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All Journal Indonesian Journal of Applied Mathematics
Ayu Aprianti
Program Studi Matematika, Jurusan Sains, Institut Teknologi Sumatera
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Physics

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Analisis Faktor Indeks Harga Konsumen (IHK) Menurut Kelompok Pengeluaran yang Mempengaruhi Laju Inflasi Provinsi Lampung Tahun 2020 Ayu Aprianti; Tiara Shofi Edriani
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.v2i2.1028

Abstract

The Consumer Price Index (CPI) is used as the basis for calculating regional inflation rates. The balance of the CPI variable by Expenditure Group (CPI-EG) needs to be considered so that it does not have a major impact on the inflation rate. The purpose of this study is to determine the CPI-EG variable that affects the inflation rate in Lampung Province using the factor analysis method through the extraction technique of Principal Component Analysis. Factor analysis aims to reduce the CPI-EG variable into a factor with a smaller number. Based on the result of factor analysis there are 3 CPI-EG variables that were reduced because they did not meet the feasibility test and 8 CPI-EG variables that could be analyzed further. These variables form 1 factor which is named the Community Supporting Needs Factor. These factors can explain 79,304% of the total variance. Of the 8 CPI-EG variables, the Health Variable is the most dominant effect on the inflation rate in Lampung Province with a strong correlation of 0,980. This inflation rate is due to public health needs due to the increase in Covid-19 cases in Lampung Province in 2020.
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 Rifky Fauzi Tiara Shofi Edriani