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SAWI TRANSFORMATION FOR SOLVING A SYSTEM OF LINEAR ORDINARY DIFFERENTIAL EQUATIONS Halim, Elvina; Zakaria, La
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2171-2186

Abstract

There are many problems in nature whose solutions are obtained through mathematical concepts. One of the most common mathematical concepts is a mathematical concept that is classified under initial value problems, such as a system of linear ordinary differential equations equipped with initial values. One tool that can solve the initial value problem is the Sawi transformation. This article describes the study of the initial value problem as a system of linear ordinary differential equations and its solution using the Sawi transformation. In addition, as part of applying the resulting theory, 2 (two) case examples are given (a first-order chemical reaction system with three certain chemicals and a mass-spring system with forced motion) to be solved using the Sawi transformation. So that problem solving can be interpreted and easily understood, in the 2 (two) case studies discussed and focused on the concentration of the chemical reactants, simulations were carried out for several different initial values and reaction rate constants. Compared to other methods (Laplace transform), the results obtained from using the Sawi transformation for the cases discussed show that the analytical solutions for the selected initial values have similar solutions.
Converting Corn Cobs into Briquettes in Braja Harjosari Village, Braja Salebah Subdistrict, East Lampung Regency Sutrisno, Agus; Zakaria, La; Aziz, Dorrah; Nisa, Khoirin
Jurnal Pengabdian Kepada Masyarakat (JPKM) TABIKPUN Vol. 6 No. 2 (2025)
Publisher : Faculty of Mathematics and Natural Sciences - Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpkmt.v6i2.164

Abstract

Corn cobs are agricultural waste that can be processed into an alternative firewood. Carbonization (pyrolysis) followed by briquetting is one method to process biomass into solid charcoal. According to a survey conducted, the large amount of corn cob waste is due to a lack of knowledge in processing waste which causes health and environmental problems. Converting corn cob waste into briquettes transforms it into a valuable commodity. In fact, transforming corn cob waste is essentially applying the zero waste concept to agricultural production systems. Based on potential and agreements with farmer groups, community members, and local government, this service activity was carried out. The productivity of the briquette charcoal business made from corn waste is increased through training and assistance.
Analysis of Population Growth in Central Lampung Regency Using Exponential and Logistic Models with GRG Parameter Optimization and Model Performance Evaluation Al Mahkya, Prana; Sutrisno, Agus; Zakaria, La
Desimal: Jurnal Matematika Vol. 9 No. 2 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v9i2.31153

Abstract

Accurate regional population projection is essential for development planning, infrastructure allocation, public service provision, and long-term resource management. This study analyzes population growth in Central Lampung Regency, Indonesia, by comparing exponential and logistic growth models supported by Generalized Reduced Gradient (GRG) parameter optimization and integrated model performance evaluation. Annual population data from 2016 to 2025 were obtained from official statistics, with 2016 defined as the base year. The exponential model was formulated under the assumption of unlimited proportional growth, whereas the logistic model incorporated a carrying capacity of 2,000,000 people as a modeling assumption to represent bounded demographic growth. The growth-rate parameter was optimized by minimizing Mean Absolute Percentage Error (MAPE), and model performance was evaluated using MAPE, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Robustness analysis, direct parameter sensitivity analysis, and paired t-test were also conducted to examine parameter stability and statistical differences between model errors. The results show that the population increased from 1,250,486 people in 2016 to 1,541,429 people in 2025, with a marked structural rise around 2020. Both models achieved very accurate performance, with MAPE values below 10%. However, the logistic model produced slightly lower errors, with MAPE of 2.3227%, MAE of 31,760.57 people, and RMSE of 40,705.66 people. Robustness and sensitivity analyses confirmed stable parameter behavior in both models. The paired t-test indicated no statistically significant difference between their errors. Thus, the logistic model is recommended as the more conceptually appropriate framework for long-term regional population projection.