Judul: Desain Komputasi Material Katoda Baterai Berbasis Na-Mn-O-(Ni) Na2Mn3-zNizO7
Abstrak: Perkembangan teknologi dibidang komputasi yang begitu pesat membuat eksperimen yang bersifat real dapat dimodelkan atau diprediksi secara komputasi yang tentunya akan dapat membantu dalam penekanan biaya dan penghematan akan penggunaan sumber daya alam. Salah satu aplikasi pemodelan komputasi yang cukup luas digunakan adalah penentuan rapatan energi dan sifat elektronik dari suatu material. Salah satu material yang masih dalam proses pengembangan dan berpotensi diaplikasian secara luas adalah material berbasis sodium pada baterai. Penulisan tesis ini berfokus pada proses penentuan rapatan energi dan sifat elektronik pada model baterai sodium (SIB) dengan doping nikel dan kombinasi mangan-oksigen (Na2Mn3-zNizO7) menggunakan perhitungan First principle pada software Quantum Espresso. Selain faktor kelimpahan natrium dan nikel di alam sebagai alternatif untuk mengatasi keterbatasan material litium, material Na2Mn3-zNizO7 memiliki kemiripan struktur dengan baterai berbasis litium yang sudah di gunakan secara luas. Proses perpindahan ion Na+ dan energi aktivasi telah disimulasikan, begitu juga tegangan dan sifat elektronik dari material telah dihitung dan menunjukkan adanya potensial untuk dijadikan sebagai katoda. Diharapkan akan adanya penelitian yang lebih lanjut terkait dengan karakterisitik lain yang belum dihitung seperti kestabilan terhadap suhu dan daya tahan. Katoda yang telah dirancang dapat di kombinasikan dengan elektrolit berbasis pelarut organik, sehingga tujuan pendayagunaan potensi alam domestik yang ramah lingkungan dapat tercapai.
Keyword: DFT, Molecular Dynamics, Quantum Espresso, Renewable Energy, SIB

Judul: Application of Machine Learning in Predicting the Properties of Sodium-ion Batteries
Abstrak: Penelitian ini berfokus pada aplikasi machine learning dalam meramalkan properti baterai berbasis ion natrium. Menggunakan data dari situs Material Project sebanyak 309 data. Penelitian ini mengkaji prediksi terhadap properti average voltage, gravimetric capacity, volumetric capacity, gravimetric energy, dan volumetric energy. Fitur-fitur yang digunakan meliputi formula, spacegroup, Nsites, E above hull, formation energy, volume, density, dan bandgap. Untuk mendapatkan fitur struktur, unsur-unsur dalam fitur formula diurai menggunakan library Chemparse. Tiga kombinasi model Artificial Neural Network diterapkan: kombinasi pertama menggunakan fitur propert material, kombinasi kedua menggunakan fitur seluruh fitur properti dan unsur yang diurai, sementara kombinasi ketiga hanya menggunakan fitur unsur yang diurai saja. Evaluasi performa model dilakukan menggunakan metrik R2 score, Mean Squared Error, dan Root Mean Squared Error. Hasil penelitian menunjukkan bahwa kombinasi kedua yang menggunakan seluruh fitur mendapatkan performal yang paling baik. Selain itu, didapatkan temuan bahwa struktur material berupa unsur yang diurai mampu menguatkan performa model., This research focuses on the application of machine learning to predict the properties of sodium-ion-based batteries. Using data from the Material Project, comprising 309 entries. This study examines the prediction of properties such as average voltage, gravimetric capacity, volumetric capacity, gravimetric energy, and volumetric energy. The features used include formula, spacegroup, Nsites, E above hull, formation energy, volume, density, and bandgap. To obtain structural features, the elements in the formula feature were parsed using the Chemparse library. Three combinations of Artificial Neural Network models were applied: the first combination used material property features, the second combination used all property features and parsed elements, while the third combination used only the parsed elements. The performance of the models was evaluated using R2 score, Mean Squared Error, and Root Mean Squared Error metrics. The results indicate that the second combination, which utilized all features, achieved the best performance. Additionally, it was found that the structural features in the form of parsed elements were able to enhance the model's performance.
Keyword: Artificial Neural Network, Machine learning, Baterai Ion Natrium

Judul: Ragam jenis ektoparasit burung tekukur (Streptopelia chinensis) dan burung puter (Streptopelia bitorquata) di penangkaran
Abstrak: Spotted dove and Javan turtle dove are the bird species that are easily raised in captivity. Nevertheless, good management is required to successfully breed and raise the birds in captivity; otherwise, the condition may endanger the sustainability of the two bird species. Information on ectoparasites on the bodies of this bird as well as their captivity breeding has not been available, although admittedly taking ectoparasites from these bird families has much been done so that a research is obviously needed. This study aimed to find out various kinds of Spotted dove and Javan turtle dove ectoparasites and to learn various kinds of parasitic insects around the Spotted dove and Javan turtle dove cages. The study was conducted from April to June 2010. The methods of data collection included the general condition of the location, collection of ectoparasites specimens, processing and specimens identification. The data analysis was done descriptively and tabulated with the degree of infestation. The kinds of ectoparasites from the class of insects found in the body of Spotted dove were Goniocotes sp. and Columbicola columbae of phthiraptera order and Pseudolynchia canariensis of diptera order, and arachnida class, that is, tungau from the pterolichidae family. In terms of ectoparasites, Spotted dove and Javan turtle dove were similar, except that Javan turtle dove had no Pseudolynchia canariensis. Goniocotes sp. and Columbicola columbae were mallopaga order (biting lice) and were obligate. The kinds of parasitic insects in the vicinity of Spotted dove and Javan turtle dove cages were from the Ordo Diptera. The diptera classified as parasites included Anopheles, Culex, Psychodidae, Culicoides and Simulium, most of them vectors of disease. The presence of ectoparasites was closely related to how a breeding place is managed.
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