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Irwan Irwan
Department Of Mathematics, Universitas Negeri Makassar, Makassar, 90223, Indonesia
Religion Humanities Biochemistry, Genetics & Molecular Biology Chemistry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences

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ESTIMATING AND FORECASTING COVID-19 CASES IN SULAWESI ISLAND USING GENERALIZED SPACE-TIME AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL Sukarna Sukarna; Nurul Fadilah Syahrul; Wahidah Sanusi; Aswi Aswi; Muhammad Abdy; Irwan Irwan
MEDIA STATISTIKA Vol 15, No 2 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.2.186-197

Abstract

A range of spatio-temporal models has been used to model Covid-19 cases. However, there is only a small amount of literature on the analysis of estimating and forecasting Covid-19 cases using the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model. This model is a development of the GSTARMA model which has non-stationary data. This paper aims to estimate and forecast the daily number of Covid-19 cases in Sulawesi Island using GSTARIMA models. We compared two models namely GSTARI and GSTIMA considering the root mean square error (RMSE). Data on a daily number of Covid-19 cases (from April 10, 2020, to May 07, 2021) were used. The location weight used is the inverse distance weight based on the distance between airports in the capital cities of each province. The appropriate models obtained based on the data are the GSTARIMA (1;0;1;1) model and the GSTARIMA (1;1;1;0) model. The results showed that the forecast for the number of new Covid-19 cases is accurate and reliable only for the short term.
Rainfall Forecasting in Makassar City Using Triple Exponential Smoothing Method Irwan Irwan; Muhammad Abdy; Ersa Karwingsi; Ansari Saleh Ahmar
ARRUS Journal of Social Sciences and Humanities Vol. 3 No. 1 (2023)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/soshum1707

Abstract

The purpose of this study is to determine the appropriate forecasting method for predicting rainfall in Makassar City in 2022. This research is based on the problems that are often experienced by the people of Makassar City, namely the occurrence of flooding which results in traffic jams due to high x rainfall which continuously occurs. for several days in a row. With this research, people can see the prediction of rainfall and anticipate flooding in Makassar City. The results of the Makassar city rainfall data plot experience increases and decreases (fluctuations), which tend to repeat every year. This shows that Makassar City's rainfall contains seasonal factors. Therefore the method used is one-parameter Brown triple exponential smoothing, three-parameter Holt-Winters additive and Holt-Winters multiplicative. The forecasting results show that the correct method to use is the Holt-Winters multiplicative method with a parameter value of a=0.001 β=0.15 γ=0.002 which produces a minimum value of MAPE = 1.18 MAD = 136.23 compared to the Brown and Holt-Winters methods additive. Forecasting results using the Holt-Winters multiplicative method show that the highest rainfall occurs from December to April 2022.
The Implementation of Spatial Model with K-Means Clustering Method to Cluster Flood Affected Areas in Bone Regency Irwan Irwan; Wahidah Sanusi; Adil Saputra Anwar; Abdul Rahman
ARRUS Journal of Social Sciences and Humanities Vol. 3 No. 2 (2023)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/soshum1771

Abstract

This research is an applied research that aims to determine the clusters of areas affected by floods in bone regency. This study user the K-means method in clustering flood data in 2020 and 2021. The data is grouped based on the number of families affected and the resulting damage. In determining the number of clusters to be used, this study validated 3, 4 and 5 clusters using the davis bouldin index (DBI) validation in determining the best cluster. The results of this validation resulted in the best number of clusters, namely k=4 for 2020 data with a minimum DBI of 0,73 and k=4 for 2021 data with a minimum DBI of 0,44. After clustering, the cluster number for data for 2020 from the firs to fourth cluster member are 34, 7, 2 and 3 sequentially, while the data for years 2021 sequentially are 3, 22, 3 and 1. Then the cluster results are displayed in a spatial form that is created using ArcGIS.
Analysis of Stock Portfolio Optimization in the Telecommunications Sector Using the Single Index Model Irwan Irwan; Muhammad Abdy; Nurul Khofifah Salsabila; Ansari Saleh Ahmar
ARRUS Journal of Mathematics and Applied Science Vol. 3 No. 1 (2023)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience1734

Abstract

The purpose of this study was to determine the optimal portfolio in the telecommunications sector listed on the Indonesia Stock Exchange based on the Jakarta Composite Index for the period January 2018–December 2020 using the Single Index Model. This type of research is an applied research. This type of research is applied research with secondary data obtained from the Indonesia Stock Exchange, Yahoo Finance, and Bank Indonesia. The number of samples taken is 5 stocks, namely TLKM, ISAT, EXCL, BTEL, and FREN. Based on the results of the analysis of the 5 stocks that are members of the JCI, the combination of 2 stocks that make up the optimal portfolio, namely ISAT and FREN, produces an expected return of 5.08% with a risk of 8.02%.
Co-Authors Adil Saputra Anwar Ahmar, Ansari Saleh Aswi, Aswi Ersa Karwingsi Muhammad Abdy Muhammad Abdy Nurul Fadilah Syahrul Nurul Khofifah Salsabila Rahman, Abdul Sukarna Sukarna Wahidah Sanusi Wahidah Sanusi