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All Journal Jurnal Fisika FLUX Jurnal Pendidikan Fisika
Wahidin, Alka Budi
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Earth & Planetary Sciences Education Materials Science & Nanotechnology Physics

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Integration of Machine Learning and NASA POWER Dataset for Predicting Coffee Production in Lampung Aprilia, Ayu; Wahidin, Alka Budi; Abdurrahman, Ahmad Faruq
Jurnal Fisika Flux: Jurnal Ilmiah Fisika FMIPA Universitas Lambung Mangkurat Vol 22, No 1 (2025): Jurnal Fisika Flux: Jurnal Ilmiah Fisika FMIPA Universitas Lambung Mangkurat
Publisher : Lambung Mangkurat University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/flux.v22i1.20980

Abstract

The production of coffee in Indonesia, particularly in Lampung, plays a crucial role in both the local and national coffee sectors. Weather plays a crucial role in the development of coffee trees. Nonetheless, the coffee production is frequently hindered by unpredictable weather conditions. This can be foreseen through the use of scientific forecasting. Blending agriculture and science can maximize coffee production and effective resource utilization. This study creates a predictive model for coffee production by combining machine learning methods with the NASA POWER dataset. Data from NASA POWER is used to acquire information on various weather factors that impact the growth of coffee trees, including solar radiation, temperature, humidity, pressure, soil wetness, and wind speed. Additionally, information on coffee production is sourced from BPS-Statistics Indonesia. The Random Forest algorithm is used to model the connection between variables. The study findings demonstrate that combining machine learning with remote sensing can offer an effective model. Assessment of the R2, RMSE, and MSE figures shows satisfactory results, though not flawless. This happens due to external factors beyond the weather that influence coffee cultivation. The combination of machine learning and remote sensing is incorporated into a website. This model has the potential to be transformed into an app that offers precise details on coffee cultivation in Lampung. This research highlights how remote sensing data can offer insights into predicting sustainable agriculture outcomes.
Photometric Observations of Short Period Variable Stars: EH Librae Djumari, Sulthan Julieri; Malasan, Hakim Luthfi; Wibowo, Ridlo Wahyudi; Wahidin, Alka Budi
Jurnal Fisika Flux: Jurnal Ilmiah Fisika FMIPA Universitas Lambung Mangkurat Vol 21, No 1 (2024): Jurnal Fisika Flux: Jurnal Ilmiah Fisika FMIPA Universitas Lambung Mangkurat
Publisher : Lambung Mangkurat University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/flux.v21i1.17865

Abstract

EH Librae [BD-00 2911; αJ2000 = 14h58m55.92s; δJ2000 = -0°56'53.01'] is a pulsating variable δ Scuti stars which is located in the constellation of Libra and has a period of 0.0884129 days. The aim of this research is to obtain and analyze the light curve, deduce the period, and derive the physical parameters of EH Librae. Observations were made using the ITERA Robotic Telescope 0.25m Ritchey-Cretian with CCD ATIK 383L+ on the BVR band. Aperture photometry was performed to measure the flux of EH Librae. Light curve was constructed using the differential photometry method, and the determination of the period was carried out using the Lomb – Scargle method. Secondary data from the Transiting Exoplanet Survey Satellite (TESS) is used as an addition to derive the physical parameters of EH Librae. The results of this study are the light curve of EH Librae on the BVR band with Magnitude, V = 9.506 – 10.071 ± 0.002. Using the index color  = 0.271, we derived the   = (7.7 ± 0.4) 103 K. O – C diagram shows no significant changes in the period. Derivation of physical parameters are  = 2.12 ;  = 1.4 ; and  = 3.93 cgs was carried out using asteroseismology method with  = 846  and  = 51.9 
English Language Aprilia, Ayu; Wahidin, Alka Budi; Syafriadi
JPF (Jurnal Pendidikan Fisika) Universitas Islam Negeri Alauddin Makassar Vol 13 No 1 (2025)
Publisher : Pendidikan Fisika UIN Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jpf.v13i1.54081

Abstract

Earthquake’s phenomena are critical for understanding Earth's interior, tectonic processes, and disaster preparedness. Because of indonesia location in the Pacific Ring of Fire, it’s suffering from regular seismic activities which result in huge annual losses. This study investigates the earthquake data from 1992 to 2024 by applying clustering techniques such as K-means and geodata visualization. By integrating physics, geospatial analysis, and machine learning, the study processes earthquake data to calculate energy release and analyze spatial-temporal patterns. Principal Component Analysis (PCA) is applied to reduce data dimensionality, while K-Means clustering identifies seismic patterns based on magnitude, depth, and energy. Visual tools, including correlation heatmaps and spatial maps, are used to present findings that support earthquake risk management in Indonesia.The results reveal temporal patterns in earthquake activity, with peaks observed in 2004–2007, associated with significant seismic energy release. Spatial analysis highlights high energy concentrations in megathrust zones. PCA and K-Means clustering identify three distinct clusters with varying correlations between seismic and atmospheric variables, indicating the influence of thermal and tectonic factors. These insights contribute to seismic hazard mapping, risk reduction strategies, and the improvement of earthquake prediction models. Future research should extend datasets and refine machine learning techniques to achieve more accurate predictions.
Co-Authors Abdurrahman, Ahmad Faruq Aprilia, Ayu Djumari, Sulthan Julieri Malasan, Hakim Luthfi Syafriadi Wibowo, Ridlo Wahyudi