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All Journal AXIOM : Jurnal Pendidikan dan Matematika Indonesian Journal of Education and Mathematical Science
Rizki Habibi
Universitas Negeri Medan
Computer Science & IT Education Languange, Linguistic, Communication & Media Library & Information Science Mathematics

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Optimization of operational cost planning in integrated farming systems using a mixed-integer linear programming approach Lasker Pangarapan Sinaga; Rizki Habibi; Suvriadi Panggabean
AXIOM : Jurnal Pendidikan dan Matematika Vol 15, No 1 (2026)
Publisher : State Islamic University of North Sumatra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30821/axiom.v15i1.26491

Abstract

This study addresses the challenge of optimizing small-scale integrated farming systems (IFS) by minimizing operational costs while ensuring sustainable land use across agricultural, livestock, and aquaculture components. The main objective is to develop a Mixed-Integer Linear Programming (MILP) model that incorporates deterministic parameters such as land availability, labor allocation, and internal-external input flows. The model integrates multiple interrelated subsystems using production coefficients, resource constraints, and cost structures derived from actual smallholder scenarios. A two-period simulation was conducted to evaluate the model’s effectiveness using fixed input values, reflecting rural farming conditions. The results demonstrate that the system achieved consistent outputs without requiring external purchases of manure, feed, or irrigation water. The total operational cost reached IDR 96,770,000, with optimized land and labor allocation across periods. This research contributes a novel MILP formulation tailored to integrated farming, providing practical insights for policymakers and practitioners. Its implications extend to the development of decision-support systems for rural agricultural planning. However, the model's deterministic assumption limits its adaptability to dynamic environments. Future work should explore stochastic variants and real-time input adjustments to improve model flexibility and realism.
Evaluation of Gaussian Process Regression, Support Vector Regression, and K-Nearest Neighbors for Predicting Concrete Compressive Strength at Various Curing Ages Elmanani Simamora; Abil Mansyur; Muhammad Badzlan Darari; Suvriadi Panggabean; Rizki Habibi
Indonesian Journal of Education and Mathematical Science Vol 7, No 3 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara (UMSU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/ijems.v7i3.31258

Abstract

Accurate prediction of concrete compressive strength is critical for quality control, structural evaluation, and mix optimization. However, the connection between mix composition, curing age, and compressive strength is often nonlinear and heterogeneous. This study assesses four prediction models, including Native Gaussian Process Regression, Gaussian Process Regression with Conformal Intervals, Support Vector Regression, and K-Nearest Neighbors, to estimate concrete compressive strength at various curing ages. Predictors are produced from a combination of composition, curing age, and material-important properties. Model performance is evaluated using Leave-One-Age-Out and repeated stratified K-fold by Age, with root mean square error, mean absolute error, coefficient of determination, coverage, and average interval length. The results demonstrate that Native Gaussian Process Regression gives the best steady overall performance in both assessment methodologies. At the same time, Gaussian Process Regression with Conformal Intervals tends to provide higher coverage with the consequence of wider intervals. In general, Native Gaussian Process Regression provides the best balance among point prediction accuracy, interval coverage, and interval efficiency for heterogeneous concrete compressive strength data by curing age.
Co-Authors Abil Mansyur, Abil Elmanani Simamora Lasker Pangarapan Sinaga Muhammad Badzlan Darari Suvriadi Panggabean