesan data, pelatihan model dilakukan pada tiga skema arsitektur CNN yang berbeda dan pengujian model dengan evaluasi menggunakan hasil klasifikasi, akurasi, dan citra hasil deteksi. Hasil penelitian menunjukkan dari tiga skema model perhatian CNN yang telah memberikan performa tertinggi pada skema satu dengan akurasi 74,17% dan kappa 72,84%, disusul secara berurutan yaitu skema kedua dengan akurasi 72,50% dan nilai kappa 70%, serta skema ketiga dengan akurasi 70% dan nilai kappa 67,27%. Temuan ini menegaskan efektivitas CNN dalam mengekstraksi fitur spasial citra gulma berkhasiat obat dan memberikan kontribusi signifikan sebagai langkah awal menuju digitalisasi botani dan pengembangan fitofarmaka berbasis gulma obat.
AKTIVITAS ANTIBAKTERI EKSTRAK DAN FRAKSI DAUN GELINGGANG TERHADAP BAKTERI Propionibacterium Fildayanti Fildayanti; Herlina Ekapratama; Rika Melati
Jurnal Penelitian Farmasi Indonesia Vol 15 No 1 (2026): Jurnal Penelitian Farmasi Indonesia (JPFI)
Publisher : Pusat Penelitian dan Pengabdian Masyarakat (P3M) Sekolah Tinggi Ilmu Farmasi Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51887/jpfi.v15i1.2206

Abstract

Gelinggang leaves (Senna alata (L.) Roxb.) have traditionally been used to treat skin diseases such as itching, ringworm, scabies, tinea versicolor, and acne. One of the main bacteria involved in acne pathogenesis is Propionibacterium acnes. Previous studies reported strong antibacterial activity of ethanol extracts of gelinggang leaves; however, comparative data between ethanol extract and its fractions remain limited. This study aimed to evaluate the antibacterial activity of the 96% ethanol extract, n-hexane fraction, and ethyl acetate fraction of gelinggang leaves against Propionibacterium acnes. This experimental laboratory research used the disk diffusion method at concentrations of 5%, 7.5%, 10%, 12.5%, and 15%. Clindamycin 1% served as a positive control, while DMSO was used as a negative control. Phytochemical screening was conducted to identify secondary metabolites. Results showed that the ethanol extract and ethyl acetate fraction contained alkaloids, flavonoids, saponins, and tannins, whereas the n-hexane fraction contained only tannins. The highest antibacterial activity was found in the ethyl acetate fraction with an inhibition zone of 6.65±0.69 mm, followed by the ethanol extract (5.87±0.31 mm) and the n-hexane fraction (3.69±0.47 mm) at 15% concentration. Based on inhibition classification, the ethyl acetate fraction showed moderate antibacterial activity. These findings indicate that the ethyl acetate fraction has potential as a natural antibacterial agent against Propionibacterium acnes due to its ability to extract semi-polar active compounds
Co-Authors Fildayanti Fildayanti Herlina Ekapratama Kusnadi Kusnadi Qamarudin Arrasyid