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Comparative Modeling of Pineapple Production Using Gaussian GLM and Random Forest Regression Radot MH Siahaan; Indah Gumala Andirasdini; Fuji Lestari; Dwi Mahrani; Amalia Listiani
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28721

Abstract

This study aims to conduct a comparative modelling of pineapple production at PT Great Giant Pineapple (GGP) using Gaussian GLM as parametric statistical approach and Random Forest Regression method as machine learning based on monthly data from 2014 to 2022. Multicollinearity testing and distribution fitting were conducted to validate the Gaussian assumption. For the Random Forest Regression, hyperparameters were optimized by tuning the number of trees (ntree) and the number of predictors at each split (mtry) with model stability evaluated using Out-of-Bag (OOB) error. The Gaussian GLM achieved a MAPE of 8.41% (R² = 0.106) for the GP3 clone and 11.27% (R² = 0.149) for the F180 clone. Random Forest Regression produced a testing MAPE of 9.28% (R² = 0.144) for GP3 and 12.11% (R² = 0.105) for F180. While both models achieved low prediction error based on MAPE, they differed in identifying influential variables and showed limited explanatory power as indicated by low R² values. The Gaussian GLM identifies air pressure as significant for both clones and rainfall for F180 clone, while Random Forest consistently identifies rainfall as the most influential predictor. These findings confirm the complementary strengths of parametric and machine learning approaches in supporting climate-based production planning and risk mitigation.
Density Functional Theory (DFT) and Quasi Harmonic Approximation (QHA) on Isotope effect of Methane Absorbed on Ag(111) Surface Putra, Septia Eka Marsha; Andirasdini, Indah Gumala
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 14, No 1 (2024): April
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v14i1.71754

Abstract

We investigated the isotope effect of methane (CH4) on Ag(111) using van der Waals density functional and the quasi-harmonic approximation.In this study, we combined two methods to investigate the nuclear quantum effect in methane adsorption on an Ag(111) surface. We obtained that the adsorption potential energies of CD4on fcc Ag(111) surfaces are shallower than those of CH4, whereas the equilibrium distances from the surface are larger.The similiar finding also observed in our previous study, however, Ag(111) gives smaller energies.It is found that the similar softening of the C-H bond pointing toward the surface is the cause of the isotope effect. This softening leading to lowering the vibrational frequency and large zero-point energy difference between CH4and CD4.