METODE KEMUNGKINAN MAKSIMUM EM PENDUGAAN PARAMETER MODEL NONLINEAR JERAPAN FOSFOR

Model jerapan fosfor tanah umumnya dimodelkan dengan model non-linear dua parameter Langmuir. Pada kasus tertentu digunakan model tiga parameter Langmuir-Freundlich dan empat parameter Langmuir dua permukaan (two surface Langmuir). Prosedur alternatif dapat digunakan untuk menduga parameter model nonlinear jerapan fosfor adalah metode kemungkinan maksimum EM. Tujuan penelitian ini adalah membandingkan model non-linear dua parameter Langmuir, tiga parameter Langmuir-Freundlich dan empat parameter Langmuir dua permukaan jerapan fosfor menggunakan metode kemungkinan maksimum EM. Penelitian ini menggunakan data isoterm jerapan P pada tanah kaolinitik dan smektitik, masing- masing sebanyak tiga lokasi. Hasil penelitian menunjukkan bahwa penggunaan model non-linear tiga parameter Langmuir-Freundlich dan empat parameter Langmuir dua permukaan jerapan fosfor tidak nyata lebih baik daripada model dua parameter Langmuir, ditunjukkan oleh nilai AIC relatif sama. Kata kunci: model nonlinear, kemungkinan maksimum EM, dua parameter Langmuir, tiga parameter Langmuir-Freundlich, empat parameter Langmuir dua permukaan

Perhatian Orang Tua terhadap Prestasi Belajar Siswa

Improving the quality of education for the people of Indonesia is an issue that always gets attention essential for the implementation of community development f a country. Problem in this research is there any influence of parent’s attention on student achievement Elementery School fifth grade Ampel 04 Wuluhan – Jember 2018/2019 school year. Research using questionnaires, observation, documentation, and interviews to obtain data, and the analyzed using product moment correlation, an tested with the level of 5% N=22 at 0,404 and level 1% of 0,515. 0,12 rxy result is much lower than rt product moment. In this case, HA HO rejected an accepted. Of these results prove no influence parent’s attention on student achievement Elementery School fifth grade Ampel 04 Wuluhan-Jember 2018/2019 school year with a significant level is very low. Keywords: Attention Parents, Academic Achievemen

RANDOM PARAMETER MODELS OF FERTILIZER RESPONSE FOR CORN USING SKEWED DISTRIBUTIONS

Random parameter models have been found to better determine the optimum dose of fertilizer than fixed parameter. However, a major restriction of it is the normality assumption.. The purpose of this study the introduction of random parameter models of fertilizer response using skewed distributions from a Bayesian perspective. The method is applied to data sets of multilocation trials of potassium fertilization on corn. We compare the Linear Plateau, Spillman-Mitscherlich, and Quadratic random parameter models with different random errors distribution assumption, i.e. as normal, skew-normal, Student-t and Skew-t distribution using the Deviance Information Criterion (DIC). The results show that the smallest DIC value is obtained for the normal linear plateau model compare with the other models. The correlation between observed and fitted values was significant.Key words : fertilizer response model, mixed effects, skewed distributions, DIC

Pendugaan Komponen Utama pada Pengaruh Acak Model Linear Campuran Terampat

Model linear campuran terampat (generalized linear mixed model) merupakan model yang memberikan ekstra flexibilitas dalam pengembangan model yang sesuai bagi data, sedangkan analisis komponen utama merupakan teknik ‘reduksi dimensi’ data yang terandalkan. Tujuan penelitian ini adalah mengetahui pendugaan komponen utama pada pengaruh acak model linear campuran terampat dan membandingkannya dengan model linear campuran terampat berdasarkan data asli. Penelitian ini menggunakan data percobaan lapangan pemupukan P padi sawah pada enam lokasi. Percobaan lapangan menggunakan rancangan acak kelompok (RAK) dengan 4 ulangan. Perlakuan terdiri dari 5 tingkat pupuk P yaitu : 0, 23, 46, 69, dan 115 kg P2O5/ha. Peubah respons yang diamati ialah serapan P tanaman pada saat panen. Sifat-sifat tanah yang diukur terdiri dari : pH H2O, C organik, P-H2O, Pi-NaHCO3, Pi-NaOH, P-HCl25, P-Truog, P-Olsen, P-Bray1, dan P-Mehlich 1. Model linear campuran terampat digunakan untuk menduga respons serapan P padi sawah terhadap pengaruh pemupukan P dan sifat-sifat tanah. Faktor pemupukan sebagai pengaruh tetap (fixed effect) dan sifat-sifat tanah sebagai pengaruh acak (random effect). Pada model pertama, hanya pengaruh acak sisaan digunakan (the residual only model) atau model tetap. Pada model kedua, pengaruh acak sifat-sifat tanah dimasukkan dalam model. Pada model ketiga dan seterusnya, komponen utama pengaruh acak dimasukkan secara sekuensial ke dalam model. Keterandalan model diuji dengan membandingkan pada model dengan hanya pengaruh acak sisaan menggunakan uji χ2 deviance. Kebaikan suai model juga dievaluasi menggunakan kriteria informasi Bayesian (BIC) dan Akaike (AIC dan AICC). Hasil penelitian menunjukkan bahwa data kasus serapan P terdiri dari dua kelompok sebaran (bimodus), yaitu kelompok serapan P rendah dan kelompok serapan P tinggi, masing-masing menyebar normal. Pada kelompok serapan P rendah dan kelompok serapan P tinggi, model campuran berdasarkan komponen utama lebih terandalkan daripada model campuran berdasarkan data asli dan model tetap. Kata kunci : model linear campuran terampat (generalized linear mixed model), pengaruh tetap (fixed effect), pengaruh acak (random effect), komponen utama (principal component

Bayesian estimation of linear and nonlinear mixed models of fertilizer dosing with independent normally distributed random components

Many linear and nonlinear mixed response models are proposed to predict the optimum dose of fertilizer. However, a major restriction of this class of models is the normality assumption of the random parameter component. The purpose of this paper is to analyze the performance of linear and nonlinear mixed models of fertilizer dosing with independent normally distributed random parameter components. We compare the Linear Plateau, Spillman-Mitscherlich, and Quadratic random parameter models with different random effects distribution assumption, i.e. the normal, Student-t, slash, and contaminated normal distributions and the random errors following their symmetric normal independent distributions. The method is applied to datasets of multi-location trials of potassium fertilization of soybeans. The results show that the Student-t Spillman-Mitscherlich Response Model is the best model for soybean yield prediction

Bayesian Estimation of Spatial Regression Models with Skew-normally Covariates Measured with Errors: Evidence from Monte Carlo Simulations

Spatial data are susceptible to covariates measured with errors. However, the errorprone covariates and the random errors are usually assumed to be symmetrically, normally distribution. The purpose of this paper is to analyze Bayesian inference of spatial regression models with a covariate measured with Skew-normal error by way of Monte Carlo simulation. We consider the spatial regression models with different degree of spatial correlation in the covariate of interest and measurement error variance. The simulation examines the performance of Bayesian estimators in the case of (i) Naive models without measurement error correction; (ii) Normal distribution for the error-prone covariate and random errors; (iii) Skew-normal distribution (SN) for the error-prone covariate and normal distribution for random errors. We use the relative bias (RelBias) and Root Mean Squared Error (RMSE) as valuation criteria. The main result is that the Skew-normal prior estimator outperform the normal, symmetrical prior distribution and the Naive models without measurement error correction.     Keywords: Spatial regression, measurement error, Bayesian analysis, Skew-normal distributio