Judul: Pemodelan Tingkat Kemanisan Buah Melon Hasil Budidaya Tanaman dalam Sistem Hidroponik menggunakan Artificial Neural Network (ANN)
Abstrak: Buah melon merupakan salah satu buah yang diminati oleh masyarakat dikarenakan buah ini memiliki rasa yang manis dan segar. Agar buah dari tanaman ini dapat mencapai hasil yang diinginkan oleh konsumen, dibutuhkan beberapa parameter khusus yang dapat mempengaruhi tingkat kemanisan buah melon. Pada penelitian ini, digunakan varietas golden melon dan budidayanya dilakukan di dalam smart greenhouse dengan sistem irigasi drip irrigation. Pemodelan tingkat kemanisan buah melon dilakukan menggunakan Artificial Neural Network (ANN) dengan parameter input yang terdiri dari intensitas cahaya, kadar air media tanam, total luas daun tanaman, Umur Buah, dan letak buah. Pemodelan tersebut dilakukan menggunakan parameter pelatihan yang terdiri dari nilai learning rate sebesar 0.3, nilai momentum sebesar 0.6, jumlah iterasi sebanyak 8000 iterasi, dan jumlah hidden layer sebanyak 1 buah dengan jumlah node sebanyak 4 node. Plot tingkat kemanisan buah melon yang diprediksi menggunakan model ANN dan hasil pengukuran menghasilkan persamaan y = 1.1152x-1.3081 dengan R2 sebesar 0.9054 dan RMSE sebesar 0.06277525.
Keyword: ANN, golden melon, greenhouse, parameter lingkungan, pemodelan

Judul: Net Type Melon Fruit Grade Evaluation System with Artificial Neural Network and Fuzzy Logic Preprocess
Abstrak: This research utilize Artificial Neural Network (ANN), image processing and fuzzy logic as preprocess in evaluating melon fruit grade system. There are 66 samples out of 80 samples for ANN input with fuzzy logic in taste data normalization phase that suitable for organoleptic test. 49 unit image for image processing based on melon fruit characteristic were use for ANN input. Mutilayer neural network with 1 hidden layer and back propagation algorithm were use in ANN architecture for training, validation and prediction phases. The optimal ANN architecture were use 30 hidden layer node with 0.8 learning rate. The result are 90% total accuratecy in validation process consist of 100% prediction accuratecy of sweetness, 75% medium taste and 100% sour taste.
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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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