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All Journal Jurnal Fisika FLUX Engineering, Mathematics and Computer Science Journal (EMACS) International Journal Software Engineering and Computer Science (IJSECS)
Rena Misliniyati
University of Bengkulu
Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Education Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Physics

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Probabilistic Liquefaction Potential Mapping in South Lebong Based on PSHA-Derived PGA Diefi Deayuzeta; Lindung Zalbuin Mase; Fepy Supriani; Tri Bintang Pratitis; Rena Misliniyati; Khairul Amri
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 1 (2026): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i1.15761

Abstract

This study evaluates liquefaction potential in South Lebong Subdistrict, Lebong Regency, Bengkulu Province, which is located in an active tectonic region influenced by the Sumatra Fault system and dominated by alluvial deposits. A probabilistic approach using the Probabilistic Seismic Hazard Analysis method to determine Peak Ground Acceleration (PGA) values for 10% and 2% probabilities of exceedance in 50 years, representing moderate to extreme earthquake conditions. South Lebong District is dominated by PGA values greater than 0.8g, particularly under the 2% probability scenario, indicating relatively high seismic hazard in the study area. Under the 10% probability scenario, several locations still show PGA values ranging from 0.4g to 0.8 g. The obtained PGA values were subsequently used to evaluate liquefaction potential through calculations of the Cyclic Stress Ratio (CSR), Cyclic Resistance Ratio (CRR), and Factor of Safety (FS). Additionally, susceptibility was assessed using the Liquefaction Potential Index (LPI) method. The results indicate that higher PGA values under the 2% probability scenario increase seismic loading intensity and liquefaction susceptibility within the study area. Based on the LPI classification, liquefaction potential under the 10% probability scenario is generally categorized as low to moderate, whereas under the 2% probability scenario, several locations shift into the moderate to severe liquefaction category. These findings indicate that liquefaction susceptibility in the South Lebong District is strongly influenced by the interaction between earthquake loading intensity and local geotechnical conditions. Therefore, the results of this study can support earthquake hazard mitigation planning and the development of safer areas that are less vulnerable to liquefaction hazards.
Assessment of Soil Resistance Based on Variations in Shear Wave Velocity (Vs) in the Muara Bangkahulu Area, Bengkulu City Roky Suprandani; Lindung Zalbuin Mase; Fepy Supriani; Rena Misliniyati; Khairul Amri
Jurnal Fisika Flux: Jurnal Ilmiah Fisika FMIPA Universitas Lambung Mangkurat Vol 23, No 1 (2026): 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.v23i1.22575

Abstract

Kawasan Muara Bangkahulu merupakan kawasan yang rentan terhadap aktivitas seismik karena terletak di zona subduksi serta keberadaan patahan aktif Sumatera dan Mentawai. Penelitian ini bertujuan untuk menyajikan peta mikrozonasi berdasarkan variasi kecepatan gelombang geser (VS) pada kedalaman 10 hingga 50 m (VS10 s.d. VS50). Data yang digunakan adalah kecepatan gelombang geser (VS) hasil survei mikrotremor. Enam titik representatif dipilih berdasarkan persebaran permukiman dan fasilitas publik. Pengolahan data VS dilakukan untuk mendapatkan nilai VS10, VS20, VS30, VS40, VS50 kelas situs, dan nilai Ground Ampification factor (GAF). Hasil penelitian menunjukkan bahwa sebagian besar titik penelitian tergolong kelas situs C (tanah keras, batuan lunak) dan D (tanah sedang) dengan rentang VS10, VS20, VS30, VS40, VS50   berkisar antara 290,3 - 534,7 m/s. Kawasan Muara Bangkahulu didominasi oleh nilai GAF sedang hingga tinggi berkisar antara 0,8 s.d. 2,0. Penelitian ini diharapkan dapat menjadi acuan dalam memahami karakteristik tanah yang dinamis sebagai informasi penting untuk perencanaan tata ruang dan mitigasi risiko gempa bumi di kawasan Muara Bangkahulu.
Machine Learning-Based Prediction of Shallow Foundation Bearing Capacity Incorporating Slope Inclination as a Predictive Feature Afifah Yurisa Putri; Lindung Zalbuin Mase; Muharram Nur Fikri; Rena Misliniyati; Aidil Fitriansyah
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7430

Abstract

Accurate prediction of the ultimate bearing capacity of shallow foundations is important for safe geotechnical design, particularly under varying slope and soil conditions where conventional analytical methods may be limited by simplifying assumptions. This study developed and evaluated machine learning models for predicting the ultimate bearing capacity of shallow foundations using slope inclination, footing width, foundation depth, and soil friction angle as input variables. A dataset of 399 samples obtained from a previously validated finite element method (FEM)-based investigation was used to train and test Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) models. Model performance was evaluated using the coefficient of determination (R²), Root Mean Square Error (RMSE), and 10-fold cross-validation. Unlike previous studies that primarily considered conventional foundation and soil parameters, this study incorporates slope inclination within a unified machine learning framework and compares statistical, kernel-based, and ensemble learning approaches under the same evaluation conditions. XGBoost achieved the highest predictive performance, with a testing R² of 0.9938 and an RMSE of 31.669, followed by SVR with an R² of 0.9534 and an RMSE of 86.900. MLR showed comparatively lower performance, with an R² of 0.8396 and an RMSE of 161.158. The 10-fold cross-validation results further indicated stable XGBoost performance, with a mean R² of 0.991 and a standard deviation of 0.003. These results indicate that XGBoost provides high predictive performance for the evaluated dataset and may support bearing capacity estimation for shallow foundation design.
Co-Authors Afifah Yurisa Putri Aidil Fitriansyah Diefi Deayuzeta Fepy Supriani Khairul Amri Lindung Zalbuin Mase Muharram Nur Fikri Roky Suprandani Tri Bintang Pratitis