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All Journal Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam (JURRIMIPA) Jurnal Pendidikan Kimia, Fisika dan Biologi
Rohimatul Anwar
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Education Mathematics

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Sample Preparation Technique for Scanning Electron Microscopy in Analyzing Shrimp Shell Biodegradation by Actinomycetes through Solid-State Fermentation Widyastuti Widyastuti; Annisa Ananda; Rohimatul Anwar; Kurniawan Shidiq
JURNAL RISET RUMPUN MATEMATIKA DAN ILMU PENGETAHUAN ALAM Vol. 4 No. 2 (2025): Agustus: Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrimipa.v4i2.6342

Abstract

Shrimp shell waste is a chitin-rich biopolymer with high potential for microbial bioconversion into value-added products. This study aimed to analyze the microstructural degradation of shrimp shells by the actinomycete isolate 18D38A1 under solid-state fermentation, using Scanning Electron Microscopy (SEM). A fresh sample preparation method was applied, involving washing and immersion in 70% ethanol, followed by vacuum drying and gold sputter-coating. Fermentation was conducted over a period of 1 to 4 days. SEM analysis revealed progressive structural deterioration of the shrimp shell matrix, including increased surface erosion, pore formation, and breakdown of chitin fibers, which became more pronounced each day. These observations indicate active and time-dependent biodegradation by isolate 18D38A1. The sample preparation protocol proved effective in preserving morphological features and enhancing SEM image clarity, enabling precise visualization of degradation stages. The combination of solid-state fermentation and optimized SEM preparation provides a reliable approach to evaluate the biodegradation process of chitinous waste by actinomycetes over time. This study demonstrates a practical SEM sample preparation method for visualizing the progressive biodegradation of shrimp shell by actinomycete isolate 18D38A1 from day 1 to day 4.
Analisis Regresi Kernel Gaussian untuk Memprediksi Indeks Pembangunan Manusia (IPM) Berdasarkan Faktor Sosial-Ekonomi Provinsi di Indonesia Rohimatul Anwar; Linda Rassiyanti; Rizka Pitri
JURNAL RISET RUMPUN MATEMATIKA DAN ILMU PENGETAHUAN ALAM Vol. 4 No. 3 (2025): Desember : JURRIMIPA: Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrimipa.v4i3.7017

Abstract

The Human Development Index (HDI) functions as a key indicator for assessing the level of welfare and overall quality of life of the population within a specific region. This study aims to examine the socio-economic factors influencing HDI at the provincial level in Indonesia using a Gaussian kernel regression approach. A nonparametric method is employed due to its flexibility in capturing nonlinear relationships between the response and predictor variables without the need to assume a specific functional form. The analysis utilizes secondary data, including education, poverty, per capita expenditure, expected years of schooling, open unemployment rate, and gross regional domestic product for each Indonesian province. The findings from this study indicate that educational factors, particularly mean years of schooling and expected years of schooling, exert the most significant impact on HDI improvement. The estimated Gaussian kernel regression model demonstrates a coefficient of determination of 0.9954 and a residual standard error of 0.3468, reflecting a very high predictive accuracy and relatively low error. These results suggest that Gaussian kernel regression is an effective nonparametric approach for analyzing human development in Indonesia.
Analisis Morfologi Rambut Sebelum dan Sesudah Rebonding Menggunakan Scanning Electron Microscopy (SEM) Annisa Ananda; Widyastuti Widyastuti; Rohimatul Anwar
Jurnal Pendidikan Kimia, Fisika dan Biologi Vol. 2 No. 3 (2026): Mei : Jurnal Pendidikan Kimia, Fisika dan Biologi
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupenkifb.v2i3.1117

Abstract

Rebonding is a chemical hair straightening procedure widely popular in Indonesia, involving two-stage redox reactions on keratin disulfide bonds using ammonium thioglycolate and hydrogen peroxide. Although it provides effective hair-straightening results, rebonding potentially causes significant damage to the hair cuticle structure. This study aimed to analyze the morphological changes of hair surface before and after rebonding treatment through a descriptive comparative case study approach using Scanning Electron Microscopy (SEM). The samples consisted of one strand of healthy, chemically untreated hair (control) and one strand of post-rebonding hair that both obtained from a female. Samples were prepared using gold-sputter coating prior to SEM observation. Representative SEM images from each sample were then qualitatively compared based on cuticle morphology parameters. The results showed clear morphological differences between the two samples: the control hair displayed tightly arranged and orderly cuticle scales with a smooth surface, while the post-rebonding hair showed surface erosion and the presence of cracks in multiple areas of the cuticle. These findings confirm that rebonding procedures cause visible and characterizable morphological cuticle damage as identified through SEM, providing a scientific basis for developing more protective post-rebonding hair care products.
Pengaruh Parameter Regularisasi (λ) terhadap Stabilitas Estimasi pada Regresi Ridge Linda Rassiyanti; Rohimatul Anwar
JURNAL RISET RUMPUN MATEMATIKA DAN ILMU PENGETAHUAN ALAM Vol. 5 No. 1 (2026): April : JURRIMIPA: Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrimipa.v5i1.8726

Abstract

Multicollinearity is one of the common issues in multiple linear regression that can lead to instability in the estimation of regression coefficients. This study aims to examine the impact of multicollinearity on regression models and to evaluate the use of Ridge Regression as an alternative estimation method. The study employs simulated data consisting of 1,000 observations, including one dependent variable and four independent variables designed to exhibit high correlation. The analysis begins with model estimation using the Ordinary Least Squares (OLS) method, followed by multicollinearity testing using the Variance Inflation Factor (VIF). The OLS results indicate that most independent variables significantly influence the dependent variable, with a coefficient of determination (R²) of 0.9863. However, the high VIF values reveal the presence of strong multicollinearity in the model. To address this issue, Ridge Regression is applied, with the optimal penalty parameter determined through cross-validation, yielding a lambda value of 4.201589. The results show that the regression coefficients in the Ridge model undergo shrinkage, resulting in greater stability compared to the OLS estimates. Model evaluation indicates that the Mean Squared Error (MSE) for the OLS model is 24.77, whereas the Ridge model produces an MSE of 29.72. Although the Ridge model exhibits a slightly higher MSE, it effectively mitigates the impact of multicollinearity and provides more stable parameter estimates.
Co-Authors Annisa Ananda Kurniawan Shidiq Linda Rassiyanti Rizka Pitri Widyastuti Widyastuti