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All Journal Amina Infolitika Journal of Data Science
Muslem Muslem
Department Of Chemistry, Faculty Of Science And Technology, Universitas Islam Negeri Ar-Raniry, Banda Aceh 23111, Indonesia
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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AKTIVITAS SEDIAAN GEL ANTIJERAWAT DARI EKSTRAK ETANOL DAUN SALAM (Syzygium polyanthum (Wight) Walp.) TERHADAP BAKTERI Staphylococcus epidermidis Reni Silvia Nasution; Muslem Muslem; Salmah Nasution
AMINA Vol 5 No 1 (2023): April 2023
Publisher : Department of Chemistry, Faculty of Science and Technology, UIN Ar-Raniry

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Abstract

Gel antijerawat merupakan sediaan farmasi yang digunakan untuk mengobati jerawat dengan menurunkan komedo dan membantu pengelupasan sel kulit mati. Daun salam memiliki aktivitas antibakteri yang dapat dibuat dalam bentuk sediaan farmasi gel. Tujuan dari penelitian ini adalah untuk mengetahui aktivitas antibakteri gel antijerawat dari konsentrasi paling efektif ekstrak etanol daun salam terhadap bakteri Staphylococcus epidermidis melalui metode difusi. Gel antijerawat dipreparasi dengan variasi konsentrasi ekstrak etanol daun salam yaitu 0%; 1,5%; 2%; 2,5% dan 3%. Gel yang dihasilkan dilakukan beberapa pengujian seperti uji organoleptis, homogenitas, pH, viskositas, daya lekat, daya sebar dan antibakteri. Aktivitas antibakteri terhadap Staphylococcus epidermidis dihasilkan dari formula yang mengandung 3% ekstrak etanol daun salam. Daya hambat formula kuat dengan diameter zona hambat sebesar 16,5 mm. Hasil ini menunjukkan bahwa sediaan farmasi gel yang mengandung 3% ekstrak etanol daun salam berpotensi untuk dijadikan gel antijerawat.
ANFIS-Based QSRR Modelling for Kovats Retention Index Prediction in Gas Chromatography Idroes, Rinaldi; Noviandy, Teuku Rizky; Maulana, Aga; Suhendra, Rivansyah; Sasmita, Novi Reandy; Muslem, Muslem; Idroes, Ghazi Mauer; Jannah, Raudhatul; Afidh, Razief Perucha Fauzie; Irvanizam, Irvanizam
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.73

Abstract

This study aims to evaluate the implementation and effectiveness of the Adaptive Neuro-Fuzzy Inference System (ANFIS) based Quantitative Structure Retention Relationship (QSRR) to predict the Kovats retention index of compounds in gas chromatography. The model was trained using 340 essential oil compounds and their molecular descriptors. The evaluation of the ANFIS models revealed promising results, achieving an R2 of 0.974, an RMSE of 48.12, and an MAPE of 3.3% on the testing set. These findings highlight the ANFIS approach as remarkably accurate in its predictive capacity for determining the Kovats retention index in the context of gas chromatography. This study provides valuable perspectives on the efficiency of retention index prediction through ANFIS-based QSRR methods and the potential practicality in compound analysis and chromatographic optimization.
Co-Authors Ghazi Mauer Idroes Irvanizam, Irvanizam Maulana, Aga Raudhatul Jannah Razief Perucha Fauzie Afidh Reni Silvia Nasution Rinaldi Idroes Salmah Nasution Sasmita, Novi Reandy Suhendra, Rivansyah Teuku Rizky Noviandy