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All Journal BIOEDUSAINS: Jurnal Pendidikan Biologi dan Sains Jurnal Fisika
Mochamad Aryono Adhi
Universitas Negeri Semarang
Education Physics

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Penggunaan Metode Random Forest, Support Vector Machine dan Artificial Neural Networks dalam Prediksi Suhu Udara di Balikpapan Erika Meinofelia; Mochamad Aryono Adhi; Achmad Fahruddin Rais; Djunaidi Djunaidi; Feddy Setio Pribadi
BIOEDUSAINS:Jurnal Pendidikan Biologi dan Sains Vol. 8 No. 6 (2025): BIOEDUSAINS:Jurnal Pendidikan Biologi dan Sains
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/9s0xxr05

Abstract

This study aimed to compare the performance of three algorithmic models, namely Random Forest, Support Vector Machine (SVM), and Artificial Neural Networks (ANN), in predicting air temperature in Balikpapan. Changes in air temperature influenced by various climatic and geographical factors present a major challenge in urban planning; thus, accurate predictions are crucial to support sustainable and climate-adaptive city planning. The dataset used consists of observational data from the Balikpapan Meteorological Station, BMKG, over ten years, from January 2014 to December 2024. The models were evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R Squared (R²) metrics. The results show that the SVM method produced an MAE of 0.17, RMSE of 0.21, and R² of 0.95, providing better predictions than ANN and Random Forest. In conclusion, SVM is an effective method for air temperature prediction in Balikpapan. Keywords: Artificial Neural Networks, Random Forest, Support Vector Machine, Machine Learning, Air Temperature Prediction
Perubahan Iklim Terhadap Kesesuaian Lahan Agroklimat Tanaman Padi dengan Skenario SSP245, SSP370, dan SSP585 Studi Kasus Jawa Tengah Pujiati Pujiati; Mochamad Aryono Adhi; Trinah Wati; Andreas Kristiawan; Firmansyah Firmansyah
BIOEDUSAINS:Jurnal Pendidikan Biologi dan Sains Vol. 8 No. 6 (2025): BIOEDUSAINS:Jurnal Pendidikan Biologi dan Sains
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/zyz5hv59

Abstract

This study investigates the long-term impact of climate change on the agricultural sector and land suitability in Central Java—Indonesia’s third-largest rice producer. To project future atmospheric, oceanic, and land dynamics, the research utilizes the Global Climate Model (GCM) under the CMIP6 (Coupled Model Intercomparison Project Phase 6) framework. These models integrate socioeconomic factors through three specific Shared Socioeconomic Pathways: SSP2-4.5, SSP3-7.0, and SSP5-8.5, downscaled to a 25 km resolution. The analysis utilizes historical rainfall and temperature data (1991–2014) as a baseline to evaluate two future windows: the near-future (2027–2050) and the far-future (2077–2100). Employing linear regression and agro-climatic land suitability calculations for rice, the results indicate that while climate change impacts remain relatively stable in the near-term, there is a significant risk for the far-future. Specifically, agro-climatic land suitability is projected to decline in the far-future period, driven primarily by a consistent rise in average annual temperatures across Central Java. These findings serve as a critical evidence-based recommendation for regional agricultural planning and adaptation strategies.   Keywords: Central Java, Agroclimate Land Suitability, Climate Change, Shared Socioeconomic Pathways
Virtual Tide Gauge Observation of the 2006 Pangandaran Tsunami Using COMCOT Version 1.7 Farikha Qurotun Uyun; Supriyadi; Sutikno; Mochamad Aryono Adhi; Hamzah Latief
Jurnal Fisika Vol. 14 No. 2 (2024): Jurnal Fisika 14 (2) 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jf.v14i2.14678

Abstract

The 2006 Pangandaran tsunami was one of the most significant natural disasters in Indonesia, causing major damage to coastal infrastructure and extensive social impacts. This study aims to reconstruct the 2006 Pangandaran tsunami using topographic data from DEMNAS and bathymetry data from BATNAS by applying it to a fine grid size (nested model). The other purpose is to determine the validity of the model that has been made if verified using a comparison with the results of field surveys and calculations of Aida numbers. The research method used is by using topographic data from DEMNAS and bathymetry data from BATNAS as well as using earthquake mechanism data from Global CMT. Furthermore, it is simulated using COMCOT software version 1.7 as well as by creating a nested grid of 4 grids.  The obtained simulation results are then verified using field data and Aida numbers. The results of this study show that the earthquake with a magnitude of 7.7 Mw caused the movement of tectonic plates which resulted in a decrease and increase in sea level along the coast of Pangandaran. The simulation results have an Aida K value of 1.48 and an Aida
Co-Authors Achmad Fahruddin Rais Andreas Kristiawan Djunaidi Djunaidi Erika Meinofelia Farikha Qurotun Uyun Feddy Setio Pribadi Firmansyah Firmansyah Hamzah Latief Pujiati Pujiati Supriyadi Sutikno Trinah Wati