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All Journal Journal of Mathematical and Fundamental Sciences Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Jurnal Eurekamatika Basis : Jurnal Ilmiah Matematika Al-Aqlu : Jurnal Matematika, Teknik dan Sains Indonesian Journal of Applied Mathematics Journal of Actuarial, Finance, and Risk Managment TeknoKreatif: Jurnal Pengabdian kepada Masyarakat
Rifky Fauzi
Program Studi Matematika, Jurusan Sains, Institut Teknologi Sumatera
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Mathematics Physics Other

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Journal : Indonesian Journal of Applied Mathematics

 1 
Simulasi Terbentuknya Gelombang Permukaan Akibat Adanya Longsoran Bawah Laut (Metode Lax- Friedrich) Yenci Brika Enkekes; Rifky Fauzi
Indonesian Journal of Applied Mathematics Vol 2 No 2 (2023): Indonesian Journal of Applied Mathematics Vol. 2 No. 2 January Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/indojam.v2i2.530

Abstract

The problem studied in this paper is the formation of surface waves due to underwater landslides. The shallow water wave equation is employed to study the phenomenon. The equation is solved numerically by using the Lax-Friedrich method. The numerical result show that this method is able to simulate the generation of surface wave due to underwater landslide.
Analisis Prediksi Data Kasus Covid-19 di Provinsi Lampung Menggunakan Recurrent Neural Network (RNN) Akhdan Aziz Ghozi; Ayu Aprianti; Ahmad Dzaki Putra Dimas; Rifky Fauzi
Indonesian Journal of Applied Mathematics Vol 2 No 1 (2022): Indonesian Journal of Applied Mathematics Vol. 2 No. 1 April Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/indojam.v2i1.763

Abstract

This study aims to examine the architectural performance of the Recurrent Neural Network (RNN) model in predicting Covid-19 cases in Lampung Province. The RNN method is part of Deep Learning which will be used to model data on Covid-19 cases in Lampung Province from March 26, 2020 to March 28, 2021. The RNN model was chosen because the Covid-19 data is in the form of a time series and the advantages of RNN are that it can capture information on the data time series using multiple network layers which allow better modeling and resulting in high prediction accuracy. The data is divided into 3, namely active cases, recovered cases, and dead cases. After preparing the data, the 368 data were divided into 294 initial latih data and 74 test data. After latih on the data for each data, then a test is carried out on the data for each data as a reference for predicting the latest data. The most optimal results show the cumulative active case model with RMSE=0.0022; for cumulative recovery cases obtained RMSE = 0.0007; while the cumulative death cases obtained RMSE = 0.0012. Based on the modeling error, then make predictions on the three cases which results in RMSE = 0.001 for cumulative active cases; RMSE=0.0027 for cumulative recovery cases; and RMSE=0.001 for cumulative death cases.
Co-Authors Ahmad Dzaki Putra Dimas Akhdan Aziz Ghozi Aliffia, Arsy Alkautsar, Muhammad Ammar Ayu Aprianti Dani Al Mahkya Dear Michiko Mutiara Noor Diana, Ulfa Edriani, Tiara Shofi Edwin Setiawan Nugraha Edy Soewono Faiqoh, Maya Himmah Gres Maretta, Gres Listriani, Amalia Lovena Louisa Mahrani, Dwi Mufidah, Zunanik Pribadi, Aswan Anggun Rizka, Nela Saefudin Saefudin Sri Haryani Styaningrum, Amalia Tiara Shofi Edriani Utami, Riani Utriweni Mukhaiyar Yenci Brika Enkekes