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All Journal SAINSMAT Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Varian Inferensi Jurnal Darma Agung Quantitative Economics and Management Studies Panrannuangku Jurnal Pengabdian Masyarakat Parameter: Journal of Statistics Journal of Mathematics: Theory and Applications SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam VARIANSI: Journal of Statistics and Its Application on Teaching and Research English Language, Linguistics, and Culture International Journal (ELSTIC-IJ) Ininnawa: Jurnal Pengabdian Masyarakat Prosiding Seminar Nasional Dies Natalis Universitas Negeri Makassar Jurnal Pengabdian Masyarakat Indonesia Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M) SMART: Jurnal Pengabdian Kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS) JAMP
Hardianti Hafid
Universitas Negeri Makassar
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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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.
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 Abd. Hafid Abdul Rahman Ahmad Talib Ahmad Yasir Aras Ahmar, Ansari Saleh Arwini Arisandi Awaluddin Muin Ayu Pebriyanti Bungatang DH, Satriani Fahmuddin S., Muhammad Fahmuddin, Muhammad Fahmuddin, Muhammad Hafid, Abd. Haryono, Andri Herdawati, Herdawati Isma Muthahharah Mar'ah, Zakiyah Mar’ah, Zakiyah Meliyana R, S.Pd, M.Si, Sitti Masyitah Meliyana R, Sitti Masyitah Meyliana, Sitti Masyitah Moeh Zainal Khairul Muh. Fahmuddin Muhammad Fahmuddin S Muhammad Isbar Pratama Muhammad Nusrang Muhammad Nusrang Muhammad Yusuf Muhsin Efendi Muthahharah, Isma Muthahharah, Isma Mutiara Nur Agus Salim, Nur Agus Nusrang, Muhammad Nusrang, Muhammad Rahmanda, Lalu Ramzy Rahmat H. S. Rahmat Hidayat Rais, Zulkifli Reski Wahyu Yanti Rezky Amalia Hamka Rezky, Akhmad Rezky Ramadhana T Risqi, Shopia Rosmalah Rosmalah Rosmalah Rosmalah, Rosmalah Ruliana Rustam, Sitti Nail Satriani DH Selvi Annisa Sitti Masyitah Meliyana R. Sitti Nailah Rustam Sudarmin Yati Samsyuddin Zakiyah Mar'ah Zakiyah Mar’ah Zulkifli Rais