Image retrieval is a challenging and important research applications like digital libraries and medical image databases. Content-based image retrieval is useful in retrieving images from database based on the feature vector generated with the help of the image features. In this study, we present image retrieval based on the genetic algorithm. The shape feature and morphological based texture features are extracted images in the database and query image. Then generating chromosome based on the distance value obtained by the difference feature vector of images in the data base and the query image. In the selected chromosome the genetic operators like cross over and mutation are applied. After that the best chromosome selected and displays the most similar images to the query image. The retrieval performance of the method shows better retrieval result.

This study aimed to analyse the effect of carbonisation temperature and type of activating agent which were best to be used in the chemical activation process in the manufacture of activated carbon from oil palm empty fruit bunches (EFB). The process of making activated carbon consisted of three stages, i.e. dehydration, carbonisation, and activation process. The experimental design was a randomised block design arranged as factorial with two factors, i.e. the first factor was variations of carbonisation temperature: 300°C, 400°C, and 500°C the second factor was the variations of activating agent type: ZnCl2, CaCl2, CH3COOH, and without activation. The results showed that carbonisation temperature and the type of activating agent had a significant effect on the characteristics of the activated carbon. The best results were achieved using CH3COOH as the activating agent at by the 500°C. The characteristics of the best-activated carbon consisted of 5.78% of ash content, 19.84% volatile matter content, 74.39% fixed carbon content, 1007.320 mg/g adsorption of iodine solution. The Brunauer – Emmett – Teller (BET) surface areas were up to 1110.87 m2/g and had a hollow surface structure, and open pores with a weight percentage atoms component of carbon reached 77.132%.

The process of sorting the chili pepper (Capsicum frutescens) is done physically by using the human eye, based on the visual color uniformity of its skin. This method is not effective and efficient. This study aims to identify the total content of carotene in chili pepper using the color and textural features approach. Color feature extraction used is the value of RGB, HSV, HSL, XYZ, CMY, CMYK, Lab, LUV, LCH, grey color, and 10 textural features from each color-space. The feature extraction results were used to identify the total carotene content by the image analysis method. The image of chili peppers used was 360, consisting of 300 training data and 60 test data. Classification test results with a level of 20% produce the best parameters as an indicator of total carotene chili pepper i.e. hue mean features with a range for quality A (55.04 > hue mean > 32.15), quality B (19.80 > hue mean > 12.21), quality C (3.55 > hue mean > 1.93), and 80% accuracy using the confusion matrix and mean square error (MSE).

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