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All Journal VALENSI Jurnal Ilmiah Kursor Jurnal Matematika Integratif
Sri Purwani
Departemen Matematika FMIPA Universitas Padjadjaran
Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering Transportation

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Molecular Docking on Kokosanolide A and C for Anticancer Activity Against Human Breast Cancer Cell MCF-7 Sri Purwani; Julita Nahar; Zulfikar Zulfikar; Nurlelasari Nurlelasari; Tri Mayanti
Jurnal Kimia Valensi Jurnal Kimia VALENSI Volume 7, No. 1, May 2021
Publisher : Syarif Hidayatullah State Islamic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jkv.v7i1.20534

Abstract

Kokosanolide A (1), from the seeds of Lansium domesticum Corr. cv Kokossan, has been shown strong cytotoxic activities (IC50 = 8.62 μg/mL) against MCF-7 breast cancer cells. The aim of this work was to study the molecular interactions of kokosanolide A and kokosanolide C with the Estrogen Receptor α (ERα) using computer-aided drug design approaches. Molecular docking using Autodock Vina (open-source software PyRx 0.8) was employed to explore the modes of binding of kokosanolide A (1) and kokosanolide C (2) with ERα. Compounds 1 and 2 showed strong bond-free energy (-8.8 kcal/mol and -8.7 kcal/mol) to ERα. These two compounds have a molecular mechanism to inhibit ERα in breast cancer cells.
A COMBINATION DEEP BELIEF NETWORKS AND SHALLOW CLASSIFIER FOR SLEEP STAGE CLASSIFICATION Intan Nurma Yulita; Rudi Rosadi; Sri Purwani; Rolly Maulana Awangga
Jurnal Ilmiah Kursor Vol 8 No 4 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i4.97

Abstract

In this research, it is proposed to use Deep Belief Networks (DBN) in shallow classifier for the automatic sleep stage classification. The automatic classification is required to minimize Polysomnography examination time because it needs more than two days for analysis manually. Thus the automatic mechanism is required. The Shallow classifier used in this research includes Naïve Bayes (NB), Bayesian Networks (BN), Decision Tree (DT), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN). The results obtained that many methods of the shallow classifier are increasing precision, recall, and F-Measure if they use DBN output as input for classification. Experiments that have been done indicate a significant increase of Naive Bayes after being combined with DBN. The high-level features generated by DBN are proven to be useful in helping Naive Bayes' performance. On the other hand, the combination of KNN with DBN shows a decrease because high-level features of DBN make it harder to find neighbors that optimize the performance of KNN.
Analisis Dinamik Penyebaran Covid-19 dengan Faktor Vaksinasi dengan menggunakan Metode Runge-Kutta Fehlberg Rizky Ashgi; Sri Purwani; Nursanti Anggriani
Jurnal Matematika Integratif Vol 18, No 2: Oktober 2022
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (349.962 KB) | DOI: 10.24198/jmi.v18.n2.40224.115-126

Abstract

Penyakit Covid-19 merupakan penyakit yang sedang mewabah pada saat ini, hampir seluruh dunia terkena dan meninggal diakibatkan oleh penyakit Covid-19, berbagai cara dilakukan untuk mencegah penularan salah satunya dengan program vaksinasi. Kemudian ada upaya memperhitungkan kapan akan berakhirnya penyakit Covid-19 di suatu wilayah populasi. Hal ini bersesuain dengan bidang matematika epidemiologi yaitu pemodelan matematika yang dapat memprediksi kapan berkahirnya penyakit Covid-19 di suatu wilayah, model matematika yang telah dibuat sebelumnya yaitu model Susceptible-Infected-Recovered (SIR). Dari model tersebut dapat dikembangkan lagi dengan menambahkan faktor Exposed menjadi model Susceptible-Exposed-Infected-Recovered (SEIR), atau faktor Deceased sehingga menjadi model Susceptible-Infected-Deceased-Recovered (SIDR), atau faktor Vaccinated sehingga menjadi model Susceptible-Vaccinated-Infected-Recovered (SVIR). Pada penelitian ini kasus penyakit Covid-19 di analisis dengan menentukan titik equilibrium dan basic reproduction number (R0) sedangkan analisis numeriknya dengan menggunakan metode Runge-Kutta Fehlberg dalam model penyebaran penyakit Covid-19. Penelitian ini akan mengembangkan model SVIR dengan melibatkan faktor vaksinasi. Penelitian ini bertujuan untuk mengetahui model matematika yaitu model SVIR pada penyebaran penyakit Covid-19, titik equilibrium model SVIR pada penyebaran penyakit Covid-19, basic reproduction number (R0) model SVIR pada penyebaran penyakit Covid-19, solusi numerik metode Runge-Kutta Fehlberg pada penyebaran penyakit Covid-19, dan efektivitas model SVIR pada penyebaran penyakit Covid-19. Kata kunci:  Covid-19, Metode Runge-Kutta Fehlberg, model SVIR.
Co-Authors Intan Nurma Yulita Julita Nahar Nurlelasari Nurlelasari Nursanti Anggriani Rizky Ashgi Rizky Ashgi Rolly Maulana Awangga, Rolly Maulana Rudi Rosadi Tri Mayanti Zulfikar Zulfikar