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: Evaluasi Kinerja Metode Deteksi Besi dalam Air Menggunakan Membran Optode Berbasis Selulosa Triasetat
Abstrak: Besi merupakan logam berat yang mencemari lingkungan perairan dan dapat membahayakan kesehatan manusia. Oleh karena itu, diperlukan metode untuk mendeteksi kadar logam besi (Fe) di perairan dengan cepat dan mudah. Besi dideteksi menggunakan sensor optik (optode). Penelitian ini bertujuan memfabrikasi membran optode berbasis selulosa triasetat dengan menambahkan reagen selektif terhadap Fe(III). Optode dibuat dengan mencampurkan polimer selulosa triasetat (CTA) dengan tambahan pemlastis (asam oleat dan asetofenon), alikuot 336, dan ion tiosianat. Kinerja optode dievaluasi berdasarkan linearitas, batas deteksi dan kuantitasi, presisi, akurasi, dan selektivitas. Linearitas terbaik optode didapatkan koefisien determinasi (R2) 0,9972 pada konsentrasi 0,1–4,0 ppm. Batas deteksi dan kuantitasi diperoleh berturut-turut 9,88×10-7 M dan 0,1676 ppm, dengan presisi 3,01% dan akurasi 101,62±0,04%. Optode memiliki selektivitas yang baik terhadap ion penganggu Pb(II) dan Cr(VI). Aplikasi dalam sampel air menggunakan membran optode menunjukkan rerata konsentrasi 0,0578 ppm dan hasil pengujian tidak berbeda nyata dengan hasil deteksi menggunakan metode standar spektrofotometer UV-Vis, yaitu 0,0584 ppm., Iron is a heavy metal that pollutes the aquatic environment and threatens human health. Therefore, a quick and easy method is needed to detect iron (Fe) metal content in the aquatic environment. Iron detection can use an optical sensor (optode). This research aims to fabricate optode membranes based on cellulose triacetate with selective reagents for Fe(III). The optode was fabricated by mixing cellulose triacetate (CTA) polymer with plasticizer (oleic acid and acetophenone), aliquot 336, and thiocyanate ion. The performance was evaluated based on linearity, limit of detection and quantitation, precision, accuracy, and selectivity. The best optode linearity was obtained by a coefficient of determination (R2) of 0,9972 at a concentration of 0.1–4.0 ppm. The LOD and LOQ were 9,88×10-7 M and 0,1676 ppm, respectively, with a precision of 3.01% and an accuracy of 101.62±0,04%. The optode gave good selectivity for Pb(II) and Cr(VI) ions interfering. Application in water samples using the optode membrane showed an average concentration of 0.0578 ppm, and the results indicated no significant difference with the detection result using a standard UV- Vis spectrophotometer method, which was 0.0584 ppm.
Keyword: besi, evaluasi kinerja, optode, selulosa triasetat, tiosianat

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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