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All Journal Jambura Journal of Mathematics
Mochamad Jamil
Program Studi Manajemen, Sekolah Tinggi Ilmu Ekonomi Indonesia (STIESIA) Surabaya,
Mathematics

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Analisis Dampak COVID-19 dan Faktor-Faktor yang Mempengaruhi Indeks Saham Bank Jatim Menggunakan Pendekatan Regresi Time Series Muktar Redy Susila; Mochamad Jamil; Bambang Hadi Santoso
Jambura Journal of Mathematics Vol 4, No 2: July 2022
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1277.936 KB) | DOI: 10.34312/jjom.v4i2.13401

Abstract

Stock is one of the instruments that can be used to invest. The factors suspected of influencing the fluctuations in the stock price index are the Jakarta Composite Index (JCI), the rupiah exchange rate, and the Covid-19. The purpose of this study is to analyze the effect of the JCI, the rupiah exchange rate, and the Covid-19 on the Jatim Bank stock price index. To analyze the effect of the JCI, the rupiah exchange rate, and the Covid-19 on the Jatim Bank stock price index, time series regression analysis was used. The results of the analysis show that the JCI, the rupiah exchange rate, and the Covid-19 have a significant effect on the Jatim Bank stock price index. The p-value of the three variables is less than 0.05. The value of the determination coefficient is 65.56%, so that the diversity of the dependent variable that can be explained by the model is 65.56%.
Akurasi Model Hybrid ARIMA-Artificial Neural Network dengan Model Non Hybrid pada Peramalan Peredaran Uang Elektronik di Indonesia Muktar Redy Susila; Mochamad Jamil; Bambang Hadi Santoso
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1973.518 KB) | DOI: 10.34312/jjom.v5i1.14889

Abstract

The purpose of this study is to model electronic money in Indonesia using a hybrid model and compare its accuracy with the non-hybrid model. The hybrid model used is Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network. The data used is the amount of electronic money circulation for the monthly period January 2009 to October 2021. The ARIMA model formed from research data is ARIMA (1,1,0) with additive outliers and level shift outliers. For Artificial Neural Network modeling is limited by using one hidden layer with three neurons. In the modeling process, 20 repetitions were carried out. The smallest repetition value was obtained, namely the 13th repetition with an error value of 2.569. In this study, it was found that the ARIMA- Artificial Neural Network hybrid model had a smaller Root Mean Squared Error (RMSE) in sample and out sample than the non-hybrid model. Based on the results of the study, it can be concluded that by combining the ARIMA model with Artificial Neural Network, it can increase the accuracy of the data fit results and forecast results.
Co-Authors Bambang Hadi Santoso Bambang Hadi Santoso Susila, Muktar Redy