Analisis Dampak COVID-19 dan Faktor-Faktor yang Mempengaruhi Indeks Saham Bank Jatim Menggunakan Pendekatan Regresi Time Series

Susila, Muktar Redy; Santoso, Bambang Hadi

doi:10.34312/jjom.v4i2.13401

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Mendiskripsikan data jumlah peredaran uang elektronik periode bulanan di Indonesia. Untuk mendiskripsikan data tersebut, digunakan time series plot untuk melihat pola data seperti pada [14] dan statistik deskriptif untuk melihat karakteristik data seperti yang terdapat pada [15]. 2.…”

Section: Langkah Analisisunclassified

Akurasi Model Hybrid ARIMA-Artificial Neural Network dengan Model Non Hybrid pada Peramalan Peredaran Uang Elektronik di Indonesia

Susila

Santoso

2023

Jambura J. Math

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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.

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Section: Langkah Analisisunclassified

Akurasi Model Hybrid ARIMA-Artificial Neural Network dengan Model Non Hybrid pada Peramalan Peredaran Uang Elektronik di Indonesia

Susila

Santoso

2023

Jambura J. Math

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“…Terdapat beberapa faktor yang menarik daya minat investor untuk berinvestasi pada saham. Pergerakan saham setiap periode waktu mengalami fluktuasi, indeks harga saham sekarang akan berbeda dengan indeks harga saham periode sebelumnya (Susila et al, 2022). Perbedaan harga tersebut bisa dimungkinkan akan mengakibatkan peningkatan indeks harga saham (capital gain).…”

Section: Pendahuluanunclassified

Dampak COVID-19 Terhadap Hasil Analisis Kelayakan Investasi pada Saham Perusahaan BUMN

Susila

2023

E-Jurnal Akuntansi

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The aim of the research is to analyze the impact of COVID-19 on the stock performance of State-Owned Enterprises (BUMN). The research sample is the shares of BUMN companies. The sampling technique used in this research is purposive sampling. The number of samples that met the research criteria were 19 state-owned companies. The method used to analyze the CAPM. The results of the analysis prior to COVID-19 found that five state-owned companies were efficient shares. The results of the analysis at the time of COVID-19 obtained that fifteen BUMN companies were efficient shares. The results of the different tests on the performance of BUMN shares before and during COVID-19 concluded that there were differences. The results of the study show that with COVID-19 the feasibility of state-owned company shares has changed.Keywords : Stocks; COVID-19; CAPM; State-Owned Companies

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“…To find out the long-term prediction of water levels, machine learning can be used. The machine learning used is a time series regression (Susila et al, 2022) with the output in the form of river water level data. As for the input to predict the level of river water, namely rainfall.…”

Section: Introductionmentioning

confidence: 99%

Smart Disaster Mitigation: The Role of IoT and Machine Learning

Nur Laily,

Juwita Sari,

Muktar Redy Susila

2023

int.jour.sci.res.mana

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This prototype output research has the aim of creating a dashboard that is used to monitor river water levels. The dashboard created will display data in real-time and prediction results. The use of the dashboard is to minimize the risk in the event of flooding caused by river overflow. The way this prototype works is to take data from sensors that have been installed at several points. The recorded data will be stored in a database using the working principles of the Internet of Things. For predictions, machine learning is used to produce future river water level figures. The machine learning used is using time series regression with rainfall input and river water level output. Long-term output data is needed, therefore to forecast future rainfall the Hybrid method is used. Data generated from sensors as well as from prediction results are stored in one database. From the database, data visualization is displayed along with important figures used for river overflow intelligence. Therefore, the dashboard is very useful for people living around the river flow.

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Analisis Dampak COVID-19 dan Faktor-Faktor yang Mempengaruhi Indeks Saham Bank Jatim Menggunakan Pendekatan Regresi Time Series

Cited by 4 publications

References 7 publications

Akurasi Model Hybrid ARIMA-Artificial Neural Network dengan Model Non Hybrid pada Peramalan Peredaran Uang Elektronik di Indonesia

Akurasi Model Hybrid ARIMA-Artificial Neural Network dengan Model Non Hybrid pada Peramalan Peredaran Uang Elektronik di Indonesia

Dampak COVID-19 Terhadap Hasil Analisis Kelayakan Investasi pada Saham Perusahaan BUMN

Smart Disaster Mitigation: The Role of IoT and Machine Learning

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