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: Artificial Neural Network Modeling for Nutrient Solution Outflow Volume from Drip Irrigation Hydroponic System of Melon Plants in Vegetative Phase
Abstrak: Hubungan antara parameter lingkungan pertumbuhan tanaman melon pada sistem hidroponik irigasi tetes di dalam greenhouse dan kondisi tanaman melon terhadap outflow volume larutan nutrisi perlu diketahui sebagai dasar optimasi pemberian larutan nutrisi. Penelitian ini bertujuan untuk mengembangkan model Artificial Neural Network (ANN) parameter lingkungan pertumbuhan tanaman melon dan kondisi tanaman terhadap outflow volume larutan nutrisi menggunakan algoritma backpropagation. Model ANN yang dikembangkan menggunakan 596 data set yang dikumpulkan selama proses budidaya tanaman melon fase vegetatif pada sistem hidroponik irigasi tetes di dalam greenhouse. Pengembangan model ANN dilakukan dengan menentukan parameter model ANN terbaik yaitu meliputi learning rate initial, momentum, hiden layer size, dan maximum iteration. Parameter input meliputi intensitas radiometrik cahaya matahari, suhu udara, kelembapan relatif udara, umur tanaman, luas daun, tinggi tanaman, dan inflow volume larutan nutrisi, sedangkan parameter outputnya adalah outflow volume larutan nutrisi. Model ANN yang dikembangkan ternyata dapat memprediksi nilai outflow volume larutan nutrisi dengan kinerja yang sangat baik yaitu dengan nilai 0,96570 untuk R2, 45,28986 untuk RMSE, dan 6,94 % untuk MAPE., The relationship between environmental parameters of melon plant growth in a hydroponic drip irrigation system in a greenhouse and melon plant conditions on the outflow volume of nutrient solution needs to be known as the basis for optimization of nutrient solution supply. This study aims to develop an Artificial Neural Network (ANN) model for melon plant growth environmental parameters and plant conditions on the outflow of nutrient solution volume using the backpropagation algorithm. The ANN model was developed using 596 data sets collected during the vegetative phase of melon cultivation in a drip irrigation hydroponic system in a greenhouse. The development of the ANN model is done by determining the best ANN model parameters, which include initial learning rate, momentum, hidden layer size, and maximum iteration. The input parameters include the radiometric intensity of sunlight, air temperature, relative humidity, plant age, leaf area, plant height, and inflow volume of nutrient solution, while the output parameter is outflow volume of nutrient solution. The ANN model developed was able to predict the value of the nutrient solution volume outflow with very good performance, namely with a value of 0.96570 for R2, 45.28986 for RMSE, and 6.94 % for MAPE.
Keyword: artificial neural network, drip irrigation, hydroponic, melon plants, outflow volume of nutrient solution

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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