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All Journal Building of Informatics, Technology and Science CERATA
Sari, Ely Novita
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Computer Science & IT Medicine & Pharmacology

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Uji Efektivitas Antibakteri Pasta Gigi Ekstrak Daun Peppermint (Mentha piperita L) dan Ekstrak Daun Sirih Merah (Piper crocatum) terhadap Streptococcus mutans Hidayati, Nurul; Sari, Ely Novita; Budiman, Hendra; Handayani, Sri
CERATA Jurnal Ilmu Farmasi Vol 14 No 2 (2023): Cerata Jurnal Ilmu Farmasi
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/cerata.v14i2.868

Abstract

Karies gigi adalah proses perlahan demineralisasi jaringan gigi yang disebabkan oleh bakteri kariogenik penghasil asam melalui fermentasi karbohidrat, yang dapat mengakibatkan penurunan pH saliva yaitu Streptococcus mutans. Penelitian ini bertujuan untuk membuat sediaan pasta gigi ekstrak daun peppermint dan ekstrak daun sirih merah kemudian dilakukan pengujian ektivitas pasta gigi sebagai antibakteri terhadap Streptococcus mutans.Penelitian ini merupakan penelitian experimental yang diawali dengan pembuatan sediaan pasta gigi ekstrak daun peppermint (Mentha piperita L) dan ekstrak daun sirih merah (Piper crocatum) variasi konsentrasi 15%: 5%, 10%: 10%, 5%: 15%. Pasta gigi dievaluasi dengan uji organoleptiis, homogenitas, uji ph, uji viskositas, dan uji efektivitas antibakteri. Hasil menunjukkan pasta gigi memenuhi standar penerimaan dari segi organoleptis, homogenitas, viskositas, dan pH. Hasil uji efektivitas antibakteri pasta gigi ekstrak daun peppermint dan ekstrak daun sirih merah dengan variasi konsentrasi 15%: 5%, 10%: 10%, 5%: 15% menunjukkan efektif menghambat Streptococcus mutansdengan zona hambat 11,66 mm, 16,5 mm dan 18,83 mm
Klasifikasi Jenis Bunga Iris Berdasarkan Fitur Morfologi Menggunakan Algoritma Naive Bayes Sari, Ely Novita; Irmayani, Deci; Bangun, Budianto
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7401

Abstract

This study aims to classify the types of Iris flowers based on morphological features using the Naive Bayes algorithm. Iris flowers consist of three types, namely Iris-Setosa, Iris-Versicolor, and Iris-Virginica, which can be distinguished based on the length and width of the petals as well as the length and width of the sepals. The dataset used in this research is the Iris dataset, which contains information on four morphological features from these three types of flowers. The Naive Bayes algorithm was chosen because of its advantages in performing probability-based classification in a simple, fast, and effective manner, especially for data with independent features. The research stages include data collection, feature extraction, splitting the data into training and testing sets, training the model using the Naive Bayes algorithm, and testing the model to evaluate classification accuracy. The results of the study show that the Naive Bayes model is able to classify the test data accurately, with the highest probability value obtained in the Iris-Versicolor class, with a value of P(Versicolor│X)=1. This indicates that the test data has the highest similarity to that species compared to the other two species. Thus, the Naive Bayes algorithm proves effective for classifying types of Iris flowers based on their morphological features.
Co-Authors Bangun, Budianto Hendra Budiman Irmayani, Deci Nurul Hidayati Sri Handayani