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: Kajian Keberadaan Virus Avian Influenza pada Unggas di Pasar Burung Pramuka, Jakarta
Abstrak: Avian Influenza (AI) which is caused by influenzavirus A of the family Orthomyxoviridae cause an important impact, including in health, social and economic sectors. AI viruses (AIV) can infect various birds and mammals, therefore bird market as a meeting place for human and poultry can potentially transmit AI virus among birds or from birds to human. Indonesia is a habitat of various bird species; therefore research about AIV on bird, especially which are sold in the Pramuka Bird Market, as the biggest bird market in Jakarta, is urgently needed. In general, the aim of this study was to examine the presence of AIV in various bird species sold in Pramuka Bird Market, Jakarta. Specifically, the aims of this study were: (1) to assess the presence of H5 AI virus in oropharyngeal swab, cloacal swab, and fecal samples of various bird species using real time Reverse Transcriptase-Polymerase Chain Reaction (rRT-PCR); (2) to detect the presence of antibodies against AIV (H5) in various bird species using hemagglutination inhibition (HI) test, and (3) to determine the relationship among weather and geographic origin of birds to the prevalence of AIV. During April until September 2011, a number of 862 field samples from 28 families and 96 bird species were collected in Pramuka Bird Market, Jakarta. These field samples consisted of 649 fecal samples, 114 cloacal swab samples, and 99 oropharyngeal swab samples. As many as 7 of 649 faecal samples (1.08%) were positive AI. However, none cloacal or oropharyngel swab were positive AI. Positive fecal samples were detected in four bird species, which are three magpie-robins (Copsychus saularis, family Turdidae), two white-eyes (Zosterops palpebrosus, family Zostropidae), a yellow-vented bulbul (Pycnonotus goiavier, family Pycnotidae), and a robin (Leiothrix lutea, family Sylviidae). In addition to field samples, during April until September 2011, 178 birds from 21 families and 47 species were purchased from the same bird market. From 178 birds, two birds (1.12%), which were two white-eyes (Zosterops palpebrosus, family Zostropidae), were positive AI. The whole positive samples were from Passeriformis order. However, based on rRT-PCR result, there were no H5 positive samples. A number of 163 serum samples were collected from 178 purchased birds from Pramuka Bird Market. Based on HI test using antigen A/Legok/IPB-SGT/1/2004 (H5N1) and A/Ck/West Java/PWT-Wij/2006 (H5N1), seven of 163 birds (4.29%) had positive antibodies to AI. There were differences in HI titers between the two antigens. By using A/Legok/IPB-SGT/1/2004 (H5N1) antigen, two sample were strongly positive (titers ≥ 24), while the other five samples were weakly positive (titers <24). Strongly positive results were obtained from two scooty-headed bulbul (Pycnonotus aurigaster, family Pycnotidae). While the weakly positive results were obtained from a swan (Cygnus sp., family Anatidae), a duck (Anas sp., family Anatidae), a munia (Lonchura maja, family Ploceidae), a spotted-dove (Streptopelia chinensis, family Columbidae), and a black-crowned iv night heron (Nyctorax nyctorax, family Ardeidae). By using A/Ck/West Java/PWT-Wij/2006 (H5N1) antigen, the average HI titer obtained was higher. Five samples were strongly positive (Lonchura maja, Cygnus sp., Nyctorax nyctorax, and two Pycnonotus aurigaster), while samples from Anas sp. and Streptopelia chinensis were weakly positive.
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