Judul: Identification of Coagulant and Flocculant Usage in Gold Mining Wastewater Treatment Plant Based on Artificial Neural Network
Abstrak: Salah satu resiko kerusakan lingkungan karena kegiatan pertambangan emas, yaitu air limbah. Instalasi pengolahan air limbah (IPAL) perusahaan tambang emas belum memiliki teknologi maju untuk menangani air limbah. Jaringan saraf tiruan (JST) sebagai hasil perkembangan teknologi 4.0 digunakan sebagai solusi permasalahan IPAL tersebut. Penelitian ini bertujuan untuk mengidentifikasi penggunaan koagulan dan flokulan pada IPAL tambang emas menggunakan basis jaringan saraf tiruan. Algoritma yang digunakan JST merupakan model feedforward neural network untuk menganalisis data sianida (CN), derajat keasaman (pH), dan total suspended solid (TSS), serta reagen koagulan flokulan IPAL perusahaan tambang. Model JST menggunakan 6 parameter input, 2 parameter output, dan 6 hidden layer sebanyak 512, 256, 128,64, 32, 2 serta menghasilkan nilai evaluasi MSE 0,0076. Evaluasi hasil prediksi dibandingkan data aktualnya menunjukan hasil yang baik ditinjau dari persamaan regresi dan koefisien determinasi. Namun masih ada titik poin yang berada jauh dari garis trendline, sehingga menunjukan hasil belum bagus. Hasil Program JST tidak dapat direkomendasikan untuk digunakan secara langsung pada operasional IPAL tambang emas., One of the risks of environmental damage due to gold mining activities is wastewater. Gold mining companies' wastewater treatment plants (IPAL) do not yet have advanced technology to handle waste water. Artificial neural networks (ANN) as a result of the development of technology 4.0 are used as a solution to the IPAL problem. This research aims to identify the use of coagulants and flocculants in gold mining WWTPs using an artificial neural network basis. The algorithm used by JST is a feedforward neural network model to analyze data on cyanide (CN), degree of acidity (pH), and total suspended solids (TSS), as well as flocculant coagulant reagents for mining companies' WWTPs. The ANN model uses 6 input parameters, 2 output parameters, and 6 hidden layers totaling 512, 256, 128.64, 32, 2 and produces an MSE evaluation value of 0.0076. Evaluation of the prediction results compared to the actual data shows good results in terms of the regression equation and coefficient of determination. However, there are still points that are far from the trendline, so the results are not good. The results of the JST Program cannot be recommended for direct use in gold mining IPAL operations.
Keyword: air limbah, feedforward, jaringan saraf tiruan, prediksi

Judul: Artificial Neural Network to Detect Iron Metal Concentrations in Water
Abstrak: Logam besi (Fe) merupakan salah satu kontaminan anorganik yang sering dijumpai di dalam air dan dapat menyebabkan kualitas air menjadi memburuk. Salah satu metode untuk mendeteksi konsentrasi besi dalam air adalah dengan Jaringan Saraf Tiruan (JST). Penelitian ini bertujuan mengembangkan model JST untuk memprediksi konsentrasi logam besi di dalam air menggunakan parameter-parameter fisik dan kimia kualitas air, yaitu pH, suhu, Total Dissolved Solid (TDS), dan turbiditas. Jaringan Saraf Tiruan yang dibuat dengan model Multilayer Feedforward Neural Network memiliki tiga lapisan utama, yaitu satu lapisan input dengan 4 node, satu lapisan tersembunyi (hidden) dengan 10 node, dan satu lapisan output dengan 1 node. Model ini menggunakan fungsi aktivasi sigmoid, regularisasi L2, algoritma optimasi Adam, dan jumlah iterasi sebanyak 500 kali. Model ini menghasilkan nilai loss (error) sebesar 0,06 dan R2 sebesar 0,95 pada proses pelatihan, serta nilai error sebesar 0,06 dan R2 sebesar 0,918 pada proses testing. Berdasarkan hasil tersebut, model memiliki kinerja yang sangat baik dalam memprediksi konsentrasi besi menggunakan parameter pH, suhu, TDS, dan turbiditas.
Keyword: Logam besi, dataset, Jaringan Saraf Tiruan

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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