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Muthahharah, Isma
Universitas Patompo Makassar
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Journal : Quantitative Economics and Management Studies

 1 
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 Agusalim Juhari Aidid, Muhammad Kasim Alfiah Nurfadhilah AM Hindi Amran Amrullah, Nursinah Andi Syukriani Aslindawati, Nur Asmayani Asmayani BT Samsuddin, Yati Bungatang Bungatang Bungatang Bungatang Hapsan, Amran Hardianti Hafid Hariani Harjuna Hariani Harjuna Harjuna, Hariani Husain, Ahmad Inayanti Fatwa Islamiah, Farida Khaidarsyah Khaidarsyah Mar'ah, Zakiyah Mar’ah, Zakiyah Masyitah Meliyana, Sitti Meliyana R, S.Pd, M.Si, Sitti Masyitah Muhammad Isbar Pratama Nur Aslindawati Rahmat Hidayat Resmawan Resmawan Rusdianto Rusdianto Rusdianto Rusdi Rustam, Sitti Nail S. Sugianty Sapinah Sapinah Septiary Pratiwi, Kartika Sitti Masyitah Meliyana R. St. Habibah, St. Habibah Sutamrin, Sutamrin Syalsabila, Annisa Syamsinar Zakiyah Mar'ah Zakiyah Mar’ah