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: Analisis Pemalsuan Tepung Ikan dengan Dedak Padi Menggunakan Metode Image Analyis
Abstrak: Adulteration of feed ingredients is one of the main problems in ensuring quality. This study aims to identify the level of fish meal forgery using rice bran based on image analysis with a convolutional neural network algorithm. The number of datasets in this study was 3200 images which were divided into 2400 images for training data and 800 images for test data. The forgery treatment consists of P0=100% fish meal, P1= 90% fish meal+10% rice bran, P2=80% fish meal+20% rice bran, P3= 70% fish meal+30% rice bran and P4=100% rice bran. In the CNN algorithm, the image is processed through the input layer, feature extraction layer and fully connected. The results of this study obtained a training data accuracy value of 100% and a validation data accuracy value of 100%. In testing through the confusion matrix table, the results of 100% accuracy, 99% precision and 100% recal using 20 epochs were obtained. Based on this study, it was concluded that testing the counterfeiting of fish meal and rice bran using CNN can provide quite good and optimal results. Keyswords: adulteration analysis of feed ingredients, convolutional neural network, fish meal, image analysis, rice brain
Keyword: adulteration analysis of feed ingredients, convolutional neural network, fish meal, image analysis, rice brain

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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