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All Journal The Asian Journal of Technology Management (AJTM) MaPan : Jurnal Matematika dan Pembelajaran Mattawang: Jurnal Pengabdian Masyarakat JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies Panrannuangku Jurnal Pengabdian Masyarakat EduLine: Journal of Education and Learning Innovation ARRUS Journal of Social Sciences and Humanities SAINSMAT: Journal of Applied Sciences, Mathematics, and Its Education Issues in Mathematics Education (IMED) Amaliah: Jurnal Pengabdian Masyarakat Seminar Nasional Pengabdian Kepada Masyarakat SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam ARRUS Jurnal Pengabdian Kepada Masyarakat
Rahman, Abdul
Department Of Mathematics, Universitas Negeri Makassar, Makassar, 90223, Indonesia
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Journal : Quantitative Economics and Management Studies

 1 
Implementation of Exponential Smoothing in Forecasting the Export Value Price of Oil and Gas in Indonesia Ansari Saleh Ahmar; Abdul Rahman; Sitti Masyitah Meliyana R.; Rusli Rusli; Nachnoer Arss; Alok Kumar Panday
Quantitative Economics and Management Studies Vol. 4 No. 4 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.qems1022

Abstract

This study aims to predict the value of oil and gas export prices in Indonesia using exponential smoothing. Exponential smoothing was applied because the data analysis revealed that the data consisted of trends and seasonal components. This study uses secondary data obtained from the website of the Central Bureau of Statistics of the Republic of Indonesia, covering the value of oil and gas exports in Indonesia every month from January 2010 to March 2022. The study obtained the exponential smoothing parameters, including α = 0.5153984, β = 0.06410119, and g = 0.7137603, with a seasonal length of L = 12. The forecast for the next five periods in millions of US$: April 2022 (1111.765), May 2022 (1250.465), June 2022 (1405.016), July 2022 (1447.510), and August 2022 (1452.984).
Forecasting the Export Value of Oil and Gas in Indonesia using Autoregressive Integrated Moving Average (ARIMA) Ansari Saleh Ahmar; Abdul Rahman; Parkhimenko Vladimir Anatolievich; Rusli Rusli; Sitti Masyitah Meliyana R.
Quantitative Economics and Management Studies Vol. 4 No. 5 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku1040

Abstract

This study aims to utilize the ARIMA method to predict the value of Indonesia's oil and gas exports. As quantitative research, it employs secondary data sourced from the Central Bureau of Statistics of the Republic of Indonesia's website. The data spans January 2010 to March 2022 and are presented on a monthly basis. Through the results and discussion, three ARIMA models were established, namely ARIMA (1,1,0), ARIMA (0,1,1), and ARIMA (1,1,1). Among these models, the ARIMA (0,1,1) model with an AIC value of 2047.65 was found to be the most suitable for forecasting Indonesia's oil and gas exports. The forecasted values for the next five periods were 1254.124 (April 2022), 1309.678 (May 2022), 1289.236 (June 2022), 1296.758 (July 2022), and 1293.990 (August 2022).
The Implementation of Holt-Winters Method to Forecast the Loan Interest Rate of Indonesia Ansari Saleh Ahmar; Abdul Rahman; Mohd. Rizal Mohd. Isa; Rahmat Hidayat
Quantitative Economics and Management Studies Vol. 5 No. 3 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.qems2718

Abstract

This study aimed to anticipate the rupiah loan interest rates at commercial banks in Indonesia by employing the Holt-winters method. This study employs data on rupiah loan interest rates from commercial banks in Indonesia. The data comprises a time series element, with monthly intervals spanning from January 2013 to November 2015, which was obtained from the official website of BPS Indonesia. The study demonstrates that the Holt-winters technique yields the most accurate forecasts, as indicated by a Root Mean Square Error (RMSE) of 0.19720630. The parameters alpha, beta, and gamma, set at 0.6, 0.6, and 0.6 respectively, constitute the optimal configuration for this method. These results indicate that the Holt-winters method is an effective tool for capturing seasonality, trends, and patterns in credit interest rate data, making it a reliable choice for future loan interest rate forecasting. The findings of this study are expected to significantly contribute to strategic decision-making in the banking sector, particularly in risk management and loan interest rate strategy determination.
Co-Authors Adil Saputra Anwar Ahmad Talib Ahmar, Ansari Saleh Alfrida Mambaya Alok Kumar Panday Annisa Audiningrum Sutarman Asdar . Assagaf, Said Fachry Aswi, Aswi B. Bernard Baso Intang Sappaile Chusnul Chatimah Azis Djadir Djadir Dodi Sukmayadi Dody Sukmayadi Edi Syukri Enni Siswati Fakhiratunnisa Fakhiratunnisa Fathrul Arriah Gusriani Hamzah Upu Hamzah Upu Hastuty Hastuty Hastuty Hastuty Hastuty Hastuty Hastuty Musa Hastuty Musa Hisyam Ihsan Ilham Minggi Irwan Irwan Isma Muthahharah Lince, Ranak Miguel Botto-Tobar Mohd. Rizal Mohd. Isa Muh. Hidayatullah Dwi Putra Muhammad Ammar Naufal Nachnoer Arss Nur Aynun Nur Ikhsan Ismail Nurhikmah Nurhikmah Parkhimenko Vladimir Anatolievich R. Rusli R. Rusli R. Rusli Rahmat Hidayat Rahmat Kamaruddin Rosmala Rosmala Ruslan Ruslan Rusli Rusli Rusli Rusli Rusli Rusli Rusli Sahid Setiawan Madya Sitti Masyitah Meliyana R. Sukarna Sukarna Suroyo Suroyo Sutamrin, Sutamrin Tahmir, Suradi Tajuddin Tajuddin Wahidah Sanusi