scholarly journals Pengaruh Penambahan Ekstrak Bahan Alami Terhadap Laju Oksidasi Minyak Kelapa

REAKTOR ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. 157
Author(s):  
Danu Ariono ◽  
Maxs Christian ◽  
Philip Irfan ◽  
Sri Mulyani Suharno ◽  
Aisya Tamara

Abstract THE INFLUENCE OF NATURAL EXTRACT TO THE COCONUT OIL OXIDATION RATE. Indonesia's coconut plantation is the largest in the world, with a share of 31.2% of the total coconut plantation area in the world. One of the products from coconut is coconut oil. However, coconut oil has a short storage time. Therefore, this experiment aims to estimate and optimize the storage time of coconut oil. The coconut oil used as the experimental sample was an oil made by traditional method. The coconut oils tested in this experiment included coconut oil, plus carrots, pineapple extracts of 10 and 30% -v/v, 10 and 30% -v/v young papaya, and 10 and 30% -v/v tomatoes. The mixture of coconut oil and carrot pieces was stored in light and dark glass bottles, while the mixture of coconut oil and liquid extract was stored only in dark glass bottles. The estimation method of storage time was based on literature values of acid number and peroxide number approximated by equation regression method and Artificial Neural Network (ANN). The experimental results showed that the storage time of coconut oil was 37-41 days for blank oil (in light and dark glass bottles), 50-51 days for the addition of carrot cuts in bright glass bottles, 56-57 days for the addition of carrot carrot cuts in glass bottles dark, 41-45 days for addition of pineapple liquid extract 10% -v/v, 30-32 days for 30% -v/v, 63-64 days for the addition of young papaya 10% -v/v, 55-62 days for 30% -v/v, 24-27 days for tomato liquid extract 10% -v/v, and 17-21 days for 30% -v/v. Keywords: natural antioxidant; artificial neural network; acid number; peroxide number; coconut oil; storage time   Abstrak Perkebunan kelapa Indonesia merupakan terbesar di dunia, dengan pangsa 31,2% dari total areal perkebunan kelapa di dunia. Salah satu produk dari kelapa adalah minyak kelapa. Minyak kelapa memiliki umur simpan yang singkat. Penelitian ini bertujuan untuk memperkirakan dan mengoptimasi umur simpan minyak kelapa. Minyak kelapa yang dijadikan sampel eksperimen merupakan minyak yang dibuat berdasarkan metode tradisional. Minyak kelapa yang diuji pada eksperimen ini meliputi minyak kelapa blanko, ditambah potongan wortel, ekstrak cair nanas 10 dan 30 %-v/v, pepaya muda 10 dan 30%-v/v, serta tomat 10 dan 30 %-v/v. Minyak kelapa blanko dan yang ditambah potongan wortel disimpan dalam botol kaca terang dan gelap, sedangkan minyak kelapa yang ditambah ekstrak cair hanya disimpan dalam botol kaca gelap. Metoda perkiraan umur simpan dilakukan berdasarkan nilai-nilai pustaka (literature value) bilangan asam dan bilangan peroksida yang didekati dengan metoda regresi persamaan dan Artificial Neural Network (ANN). Hasil eksperimen menunujukkan bahwa umur simpan minyak kelapa adalah 37-41 hari untuk minyak blanko (dalam botol kaca terang dan gelap), 50-51 hari untuk penambahan potongan wortel dalam botol kaca terang, 56-57 hari untuk penambahan potongan wortel wortel dalam botol kaca gelap, 41-45 hari untuk penambahan ekstrak cair nanas 10%-v/v, 30-32 hari untuk 30%-v/v, 63-64 hari untuk penambahan pepaya muda 10%-v/v, 55-62 hari untuk 30%-v/v, 24-27 hari untuk ekstrak  cair tomat 10%-v/v, serta 17-21 hari untuk 30%-v/v. Kata kunci:   antioksidan alami; artificial neural network; bilangan asam; bilangan peroksida; minyak kelapa; umur simpan

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.


2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.


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