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Yunita, Citra Nurma
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Agriculture, Biological Sciences & Forestry

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Pengaruh Marinasi Larutan Jeruk Lemon (Citrus limon L) Terhadap Karakteristik Organoleptik Sate Ayam Yunita, Citra Nurma; Devi Kusuma Pradana; Reza Fahlevi; Sari Dewi; Reo Radius Falah
AgriMalS Vol 5 No 1 (2025): Volume 5 Nomor 1 Tahun 2025
Publisher : Universitas Muhammadiyah Kotabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47637/agrimals.v5i1.1744

Abstract

Lemon is one of the natural ingredients that has the potential to be applied as a marinade in processed meat products such as satay. This study aims to determine the optimal percentage of lemon solution producing the best sensory quality in chicken satay products. The materials used in the study were chicken meat and local lemons. The study was conducted experimentally with different percentage treatments of lime solution, then organoleptic tests were carried out by trained panelists. The treatments consisted of T0: without lemon solution marinade, T1: 6% lemon solution marinade, T2: 9% lemon solution marinade, and T3: 12% lemon solution marinade. The data from the Completely Randomized Design (CRD) were then analyzed using ANOVA and continued with the Duncan test if there was a difference. The results showed that lemon solution marinade had a significant effect (P <0.05) on the organoleptic color, aroma, taste, and texture. The average color value is 3.72-4.80, aroma 3.32-4.72, taste 3.60-4.84, and texture 3.32-4.80. This study can be concluded that the T3 treatment (12% lemon marinade) is the most optimal, with an average color score of 4.40, aroma 4.72, taste 4.84, and texture 4.80, because it can produce the best sensory quality by improving the taste, aroma, and texture of chicken satay. The results of the study are expected to be a reference for the culinary industry in improving the quality and competitiveness of processed meat products made from chicken
Pengaruh Penambahan L-Arginin terhadap Total Spermatozoa Motil (TSM) pada Semen Beku Kambing Boer Fahlevi, Reza; Yunita, Citra Nurma; Pradana, Devi Kusuma; Dewi, Sari; Falah, Reo Radius
AgriMalS Vol 5 No 2 (2025): Volume 5 Nomor 2 Tahun 2025
Publisher : Universitas Muhammadiyah Kotabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47637/agrimals.v5i2.1954

Abstract

One important factor determining this quality is the dilution process, which aims to maintain sperm motility and viability during the freezing process. Low storage temperatures can causecold shock, namely damage to the plasma membrane of cells which can cause the death of spermatozoa.This study aims to evaluateThe effect of adding L-Arginine on total motile spermatozoa (TSM)in frozen Boer goat semen. The fresh semen used had a minimum individual motility of 80% and a mass motility of ++. The diluent used was Tris-aminomethane egg yolk, with varying concentrations of L-Arginine.Fresh semen is evaluated macroscopically based oncolor, odor, volume, consistency, and pH, then continued with observation of total motile spermatozoa after the freezing process to assess the effect of adding L-Arginine.This research usesCompletely Randomized Design (RAL)with 4 treatments and 10 replications, namely:P0: 0 mM L-Arginine (control), P1: 5 mM L-Arginine, P2: 6 mM L-Arginine P3: 7 mM L-Arginine. The results of this study: The average percentage of Total Motile Spermatozoa (TSM) after freezing is:P2 (6 mM): 28,25 ± 2,38%, P0 (0mM): 25,00 ± 2,38%, P3 (7 mM): 24,15 ± 2,38%, P1 (5 mM): 23,20 ± 2,62%. Addition L-Arginine 6 mM (P2) provided the best results in increasing the total percentage of motile spermatozoa after freezing. Thus, a concentration of 6 mM L-Arginine can be recommended as the optimal.
Pendugaan Kandungan Nutrisi Dedak Padi Menggunakan Jaringan Syaraf Tiruan (JST) Berdasarkan Data Absorban NIRS (Near Infrared Reflectance Spectroscopy) Dewi, Sari; Yunita, Citra Nurma; Falah, Reo Radius; Fahlevi, Reza
AgriMalS Vol 5 No 2 (2025): Volume 5 Nomor 2 Tahun 2025
Publisher : Universitas Muhammadiyah Kotabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47637/agrimals.v5i2.1958

Abstract

ABSTRACT: The nutritional content of feed ingredients is one of the factors considered in formulating livestock rations. Generally, the determination of nutritional content uses conventional methods, but these methods are destructive, expensive, and time-consuming, making them unsuitable for measuring nutritional content during ration formulation. This study aims to determine the accuracy of crude protein and crude fat content in rice bran using Artificial Neural Networks (ANN) based on NIRS absorbance data. This study used 60 rice bran samples from various regions representing West Sumatra. NIR spectral data were obtained using a Portable Fourier Transform Near Infrared (FT-NIR) device with a wavelength of 1000 nm-2500 nm. The results of the estimated nutritional content of rice bran were analyzed using an Artificial Neural Network (ANN) with 3, 5, 7, and 9 hidden nodes and 25,000, 30,000, 35,000, 40,000, and 50,000 iterations. The NIR absorbance data was pretreated by normalizing it using Unscrambler software and treating it using the PCA (Principal Component Analysis) method in IBM SPSS Statistics 21. The best estimation results can be seen in the lowest Standard Error of Prediction (SEP) and Coefficient of Variation (CV) values. The results showed that the use of JST with the developed model could estimate the crude protein and crude fat content of rice bran well and closely approximated the actual values. The crude protein estimation results have low SEP and CV values, namely SEP 1.26% and CV 14.91%, while the crude fat estimation results have SEP 1.21% and CV 15.12%.
Co-Authors Devi Kusuma Pradana Falah, Reo Radius Pradana, Devi Kusuma Reo Radius Falah Reza Fahlevi REZA FAHLEVI Sari Dewi