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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Achmad Fauzan
Statistics Study Program, Faculty of Mathematics and Natural Science, Universitas Islam Indonesia
Computer Science & IT Mathematics

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UNVEILING SPATIAL PATTERNS OF LAND CONVERSION THROUGH MACHINE LEARNING AND SPATIAL DISTRIBUTION ANALYSIS Mufida Fauziah Faiz; Achmad Fauzan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7281

Abstract

Kayu Agung District in Ogan Komering Ilir (OKI) Regency, South Sumatra, has undergone rapid population growth, resulting in notable land-use transformations. This study examines land-use change dynamics from 2019 to 2023 and identifies their spatial distribution using satellite imagery. Satellite imagery classification was performed using three machine learning algorithms—K-Nearest Neighbors (KNN), Naïve Bayes, and Logistic Regression—with KNN achieving the highest accuracy. Spatial analysis employing the Variance-to-Mean Ratio (VMR) revealed that land-use changes are spatially clustered, indicating concentrated land conversion in specific areas. These findings emphasize potential environmental risks, including declining green open spaces and increasing urban pressure. The study contributes by integrating machine learning and spatial statistical analysis (VMR) as a comprehensive framework for understanding land-use conversion, providing scientific insights to support adaptive spatial planning and the achievement of Sustainable Development Goal (SDG) 11: Sustainable Cities and Communities.
Modeling Airplane Passenger Volatility during the COVID-19 Crisis: a SARIMA and Intervention Analysis Nur Faizin; Achmad Fauzan
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40880

Abstract

The COVID-19 pandemic in early 2020 had a severe impact on air traffic at Indonesia’s Soekarno-Hatta International Airport, which is among the busiest in the world. This caused a sharp decline in passenger numbers in April 2020, resulting in significant data fluctuations that required statistical intervention. Therefore, to forecast passenger numbers during these fluctuating trends, this study used the SARIMA and Step Function Intervention analysis. The results showed that the Step Function Intervention model was more accurate than SARIMA in predicting the number of passengers at the domestic departure terminal. Based on data for the period between January 2006 and June 2024, the step function intervention model produced MAD, MSE, RMSE, and MAPE values that are smaller than the SARIMA model. The best model, SARIMA Intervention (2,1,0)(1,1,1)12 b = 0, s = 3, r = 1, fulfilled the white noise and normality assumptions with a MAD accuracy value of 82381.85 and MAPE of 9.62%. In addition, the Step Function Intervention Method further reduced the MAPE value by up to 6.93%.
Co-Authors Mufida Fauziah Faiz Nur Faizin