Judul: Testing of Counterfeiting Rice Bran With Rice Husk Using Image Analysis Method
Abstrak: Dedak padi sering dipalsukan dengan sekam padi giling yang dapat mengakibatkan kerugian dan penurunan produksi dari ternak. Pengujian pemalsuan dedak padi masih dijalankan secara manual yang bersifat subjektif dan kurang praktis sehingga diperlukan pengujian dengan metode image analysis berbasis Convolutional Neural Network (CNN) melalui proses analisis citra secara visual. Tujuan dari penelitian ini adalah mengukur akurasi data training, validasi, dan uji pada pemalsuan dedak padi dengan sekam menggunakan Image Analysis berbasis CNN. Penelitian ini dibagi menjadi dua perlakuan yaitu perlakuan pewarnaan menggunakan phloroglucinol dan tanpa pewarnaan. Tahapan penelitian ini terdiri atas proses pencampuran pemalsuan, pemalsuan perlakuan, pembagian data, dan pembangunan model CNN. Penelitian ini mendapatkan hasil bahwa hasil akurasi data training, data validasi, dan data uji pada perlakuan pewarnaan phloroglucinol lebih tinggi dibandingkan tanpa perlakuan pewarnaan. Namun hasil akurasi pada perlakuan pewarnaan tersebut masih jauh dari akurasi 100%, sehingga pengujian dengan metode image analisis berbasis CNN ini belum dapat digunakan, Rice bran is often adulterated with milled rice husks which can lead to losses and decreased production of livestock. Testing of rice bran forgery is still carried out manually which is subjective and less practical, so testing with image analysis methods based on Convolutional neural network (CNN) through a visual image analysis process is needed. The purpose of this study was to measure the accuracy of training, validation, and test data on rice bran fabrication with husks using Image Analysis based on CNN. This study was divided into two treatments, namely staining treatment using phloroglucinol and without staining. The stages of this research consist of the mixing process of falsification, falsification, treatment of data sharing, and CNN development model. This study found that the results of training accuracy, data validation, and data testing on phloroglucinol treatment were higher than those without treatment. However, the results of the accuracy of the treatment are still far from 100% accuracy, so testing with the CNN-based image analysis method cannot be used.
Keyword: accuracy, Convolutional Neural Network, rice bran, rice husk

Judul: Estimation of Lignin Content in Rice Bran Mixed with Husks Using PNN Based on Color Imagery
Abstrak: Dedak padi yang mengalami penurunan kualitas dipengaruhi oleh pemalsuan dedak padi dengan mencampurkan bahan lain yang kualitasnya lebih rendah, salah satu bahan yang dicampukan kedalam dedak padi adalah sekam. Dedak padi yang bercampur sekam memiliki kandungan serat kasar dan lignin yang bersifat anti nutrisi. Kandungan lignin dapat diketahui dengan pengujian reaksi pewarnaan menggunakan larutan phloroglucinol. Maka dari hal tersebut penelitian ini dilakukan untuk mengestimasi kandungan lignin pada citra dedak padi bercampur sekam dari hasil uji reaksi pewarnaan dengan memanfaatkan fitur warna RGB dan HSV menggunakan klasifikasi Probabilistic Neural Network (PNN). Data yang digunakan dalam penelitian ini sebanyak 132 dataset yang dibagi menjadi 88 data latih dan 44 data uji. Dari penelitian ini didapat hasil akurasi tertinggi sebesar 79.54%., Rice bran that has decreased in quality is influenced by the adulteration of rice bran by mixing other ingredients of lower quality, one of the ingredients that is thrown into rice bran is husk. Rice bran mixed with husks contains crude fiber and lignin which are anti-nutrients. The lignin content can be determined by testing the staining reaction using phloroglucinol solution. Therefore, this study was conducted to estimate the lignin content in the image of rice bran mixed with husks from the results of the coloring reaction test by utilizing RGB and HSV color features using the Probabilistic Neural Network (PNN) classification. The data used in this study were 132 datasets divided into 88 training data and 44 test data. From this study, the highest accuracy result of 79.54%.
Keyword: HSV, klasifikasi, lignin, probabilistic neural network, RGB

Judul: Distribution and Diversity of Ectoparasite Mites on Geckos in Indonesia
Abstrak: Data on the diversity and dispersal of parasitic mites on house geckos in Indonesia are very scarce. In this work, the distribution and diversity of mites living on three species of house geckos, namely Cosymbotus platyurus, Hemidactylus frenatus, and H. garnotii collected throughout Indonesia, has been elaborated. Geckos and mites were captured and immediately preserved in 70% ethanol. Whole mount of the mites was prepared by clearing in lactophenol followed by mounting on polyvinyl lactophenol solutions. The SEM preparation was conducted to examine the detail morphological characters of the mites. The results showed that among 448 individuals of geckos, 221 geckos were infected by Geckobia mites. Prevalences of C. platyurus, H. frenatus, and H. garnotii infested by mites were 14.8%, 50.69%, and 79.6%, respectively. Three different spesies of Geckobia (G1, G2, and G3) could be differentiated; and based on similarities of their morphological characters to ones described in published literatures, Geckobia G2 is Geckobia glebosum and Geckobia G3 is Geckobia bataviensis while G1 could not be identified to the species level. The highest mean intensity of Geckobia G1 infestation was found on H. garnotii (I=7.0), G. glebosum infestation on H. frenatus (I=3.5), and G. bataviensis infestation on H. garnotii (I=11.8). In general, C. platyurus was infested by the least number of mites. Geckobia G1 were found living on the skin folds on the claws, G. glebosum were found mainly on the body and thigh, and G. bataviensis were found on almost all parts of the host’s body. Geckobia mites are distributed randomly throughout Indonesian Archipelago, following the pattern of distribution of their hosts. So it is concluded that Geckobia G1, G. glebosum, and G. bataviensis are sympatric.
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