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All Journal EIGEN MATHEMATICS JOURNAL Mattawang: Jurnal Pengabdian Masyarakat JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies Panrannuangku Jurnal Pengabdian Masyarakat Daengku: Journal of Humanities and Social Sciences Innovation ARRUS Journal of Social Sciences and Humanities ARRUS Journal of Mathematics and Applied Science Ininnawa: Jurnal Pengabdian Masyarakat ARRUS Jurnal Pengabdian Kepada Masyarakat Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M) Jurnal MediaTIK Journal of Mathematics, Computation and Statistics (JMATHCOS)
Sitti Masyitah Meliyana R.
Universitas Negeri Makassar
Religion Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Public Health Social Sciences Other

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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).
Regression Analysis of Panel Data on Gross Enrolment Rate (GER) At Junior High School and Equivalent Education Levels in South Sulawesi Province in 2018-2022 Elisa, Nur; Aidid, Muhammad Kasim; Meliyana, Sitti Masyitah
Quantitative Economics and Management Studies Vol. 6 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.qems3932

Abstract

Panel data regression is a combination of time series and cross section data. This research aims to determine the factors that influence the gross participation rate in South Sulawesi Province using panel data regression analysis. The data used is data from 24 districts/cities in South Sulawesi province from 2018 to 2022 which was obtained through the website of the South Sulawesi Provincial Central Statistics Agency. There are three models in panel data regression analysis, namely the Common Effect Model (CEM), Fixed Effect Model (FEM) and Random Effect Model (REM). Based on the model selection carried out by carrying out the Chow Test, Hausman Test, and Lagrange Multiplier Test, the best model was obtained, namely the Random Effect Model. The equation of this model is Yit = 82,818 + 0,1485X1it − 0,0784X2it + 0,0053X3it + 0,0011X4it. Based on the results of panel data regression analysis, it was found that the variables that had a significant effect on the Gross Enrollment Rate in South Sulawesi province were the student to teacher ratio (X2), and population density (X4).
A Seasonal ARIMA (SARIMA) Model for Forecasting Domestic Passenger Traffic at Sultan Hasanuddin Airport Meliyana, Sitti Masyitah; Hafid, Hardianti; Mar'ah, Zakiyah; Muthahharah, Isma
Quantitative Economics and Management Studies Vol. 6 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.qems3935

Abstract

The growth of the domestic aviation industry in Indonesia has led to a significant increase in passenger numbers, particularly at major airports such as Sultan Hasanuddin Airport. Accurate forecasting of passenger traffic is essential for effective planning and resource allocation. This study aims to develop a suitable time series model to forecast the number of domestic air passengers departing from Sultan Hasanuddin Airport. Using monthly passenger data from January 2019 to April 2024 obtained from the Indonesian Badan Pusat Statistik (BPS), the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied. The modelling process followed the Box-Jenkins methodology, involving data exploration, stationarity testing, model identification, parameter estimation, diagnostic checking, and model validation. Among several candidate models, the ARIMA (0,1,1)(0,0,1)12 model was identified as the most appropriate, producing normally distributed, independent residuals and yielding a Mean Absolute Percentage Error (MAPE) of 4.5%. The results demonstrate that the SARIMA model provides a reliable tool for forecasting short-term domestic passenger flows at the airport.
Forecasting Indonesia’s Wholesale Price Index (WPI) Using the Holt's Exponential Smoothing Method Muthahharah, Isma; Meliyana, Sitti Masyitah; Mar’ah, Zakiyah
Quantitative Economics and Management Studies Vol. 6 No. 2 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.qems3937

Abstract

The Index of Wholesale Price (WPI) is a key benchmark in analyzing price movements at the wholesale level as it can affect the economic stability of a country. This research purpose to forecast the movement of WPI in Indonesia using Holt's Exponential Smoothing technique, which is effective in analyzing time series data that show trend patterns. This research utilizes secondary data obtained from the BPS for the period 2020-2024. The analysis is carried out by determining the optimal value of α and β parameters using trial and error techniques. Furthermore, the forecasting process is carried out using the best parameters that have been obtained. Based on the analysis results, the combination of parameters α = 0.9 and β = 0.8 provides a Mean Absolute Percentage Error (MAPE) value of 0.22%, which indicates a very good level of forecasting accuracy. WPI forecasting for the year 2025 shows a consistent upward pattern, reflecting a consistent increase in WPI previous historical trends. The results of this study can be a reference in making price and wholesale trade policies by the government and related parties in the economic sector.
Implementation K-Medoids Algorithm for Clustering Indonesian Provinces by Poverty and Economic Indicators Hafid, Hardianti; Meliyana, Sitti Masyitah; Muthahharah, Isma; Mar’ah, Zakiyah
Quantitative Economics and Management Studies Vol. 6 No. 2 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.qems3940

Abstract

Regional development disparities in Indonesia remain one of the main challenges in formulating national development policies. This study aims to classify the 38 provinces in Indonesia based on four key indicators: the percentage of the population living in poverty, Gross Regional Domestic Product (GRDP) per capita, the open unemployment rate, and the Human Development Index (HDI), using the K-Medoids algorithm. This method was chosen due to its robustness to outliers and its ability to produce representative clusters. The data used are secondary data obtained from the Central Bureau of Statistics (BPS). The analysis process began with data standardization, determination of the optimal number of clusters using the Elbow and Silhouette methods, followed by clustering implementation and result interpretation. The analysis results identified four main clusters with distinct socioeconomic characteristics. Cluster 1 reflects provinces with moderate conditions, Cluster 2 represents more developed provinces, Cluster 3 highlights regions facing significant development challenges, and Cluster 4 consists of provinces with the most underdeveloped socioeconomic conditions. These findings indicate that the K-Medoids algorithm is effective in identifying inter-provincial disparity patterns and can serve as a foundation for formulating more targeted and inclusive development policies.
Co-Authors Abdul Rahman Ahmad Yasir Aras Ahmar, Ansari Saleh Aidid, Muhammad Kasim Alfairus, Muh. Qodri Alok Kumar Panday Angri Lismayani Anisa Nurfadilah Nurdin Annisa, Nurawalia Baso Intang Sappaile Elisa, Nur Erman Syarif Hardianti Hafid Hastuty Musa Isma Muthahharah Isma Muthahharah Jamaluddin Jamaluddin Lince, Ranak Mar'ah, Zakiyah Mariani Mar’ah, Zakiyah Miguel Botto-Tobar Muhammad Ammar Naufal Muhammad Ansarullah S. Tabbu Muhammad Isbar Pratama Muhammad, Subhan Muthahharah, Isma Nachnoer Arss Parkhimenko Vladimir Anatolievich R. Rusli Rahmadhani, Amaliyah Rahman, Abdul Rahmat Hidayat Rahmat Hidayat Rais, Zulkifli Rezky Amalia Hamka Ruliana Ruliana Ruliana, Ruliana Ruliana, R. Rusli Rusli Rusli Rusli Rustam, Sitti Nail Rustam, Sitti Nailah S. Sudarmin, S. S.A. Dunggio, Anugra Siti Nurazizah Auliah Sitti Nailah Rustam Sitti Nailah Rustam Sudarmin Sudarmin Sudarmin Wahyuni, Maya Sari Yunus, Asmar Zakiyah Mar'ah Zakiyah Mar'ah Zakiyah Mar’ah Zulkifli Rais