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All Journal JOIV : International Journal on Informatics Visualization Eksakta : Berkala Ilmiah Bidang MIPA Indonesian Journal of Applied Mathematics
Herho, Sandy Hardian Susanto
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Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics

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Efficient 1D Heat Equation Solver: Leveraging Numba in Python Herho, Sandy Hardian Susanto; Kaban, Siti Nurzannah; Irawan, Dasapta Erwin; Kapid, Rubiyanto
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 25 No. 02 (2024): 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/vol25-iss02/487

Abstract

This paper presents a Numba-based solver for the 1D Heat Equation, seamlessly blending Python’s readability with Numba’s dynamic Just-In-Time (JIT) compilation. The explicit method exhibits a notable runtime reduction from 8.324 s to 4.035 s, while the implicit method sees a more pronounced improvement, decreasing from 9.970 s to 1.195 s. Statistical tests confirm the statistical significance of these efficiency gains. Future research directions include extending the solver to multidimensional heat equations, exploring advanced parallelization techniques, and implementing dynamic parameter optimization strategies. Collaboration with domain experts for real-world applications is also envisioned to validate the solver’s performance and impact. In summary, the symbiosis of Python and Numba in crafting an optimized 1D Heat Equation solver marks a pivotal advancement in efficient numerical solutions. This research holds promise for diverse scientific
Design and Reliability Analysis of Four-Legged Jacket Type Offshore Platform in North Java Sea Kaban, Siti; Herho, Sandy Hardian Susanto; Tawekal, Ricky; Irawan, Dasapta Erwin
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 26 No. 01 (2025): 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/vol26-iss01/550

Abstract

In ideal conditions, offshore platform design follows standardized international criteria such as the American Petroleum Institute Recommended Practice 2A-LRFD (API RP2A-LRFD) to ensure structural reliability and safety. However, the real conditions in the Java Sea present unique challenges, as environmental loading patterns and regional factors may differ from those assumed in global standards. This study proposes a comprehensive solution through combined structural analysis and reliability assessment using Monte Carlo simulation methods. The urgency of this research stems from the critical need to validate and potentially adjust design standards for regional applications, ensuring the long-term safety and reliability of offshore structures in Southeast Asian waters. The research objectives focus on evaluating the structural reliability of a four-legged jacket type platform using both deterministic and probabilistic approaches, specifically assessing the applicability of API RP2A-LRFD criteria to Java Sea conditions. Results demonstrate that while the structure meets basic design criteria, the reliability indices (β = 16.70 for LRFD, β = 22.29 for unfactored) suggest current load factors may be overly conservative for regional conditions.
Quantitative Performance Analysis of Spring-Mass-Damper Control Systems: A Comparative Implementation in Python and R Herho, Sandy Hardian Susanto; Kaban, Siti Nurzannah
Indonesian Journal of Applied Mathematics Vol. 5 No. 1 (2025): Indonesian Journal of Applied Mathematics Vol. 5 No. 1 April Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/indojam.v5i1.2104

Abstract

The numerical simulation of control of spring-mass-damper (SMD) systems offer critical insights into dynamical systems and computational methodologies. This study provides a comprehensive comparative analysis of implementing SMD systems across two prominent open-source scientific computing platforms: Python and R. By examining both open-loop and closed-loop system configurations, the research investigates the computational performance, numerical accuracy, and implementation characteristics of these platforms. Utilizing an idealized one-dimensional SMD system with a Proportional-Integral-Derivative (PID) controller, the study conducted extensive numerical simulations and statistical performance analyses. Results revealed Python's significant advantages in execution speed, achieving up to 63.57% reduction in runtime for controlled system simulations, while R demonstrated superior consistency in execution and memory usage. The controlled system demonstrated exceptional performance, with a final position error of merely 0.4% and enhanced damping characteristics. This work not only bridges theoretical stability analysis with empirical performance insights but also promotes reproducibility and transparency in computational dynamics research by leveraging open-source platforms.
Performance Evaluation of a Simple Feed-forward Deep Neural Network Model Applied to Annual Rainfall Anomaly Index (RAI) Over Indramayu, Indonesia Herho, Sandy Hardian Susanto; Irawan, Dasapta Erwin; Fajary, Faiz Rohman; Suwarman, Rusmawan; Kaban, Siti Nurzannah
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.1984

Abstract

Indramayu is a district in West Java that is known for being the leading producer of rice and brackish salt. The production of these two commodities is strongly influenced by hydroclimatological conditions, making accurate and reliable long-term estimates crucial. In this study, we evaluated a simple feed-forward deep neural network (DNN) model that could potentially be used as a candidate for statistical guidance to improve the accuracy of a mesoscale numerical climate model. We used the spatial average of the accumulated annual rainfall of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data as an input time series with a time range from 1981 to 2022. This data was then processed into annual rainfall anomaly index (RAI) data. The Annual RAI was divided into training and test sets, and the feed-forward DNN model was fitted to the annual RAI in the training set. The accuracy of the model was then tested in the test set using the root-mean-square error (RMSE) metric. Our study shows that the feed-forward DNN model is unsuitable for estimating the annual RAI over Indramayu. The RMSE values are significantly high in the training and test sets.
Co-Authors Dasapta Erwin Irawan, Dasapta Erwin Fajary, Faiz Rohman Kaban, Siti Kaban, Siti Nurzannah Rubiyanto Kapid Suwarman, Rusmawan Tawekal, Ricky