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All Journal Eksakta : Berkala Ilmiah Bidang MIPA
Nurhayati Nurhayati
Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya, Surabaya, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Residual-Based Analysis of the Mismatch Between LoRa Channel Models and Field Measurements in Urban and Rural Environments Muhamad Bagus Fikril Alan; Pradini Puspitaningayu; Nurhayati Nurhayati; Muhammad 'Aamir Nashrullah; Nobuo Funabiki; Erwin Sutanto; Fahmi Fahmi
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 27 No. 03 (2026): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464) In Progress
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol27-iss03/683

Abstract

This study investigates discrepancies between classical propagation models (Okumura-Hata, CI, FI) and LoRa field measurements in urban and rural environments. While rural areas exhibited linear path loss trends, urban scenarios showed distinct signal saturation, resulting in significant residuals even after model optimization. To address this, a residual-based analysis using Machine Learning (Linear Regression, Decision Tree, Random Forest) was proposed to map these systematic errors. The evaluation reveals that Random Forest (RF) significantly outperforms other algorithms, achieving an of 0.961 and an RMSE of 3.291 dB. These findings demonstrate that model mismatches follow deterministic patterns driven by environmental features rather than random noise. The study concludes that integrating ML-based residual compensation is essential for accurate radio planning in heterogeneous network deployments.
Vehicle Inspection Forecasting Through Temporal Signal Processing and Ensemble Machine Learning Bambang Sismanto; Nurhayati Nurhayati; Raden Roro Hapsari Peni Agustin Tjahyaningtijas; Ja'afar Mahmud; Raimundo Eider Figueredo; Atul Varshney
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 27 No. 03 (2026): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464) In Progress
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol27-iss03/706

Abstract

Vehicle inspection services are essential for maintaining transportation safety and regulatory compliance. However, daily inspection volumes in developing regions exhibit high stochastic fluctuations due to operational closures and reporting inconsistencies, making direct daily forecasting unreliable. This study proposes a weekly vehicle inspection forecasting framework using ensemble machine learning with temporal signal processing. Accurate forecasting is critical for resource allocation, staffing, and service management. The dataset covers January 2020 to December 2024 from Malang Regency, Indonesia, aggregated into 258 weekly observations. Weekly aggregation acts as a low-pass filter (ω_c = 1/7 day⁻¹) to reduce high-frequency noise while preserving seasonal dynamics. Four regression models were evaluated: Random Forest (R² = 0.6198, MAE = 69.85), XGBoost (R² = 0.7198, MAE = 55.68), Gradient Boosting (R² = 0.7373, MAE = 46.41), and a weighted ensemble (R² = 0.7164, MAE = 55.89). Conventional regression methods including Linear Regression, Ridge, and Lasso were also tested as baselines. Gradient Boosting achieved the best performance. The findings indicate that tree-based ensemble models capture non-linear temporal dynamics more effectively than conventional approaches. This study demonstrates that ensemble machine learning with signal processing provides practical forecasting tools for transportation service planning.
Residual-Based Analysis of the Mismatch Between LoRa Channel Models and Field Measurements in Urban and Rural Environments Muhamad Bagus Fikril Alan; Pradini Puspitaningayu; Nurhayati Nurhayati; Muhammad 'Aamir Nashrullah; Nobuo Funabiki; Erwin Sutanto; Fahmi Fahmi
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 27 No. 03 (2026): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol27-iss03/683

Abstract

This study investigates discrepancies between classical propagation models (Okumura-Hata, CI, FI) and LoRa field measurements in urban and rural environments. While rural areas exhibited linear path loss trends, urban scenarios showed distinct signal saturation, resulting in significant residuals even after model optimization. To address this, a residual-based analysis using Machine Learning (Linear Regression, Decision Tree, Random Forest) was proposed to map these systematic errors. The evaluation reveals that Random Forest (RF) significantly outperforms other algorithms, achieving an of 0.961 and an RMSE of 3.291 dB. These findings demonstrate that model mismatches follow deterministic patterns driven by environmental features rather than random noise. The study concludes that integrating ML-based residual compensation is essential for accurate radio planning in heterogeneous network deployments.
Vehicle Inspection Forecasting Through Temporal Signal Processing and Ensemble Machine Learning Bambang Sismanto; Nurhayati Nurhayati; Raden Roro Hapsari Peni Agustin Tjahyaningtijas; Ja'afar Mahmud; Raimundo Eider Figueredo; Atul Varshney
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 27 No. 03 (2026): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol27-iss03/706

Abstract

Vehicle inspection services are essential for maintaining transportation safety and regulatory compliance. However, daily inspection volumes in developing regions exhibit high stochastic fluctuations due to operational closures and reporting inconsistencies, making direct daily forecasting unreliable. This study proposes a weekly vehicle inspection forecasting framework using ensemble machine learning with temporal signal processing. Accurate forecasting is critical for resource allocation, staffing, and service management. The dataset covers January 2020 to December 2024 from Malang Regency, Indonesia, aggregated into 258 weekly observations. Weekly aggregation acts as a low-pass filter (ω_c = 1/7 day⁻¹) to reduce high-frequency noise while preserving seasonal dynamics. Four regression models were evaluated: Random Forest (R² = 0.6198, MAE = 69.85), XGBoost (R² = 0.7198, MAE = 55.68), Gradient Boosting (R² = 0.7373, MAE = 46.41), and a weighted ensemble (R² = 0.7164, MAE = 55.89). Conventional regression methods including Linear Regression, Ridge, and Lasso were also tested as baselines. Gradient Boosting achieved the best performance. The findings indicate that tree-based ensemble models capture non-linear temporal dynamics more effectively than conventional approaches. This study demonstrates that ensemble machine learning with signal processing provides practical forecasting tools for transportation service planning.
Co-Authors Atul Varshney Bambang Sismanto Erwin Sutanto Fahmi Fahmi Ja'afar Mahmud Muhamad Bagus Fikril Alan Muhammad 'Aamir Nashrullah Nobuo Funabiki Pradini Puspitaningayu Raden Roro Hapsari Peni Agustin Tjahyaningtijas Raimundo Eider Figueredo